2025
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Nature Communications, in press (2025)Close-proximity interactions are considered a key risk factor for respiratory virus transmission, but their importance relative to shared space and air quality remains unclear. We conducted a six-week longitudinal study in a Swiss secondary school (67 students, aged 14–15). We detected 87 infections in saliva samples and recorded absences to identify plausible transmissions, excluding implausible ones through genomic analysis. Time in close proximity (within 1.5 metres) was measured using wearable sensors and air quality via CO2 monitors. Students spent 21.2 minutes per day in close proximity (interquartile range 7.8–44.2) and 5.3 hours in shared classrooms (IQR 3.8–6.2), during which air quality was suboptimal for 1.9 hours (IQR 1.2–3.0). Using pairwise survival models, we found that transmission was more likely within than between classes. Close proximity was modestly associated with higher transmission risk overall (rate ratio 1.16 per doubling daily time, 95%-CI 1.01–1.33), while time in shared classrooms (RR 3.17, 95%-CI 1.96–5.17) and suboptimal air quality (RR 1.90 95%-CI 1.23–2.94) also predicted within-class risk. Prolonged exposure in shared, poorly ventilated spaces, which potentially includes several infectious sources, drives respiratory virus transmission more than close contact.
@article{Banholzer2025NatComm, author = {Banholzer, Nicolas and Munday, James Daniel and Jent, Philipp and Bittel, Pascal and Dall'Amico, Lorenzo and Furrer, Lavinia and Bürki, Charlyne and Stadler, Tanja and Egger, Matthias and Hascher, Tina and Cattuto, Ciro and Fenner, Lukas}, title = {The relative contribution of close-proximity contacts, shared classroom exposure and indoor air quality to respiratory virus transmission in schools}, journal = {Nature Communications}, year = {2025}, doi = {10.1038/s41467-025-66719-3}, url = {https://doi.org/10.1038/s41467-025-66719-3}, note = {Article in Press, received 16 July 2025, accepted 12 November 2025}, keywords = {SocioPatterns} } -
Physical Review E 112(5), 054305 (2025)Time-varying group interactions constitute the building blocks of many complex systems. The framework of temporal hypergraphs makes it possible to represent them by taking into account the higher-order and temporal nature of the interactions. However, the corresponding datasets are often incomplete and/or limited in size and duration, and surrogate time-varying hypergraphs able to reproduce their statistical features constitute interesting substitutions, especially to understand how dynamical processes unfold on group interactions. Here we introduce a temporal hypergraph model, the Emerging Activity Temporal Hypergraph (EATH), which can be fed by parameters measured in a dataset and create synthetic datasets with similar properties. In the model each node has an independent underlying activity dynamic, and the overall system activity emerges from the nodes' dynamics, with temporal group interactions resulting from both the activity of the nodes and memory mechanisms. We first show that the EATH model can generate surrogate hypergraphs of several empirical datasets of face-to-face interactions, mimicking temporal and topological properties at the node and hyperedge level. We also showcase the possibility to use the resulting synthetic data in simulations of higher-order contagion dynamics, comparing the outcome of such process on original and surrogate datasets. Finally, we illustrate the flexibility of the model, which can generate synthetic hypergraphs with tunable properties: as an example, we generate "hybrid" temporal hypergraphs, which mix properties of different empirical datasets. Our work opens several perspectives, from the generation of synthetic realistic hypergraphs describing contexts where data collection is difficult to a deeper understanding of dynamical processes on temporal hypergraphs.
@article{Mancastroppa2025PhysRevE, author = {Mancastroppa, Marco and Cencetti, Giulia and Barrat, Alain}, title = {Emerging activity temporal hypergraph: A model for generating realistic time-varying hypergraphs}, journal = {Physical Review E}, year = {2025}, volume = {112}, issue = {5}, pages = {054305}, doi = {10.1103/8rmn-mdgz}, url = {https://link.aps.org/doi/10.1103/8rmn-mdgz}, keywords = {SocioPatterns} } -
Nature Ecology & Evolution 9, 2002 (2025)Theory predicts that high population density leads to more strongly connected spatial and social networks, but how local density drives individuals' positions within their networks is unclear. This gap reduces our ability to understand and predict density-dependent processes. Here we show that density drives greater network connectedness at the scale of individuals within wild animal populations. Across 36 datasets of spatial and social behaviour in >58,000 individual animals, spanning 30 species of fish, reptiles, birds, mammals and insects, 80% of systems exhibit strong positive relationships between local density and network centrality. However, >80% of relationships are nonlinear and 75% are shallower at higher values, indicating saturating trends that probably emerge as a result of demographic and behavioural processes that counteract density's effects. These are stronger and less saturating in spatial compared with social networks, as individuals become disproportionately spatially connected rather than socially connected at higher densities. Consequently, ecological processes that depend on spatial connections are probably more density dependent than those involving social interactions. These findings suggest fundamental scaling rules governing animal social dynamics, which could help to predict network structures in novel systems.
@article{Albery2025NatEcolEvol, author = {Albery, Gregory F. and Becker, Daniel J. and Firth, Josh A. and De Moor, Delphine and Ravindran, Sanjana and Silk, Matthew and Sweeny, Amy R. and Vander Wal, Eric and Webber, Quinn and Allen, Bryony and Babayan, Simon A. and Barve, Sahas and Begon, Mike and Birtles, Richard J. and Block, Theadora A. and Block, Barbara A. and Bradley, Janette E. and Budischak, Sarah and Buesching, Christina and Burthe, Sarah J. and Carlisle, Aaron B. and Caselle, Jennifer E. and Cattuto, Ciro and Chaine, Alexis S. and Chapple, Taylor K. and Cheney, Barbara J. and Clutton-Brock, Timothy and Collier, Melissa and Curnick, David J. and Delahay, Richard J. and Farine, Damien R. and Fenton, Andy and Ferretti, Francesco and Feyrer, Laura and Fielding, Helen and Foroughirad, Vivienne and Frere, Celine and Gardner, Michael G. and Geffen, Eli and Godfrey, Stephanie S. and Graham, Andrea L. and Hammond, Phil S. and Henrich, Maik and Heurich, Marco and Hopwood, Paul and Ilany, Amiyaal and Jackson, Joseph A. and Jackson, Nicola and Jacoby, David M. P. and Jacoby, Ann-Marie and Ježek, Miloš and Kirkpatrick, Lucinda and Klamm, Alisa and Klarevas-Irby, James A. and Knowles, Sarah and Koren, Lee and Krzyszczyk, Ewa and Kusch, Jillian M. and Lambin, Xavier and Lane, Jeffrey E. and Leirs, Herwig and Leu, Stephan T. and Lyon, Bruce E. and Macdonald, David W. and Madsen, Anastasia E. and Mann, Janet and Manser, Marta and Mariën, Joachim and Massawe, Apia and McDonald, Robbie A. and Morelle, Kevin and Mourier, Johann and Newman, Chris and Nussear, Kenneth and Nyaguthii, Brendah and Ogino, Mina and Ozella, Laura and Packer, Craig and Papastamatiou, Yannis P. and Paterson, Steve and Payne, Eric and Pedersen, Amy B. and Pemberton, Josephine M. and Pinter-Wollman, Noa and Planes, Serge and Raulo, Aura and Rodríguez-Muñoz, Rolando and Rudd, Lauren and Sabuni, Christopher and Sah, Pratha and Schallert, Robert J. and Sheldon, Ben C. and Shizuka, Daizaburo and Sih, Andrew and Sinn, David L. and Sluydts, Vincent and Spiegel, Orr and Telfer, Sandra and Thomason, Courtney A. and Tickler, David M. and Tregenza, Tom and VanderWaal, Kimberley and Walmsley, Sam and Walters, Eric L. and Wanelik, Klara M. and Whitehead, Hal and Wielgus, Elodie and Wilson-Aggarwal, Jared and Wohlfeil, Caroline and Bansal, Shweta}, title = {Density-dependent network structuring within and across wild animal systems}, journal = {Nature Ecology \& Evolution}, year = {2025}, month = {sep}, volume = {9}, pages = {2002--2013}, doi = {10.1038/s41559-025-02843-z}, url = {https://doi.org/10.1038/s41559-025-02843-z}, keywords = {SocioPatterns} } -
CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Article No. 817, pp. 1–23 (2025)While digital contact tracing has been extensively studied in Western contexts, its relevance and application in Africa remain largely unexplored. This study focuses on Kenya and Côte d'Ivoire to uncover user perceptions and inform the design of culturally resonant contact tracing technologies. Utilizing a wearable proximity sensor as a technology probe, we conducted field studies with healthcare workers and community members in rural areas through interviews (N = 19) and participatory design workshops (N = 72). Our findings identify critical barriers to adoption, including low awareness, widespread misconceptions, and social stigma. The study emphasizes the need for culturally sensitive and discreet wearables and advocates for awareness campaigns over mandates to foster adoption. Our work addresses the unique needs of Kenyan and Ivorian populations, offering vital design recommendations and insights to guide designers and policymakers in enhancing digital contact tracing adoption across Africa.
@inproceedings{Niksirat2025CHI, author = {Salehzadeh Niksirat, Kavous and Munyendo, Collins W. and Leal Neto, Onicio Batista and Katya, Muswagha and Kouassi, Cyrille and Ochieng, Kevin and Georgina, Angoa and Olayo, Bernard and Barras, Jean-Philippe and Cattuto, Ciro and Aviv, Adam J. and Troncoso, Carmela}, title = {Reimagining Wearable-Based Digital Contact Tracing: Insights from Kenya and Côte d'Ivoire}, booktitle = {CHI Conference on Human Factors in Computing Systems}, series = {CHI '25}, year = {2025}, month = {apr}, location = {Yokohama, Japan}, pages = {1--23}, articleno = {817}, numpages = {23}, doi = {10.1145/3706598.3713817}, url = {https://doi.org/10.1145/3706598.3713817}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, keywords = {HCI4D, Africa, contact tracing, wearables, social acceptability, SocioPatterns} } -
Communications Physics 8, 159 (2025)Surrogate networks can constitute suitable replacements for real networks, in particular to study dynamical processes on networks, when only incomplete or limited datasets are available. As empirical datasets most often present complex features and interplays between structure and temporal evolution, creating surrogate data is however a challenging task, in particular for data describing time-resolved interactions between agents. Here we propose a method to generate surrogate temporal networks that mimic such observed datasets. The method is based on a decomposition of original datasets into temporal subnetworks encoding local structures on a short time scale. These are used as building blocks to generate new synthetic temporal networks that will hence inherit the shape of local interactions from the datasets. Moreover, we take into account larger scale correlations on structural and temporal dimension, using them to inform the process of assembling the building blocks. We showcase the method by generating surrogate networks for several datasets of social interactions and comparing them to the original data. First, we show that surrogate data possess complex structural and temporal features similar to the ones of the original data. Second, we simulate several dynamical processes and compare their outcome on the generated and original datasets.
@article{Cencetti2025CommPhys, author = {Cencetti, Giulia and Barrat, Alain}, title = {Generating surrogate temporal networks from mesoscale building blocks}, journal = {Communications Physics}, year = {2025}, volume = {8}, pages = {159}, doi = {10.1038/s42005-025-02075-4}, url = {https://doi.org/10.1038/s42005-025-02075-4}, keywords = {SocioPatterns} }
2024
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PLoS Computational Biology 20(12), e1012661 (2024)High-resolution temporal data on contacts between hosts provide crucial information on the mixing patterns underlying infectious disease transmission. Publicly available data sets of contact data are however typically recorded over short time windows with respect to the duration of an epidemic. To inform models of disease transmission, data are thus often repeated several times, yielding synthetic data covering long enough timescales. Looping over short term data to approximate contact patterns on longer timescales can lead to unrealistic transmission chains because of the deterministic repetition of all contacts, without any renewal of the contact partners of each individual between successive periods. Real contacts indeed include a combination of regularly repeated contacts (e.g., due to friendship relations) and of more casual ones. In this paper, we propose an algorithm to longitudinally extend contact data recorded in a school setting, taking into account this dual aspect of contacts and in particular the presence of repeated contacts due to friendships. To illustrate the interest of such an algorithm, we then simulate the spread of SARS-CoV-2 on our synthetic contacts using an agent-based model specific to the school setting. We compare the results with simulations performed on synthetic data extended with simpler algorithms to determine the impact of preserving friendships in the data extension method. Notably, the preservation of friendships does not strongly affect transmission routes between classes in the school but leads to different infection pathways between individual students. Our results moreover indicate that gathering contact data during two days in a population is sufficient to generate realistic synthetic contact sequences between individuals in that population on longer timescales. The proposed tool will allow modellers to leverage existing contact data, and contributes to the design of optimal future field data collection.
@article{Calmon2024PLOSCompBiol, author = {Calmon, Lucille and Colosi, Elisabetta and Bassignana, Giulia and Barrat, Alain and Colizza, Vittoria}, title = {Preserving friendships in school contacts: An algorithm to construct synthetic temporal networks for epidemic modelling}, journal = {PLoS Computational Biology}, year = {2024}, volume = {20}, number = {12}, pages = {e1012661}, doi = {10.1371/journal.pcbi.1012661}, url = {https://doi.org/10.1371/journal.pcbi.1012661}, keywords = {SocioPatterns} } -
Nature Communications 15, 9954 (2024)Temporal graphs are commonly used to represent time-resolved relations between entities in many natural and artificial systems. Many techniques were devised to investigate the evolution of temporal graphs by comparing their state at different time points. However, quantifying the similarity between temporal graphs as a whole is an open problem. Here, we use embeddings based on time-respecting random walks to introduce a new notion of distance between temporal graphs. This distance is well-defined for pairs of temporal graphs with different numbers of nodes and different time spans. We study the case of a matched pair of graphs, when a known relation exists between their nodes, and the case of unmatched graphs, when such a relation is unavailable and the graphs may be of different sizes. We use empirical and synthetic temporal network data to show that the distance we introduce discriminates graphs with different topological and temporal properties. We provide an efficient implementation of the distance computation suitable for large-scale temporal graphs.
@article{Dallamico2024NatComm, author = {Dall'Amico, Lorenzo and Barrat, Alain and Cattuto, Ciro}, title = {An embedding-based distance for temporal graphs}, journal = {Nature Communications}, year = {2024}, month = {nov}, volume = {15}, pages = {9954}, doi = {10.1038/s41467-024-54280-4}, url = {https://doi.org/10.1038/s41467-024-54280-4}, keywords = {SocioPatterns} } -
Applied Animal Behaviour Science 279, 106385 (2024)The social environment experienced by livestock can have implications for their health, welfare, and subsequently, their productivity. Yet few studies have assessed the putative impact of positive cow-cow interactions, such as proximity to preferred herd mates and engaging in grooming, on milk production and udder health. To address this, we used cattle proximity as a proxy for affiliative interactions between cows in three dairy herds in south-west England over one week study periods. We created proximity networks of dairy cows and measured cow-cow associations according to milk yield, somatic cell count (SCC; an indicator of mastitis), parity (number of lactations in the cow's lifetime), and lactation stage (grouped by days in milk for current lactation). We then assessed associations between social factors and production and health measures (milk yield and SCC). In all three herds, cows interacted more with cows in the same parity, suggesting early social bonding may be evident later in life and that grouping animals in terms of parity might encourage affiliative interactions. However, there was no link between milk yield or SCC and time spent near other cows or near preferred cows, suggesting that these factors of the social environment are not associated with production or udder health.
@article{Fielding2024dairy, author = {Fielding, Helen R. and Silk, Matthew J. and McKinley, Trevelyan J. and Delahay, Richard J. and Wilson-Aggarwal, Jared K. and Gauvin, Laetitia and Ozella, Laura and Cattuto, Ciro and McDonald, Robbie A.}, title = {Social interactions of dairy cows and their association with milk yield and somatic cell count}, journal = {Applied Animal Behaviour Science}, year = {2024}, month = {oct}, volume = {279}, pages = {106385}, doi = {10.1016/j.applanim.2024.106385}, url = {https://doi.org/10.1016/j.applanim.2024.106385}, keywords = {SocioPatterns} } -
EPJ Data Science 13, 50 (2024)The richness of many complex systems stems from the interactions among their components. The higher-order nature of these interactions, involving many units at once, and their temporal dynamics constitute crucial properties that shape the behaviour of the system itself. An adequate description of these systems is offered by temporal hypergraphs, that integrate these features within the same framework. However, tools for their temporal and topological characterization are still scarce. Here we develop a series of methods specifically designed to analyse the structural properties of temporal hypergraphs at multiple scales. Leveraging the hyper-core decomposition of hypergraphs, we follow the evolution of the hyper-cores through time, characterizing the hypergraph structure and its temporal dynamics at different topological scales, and quantifying the multi-scale structural stability of the system. We also define two static hypercoreness centrality measures that provide an overall description of the nodes aggregated structural behaviour. We apply the characterization methods to several data sets, establishing connections between structural properties and specific activities within the systems. Finally, we show how the proposed method can be used as a model-validation tool for synthetic temporal hypergraphs, distinguishing the higher-order structures and dynamics generated by different models from the empirical ones, and thus identifying the essential model mechanisms to reproduce the empirical hypergraph structure and evolution. Our work opens several research directions, from the understanding of dynamic processes on temporal higher-order networks to the design of new models of time-varying hypergraphs.
@article{Mancastroppa2024EPJDataSci, author = {Mancastroppa, Marco and Iacopini, Iacopo and Petri, Giovanni and Barrat, Alain}, title = {The structural evolution of temporal hypergraphs through the lens of hyper-cores}, journal = {EPJ Data Science}, year = {2024}, volume = {13}, pages = {50}, doi = {10.1140/epjds/s13688-024-00490-1}, url = {https://doi.org/10.1140/epjds/s13688-024-00490-1}, keywords = {SocioPatterns} } -
PLoS Computational Biology 20(6), e1012206 (2024)Contagion processes, representing the spread of infectious diseases, information, or social behaviors, are often schematized as taking place on networks, which encode for instance the interactions between individuals. The impact of the network structure on spreading process has been widely investigated, but not the reverse question: do different processes unfolding on a given network lead to different infection patterns? How do the infection patterns depend on a model's parameters or on the nature of the contagion processes? Here we address this issue by investigating the infection patterns for a variety of models. In simple contagion processes, where contagion events involve one connection at a time, we find that the infection patterns are extremely robust across models and parameters. In complex contagion models instead, in which multiple interactions are needed for a contagion event, non-trivial dependencies on models parameters emerge, as the infection pattern depends on the interplay between pairwise and group contagions. In models involving threshold mechanisms moreover, slight parameter changes can significantly impact the spreading paths. Our results show that it is possible to study crucial features of a spread from schematized models, and inform us on the variations between spreading patterns in processes of different nature.
@article{Contreras2024PLOSCompBiol, author = {Contreras, Diego Andrés and Cencetti, Giulia and Barrat, Alain}, title = {Infection patterns in simple and complex contagion processes on networks}, journal = {PLoS Computational Biology}, year = {2024}, volume = {20}, number = {6}, pages = {e1012206}, doi = {10.1371/journal.pcbi.1012206}, url = {https://doi.org/10.1371/journal.pcbi.1012206}, keywords = {SocioPatterns} } -
PLoS ONE 19(3), e0296810 (2024)Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure.
@article{Dallamico2024PLOSONE, author = {Dall'Amico, Lorenzo and Kleynhans, Jackie and Gauvin, Laetitia and Tizzoni, Michele and Ozella, Laura and Makhasi, Mvuyo and Wolter, Nicole and Language, Brigitte and Wagner, Ryan G. and Cohen, Cheryl and Tempia, Stefano and Cattuto, Ciro}, title = {Estimating household contact matrices structure from easily collectable metadata}, journal = {PLoS ONE}, year = {2024}, month = {mar}, volume = {19}, number = {3}, pages = {e0296810}, doi = {10.1371/journal.pone.0296810}, url = {https://doi.org/10.1371/journal.pone.0296810}, keywords = {SocioPatterns} } -
Journal of Complex Networks 12(2), cnae004 (2024)Temporal networks are commonly used to represent dynamical complex systems like social networks, simultaneous firing of neurons, human mobility or public transportation. Their dynamics may evolve on multiple time scales characterizing for instance periodic activity patterns or structural changes. The detection of these time scales can be challenging from the direct observation of simple dynamical network properties like the activity of nodes or the density of links. Here, we propose two new methods, which rely on already established static representations of temporal networks, namely supra-adjacency and temporal event graphs. We define dissimilarity metrics extracted from these representations and compute their power spectra from their Fourier transforms to effectively identify dominant periodic time scales characterizing the changes of the temporal network. We demonstrate our methods using synthetic and real-world data sets describing various kinds of temporal networks. We find that while in all cases the two methods outperform the reference measures, the supra-adjacency-based method identifies more easily periodic changes in network density, while the temporal event graph-based method is better suited to detect periodic changes in the group structure of the network. Our methodology may provide insights into different phenomena occurring at multiple time scales in systems represented by temporal networks.
@article{Andres2024JComplexNetw, author = {Andres, Elsa and Barrat, Alain and Karsai, Márton}, title = {Detecting periodic time scales of changes in temporal networks}, journal = {Journal of Complex Networks}, year = {2024}, volume = {12}, number = {2}, pages = {cnae004}, doi = {10.1093/comnet/cnae004}, url = {https://doi.org/10.1093/comnet/cnae004}, keywords = {SocioPatterns} }
2023
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Nature Communications 14, 6223 (2023)Going beyond networks, to include higher-order interactions of arbitrary sizes, is a major step to better describe complex systems. In the resulting hypergraph representation, tools to identify structures and central nodes are scarce. We consider the decomposition of a hypergraph in hyper-cores, subsets of nodes connected by at least a certain number of hyperedges of at least a certain size. We show that this provides a fingerprint for data described by hypergraphs and suggests a novel notion of centrality, the hypercoreness. We assess the role of hyper-cores and nodes with large hypercoreness in higher-order dynamical processes: such nodes have large spreading power and spreading processes are localized in central hyper-cores. Additionally, in the emergence of social conventions very few committed individuals with high hypercoreness can rapidly overturn a majority convention. Our work opens multiple research avenues, from comparing empirical data to model validation and study of temporally varying hypergraphs.
@article{Mancastroppa2023NatCommun, author = {Mancastroppa, Marco and Iacopini, Iacopo and Petri, Giovanni and Barrat, Alain}, title = {Hyper-cores promote localization and efficient seeding in higher-order processes}, journal = {Nature Communications}, year = {2023}, volume = {14}, pages = {6223}, doi = {10.1038/s41467-023-41887-2}, url = {https://doi.org/10.1038/s41467-023-41887-2}, keywords = {SocioPatterns} } -
eLife 12, e84753 (2023)BACKGROUND: Households are an important location for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, especially during periods when travel and work was restricted to essential services. We aimed to assess the association of close-range contact patterns with SARS-CoV-2 transmission. METHODS: We deployed proximity sensors for two weeks to measure face-to-face interactions between household members after SARS-CoV-2 was identified in the household, in South Africa, 2020–2021. We calculated the duration, frequency, and average duration of close-range proximity events with SARS-CoV-2 index cases. We assessed the association of contact parameters with SARS-CoV-2 transmission using mixed effects logistic regression accounting for index and household member characteristics. RESULTS: We included 340 individuals (88 SARS-CoV-2 index cases and 252 household members). On multivariable analysis, factors associated with SARS-CoV-2 acquisition were index cases with minimum C_t value <30 (aOR 16.8 95\% CI 3.1–93.1) vs >35, and female contacts (aOR 2.5 95\% CI 1.3–5.0). No contact parameters were associated with acquisition (aOR 1.0–1.1) for any of the duration, frequency, cumulative time in contact, or average duration parameters. CONCLUSIONS: We did not find an association between close-range proximity events and SARS-CoV-2 household transmission. Our findings may be due to study limitations, that droplet-mediated transmission during close-proximity contacts plays a smaller role than airborne transmission of SARS-CoV-2 in the household, or due to high contact rates in households.
@article {10.7554/eLife.84753, article_type = {journal}, title = {Association of close-range contact patterns with SARS-CoV-2: a household transmission study}, author = {Kleynhans, Jackie and Dall'Amico, Lorenzo and Gauvin, Laetitia and Tizzoni, Michele and Maloma, Lucia and Walaza, Sibongile and Martinson, Neil A and von Gottberg, Anne and Wolter, Nicole and Makhasi, Mvuyo and Cohen, Cheryl and Cattuto, Ciro and Tempia, Stefano and SA-S-HTS Group}, editor = {Cobey, Sarah E and Ferguson, Neil M and Pitzer, Virginia E}, volume = 12, year = 2023, month = {jul}, pub_date = {2023-07-18}, pages = {e84753}, citation = {eLife 2023;12:e84753}, doi = {10.7554/eLife.84753}, url = {https://doi.org/10.7554/eLife.84753}, keywords = {SARS-CoV-2, transmission, household, contacts}, journal = {eLife}, issn = {2050-084X}, publisher = {eLife Sciences Publications, Ltd}, } -
Phys. Rev. Lett. 130, 247401 (2023)Contagion processes on networks, including disease spreading, information diffusion, or social behaviors propagation, can be modeled as simple contagion, i.e., as a contagion process involving one connection at a time, or as complex contagion, in which multiple interactions are needed for a contagion event. Empirical data on spreading processes, however, even when available, do not easily allow us to uncover which of these underlying contagion mechanisms is at work. We propose a strategy to discriminate between these mechanisms upon the observation of a single instance of a spreading process. The strategy is based on the observation of the order in which network nodes are infected, and on its correlations with their local topology: these correlations differ between processes of simple contagion, processes involving threshold mechanisms, and processes driven by group interactions (i.e., by “higher-order” mechanisms). Our results improve our understanding of contagion processes and provide a method using only limited information to distinguish between several possible contagion mechanisms.
@article{PhysRevLett.130.247401, title = {Distinguishing Simple and Complex Contagion Processes on Networks}, author = {Cencetti, Giulia and Contreras, Diego Andr\'es and Mancastroppa, Marco and Barrat, Alain}, journal = {Phys. Rev. Lett.}, volume = {130}, issue = {24}, pages = {247401}, numpages = {7}, year = {2023}, month = {Jun}, publisher = {American Physical Society}, doi = {10.1103/PhysRevLett.130.247401}, url = {https://link.aps.org/doi/10.1103/PhysRevLett.130.247401}, keywords = {SocioPatterns} } -
PLoS Computational Biology 19(2), e1010854 (2023)The structure of social networks strongly affects how different phenomena spread in human society, from the transmission of information to the propagation of contagious diseases. It is well-known that heterogeneous connectivity strongly favors spread, but a precise characterization of the redundancy present in social networks and its effect on the robustness of transmission is still lacking. This gap is addressed by the metric backbone, a weight- and connectivity-preserving subgraph that is sufficient to compute all shortest paths of weighted graphs. This subgraph is obtained via algebraically-principled axioms and does not require statistical sampling based on null-models. We show that the metric backbones of nine contact networks obtained from proximity sensors in a variety of social contexts are generally very small, 49% of the original graph for one and ranging from about 6% to 20% for the others. This reflects a surprising amount of redundancy and reveals that shortest paths on these networks are very robust to random attacks and failures. We also show that the metric backbone preserves the full distribution of shortest paths of the original contact networks—which must include the shortest inter- and intra-community distances that define any community structure—and is a primary subgraph for epidemic transmission based on pure diffusion processes. This suggests that the organization of social contact networks is based on large amounts of shortest-path redundancy which shapes epidemic spread in human populations. Thus, the metric backbone is an important subgraph with regard to epidemic spread, the robustness of social networks, and any communication dynamics that depend on complex network shortest paths.
@article{Correia2023PLOSCompBiol, author = {Brattig Correia, Rion and Barrat, Alain and Rocha, Luis M.}, title = {Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs}, journal = {PLoS Computational Biology}, year = {2023}, volume = {19}, number = {2}, pages = {e1010854}, doi = {10.1371/journal.pcbi.1010854}, url = {https://doi.org/10.1371/journal.pcbi.1010854}, keywords = {SocioPatterns} } -
Physical Review E 107(2), 024301 (2023)Temporal networks of face-to-face interactions between individuals are useful proxies of the dynamics of social systems on fast timescales. Several empirical statistical properties of these networks have been shown to be robust across a large variety of contexts. To better grasp the role of various mechanisms of social interactions in the emergence of these properties, models in which schematic implementations of such mechanisms can be carried out have proven useful. Here, we put forward a framework to model temporal networks of human interactions based on the idea of a coevolution and feedback between (i) an observed network of instantaneous interactions and (ii) an underlying unobserved social bond network: Social bonds partially drive interaction opportunities and in turn are reinforced by interactions and weakened or even removed by the lack of interactions. Through this coevolution, we also integrate in the model well-known mechanisms such as triadic closure, but also the impact of shared social context and nonintentional (casual) interactions, with several tunable parameters. We then propose a method to compare the statistical properties of each version of the model with empirical face-to-face interaction data sets to determine which sets of mechanisms lead to realistic social temporal networks within this modeling framework.
@article{LeBail2023PhysRevE, author = {Le Bail, Didier and G{\'e}nois, Mathieu and Barrat, Alain}, title = {Modeling framework unifying contact and social networks}, journal = {Physical Review E}, volume = {107}, number = {2}, pages = {024301}, year = {2023}, doi = {10.1103/PhysRevE.107.024301}, url = {https://doi.org/10.1103/PhysRevE.107.024301}, keywords = {SocioPatterns} } -
Euro Surveill. 2023;28(5):pii=2200192Background; As record cases of Omicron variant were registered in Europe in early 2022, schools remained a vulnerable setting undergoing large disruption. Aim: Through mathematical modelling, we compared school protocols of reactive screening, regular screening, and reactive class closure implemented in France, in Baselland (Switzerland), and in Italy, respectively, and assessed them in terms of case prevention, testing resource demand, and schooldays lost. Methods: We used a stochastic agent-based model of SARS-CoV-2 transmission in schools accounting for within- and across-class contacts from empirical contact data. We parameterised it to the Omicron BA.1 variant to reproduce the French Omicron wave in January 2022. We simulated the three protocols to assess their costs and effectiveness for varying peak incidence rates in the range experienced by European countries. Results: We estimated that at the high incidence rates registered in France during the Omicron BA.1 wave in January 2022, the reactive screening protocol applied in France required higher test resources compared with the weekly screening applied in Baselland (0.50 vs 0.45 tests per student-week), but achieved considerably lower control (8% vs 21% reduction of peak incidence). The reactive class closure implemented in Italy was predicted to be very costly, leading to > 20% student-days lost. Conclusions: At high incidence conditions, reactive screening protocols generate a large and unplanned demand in testing resources, for marginal control of school transmissions. Comparable or lower resources could be more efficiently used through weekly screening. Our findings can help define incidence levels triggering school protocols and optimise their cost-effectiveness.
@article{colosi2022minimizing, title={Minimising school disruption under high incidence conditions due to the Omicron variant in France, Switzerland, Italy, in January 2022}, author={Colosi, Elisabetta and Bassignana, Giulia and Barrat, Alain and Lina, Bruno and Vanhems, Philippe and Bielicki, Julia and Colizza, Vittoria}, journal={Eurosurveillance}, volume={28}, number={5}, pages={2200192}, year={2023}, publisher={European Centre for Disease Prevention and Control}} -
Applied Animal Behaviour Science 259, 105847 (2023)Biologging offers a novel opportunity to detect lameness in sheep by identifying behavioural changes in activity patterns via accelerometers and proximity sensors. In this study, 50 Poll Dorset ewes rearing lambs were equipped with biologgers; behavioural data were analysed over a 13-day period to compare activity and standing behaviour between lame and non-lame animals. Results show decreased standing and grazing time, and increased inactivity among lame ewes and lambs, suggesting that biologging can act as an early detection tool for lameness.
@article{Lewis2023_biologgers_lame_sheep, author = {Lewis, Katharine E. and Price, Emily and Croft, Darren P. and Green, Laura E. and Ozella, Laura and Cattuto, Ciro and Langford, Joss}, title = {Potential role of biologgers to automate detection of lame ewes and lambs}, journal = {Applied Animal Behaviour Science}, year = {2023}, volume = {259}, pages = {105847}, doi = {10.1016/j.applanim.2023.105847} }
2022
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Frontiers in Veterinary Science 9, 1027020 (2022)Sheep have heterogeneous social connections that influence transmission of some infectious diseases. Footrot is one of the top five globally important diseases of sheep, it is caused by Dichelobacter nodosus and transmits between sheep when infectious feet contaminate surfaces e.g. pasture. Surfaces remain infectious for a few minutes to a few days, depending on surface moisture levels. Susceptible sheep in close social contact with infectious sheep might be at risk of becoming infected because they are likely to step onto infectious footprints, particularly dams and lambs, as they cluster together. High resolution proximity sensors were deployed on 40 ewes and their 54 lambs aged 5–27 days, in a flock with endemic footrot in Devon, UK for 13 days. Sheep locomotion was scored daily by using a 0–6 integer scale. Sheep were defined lame when their locomotion score (LS) was ≥ 2, and a case of lameness was defined as LS ≥ 2 for ≥ 2 days. Thirty-two sheep (19 ewes, 9 single, and 4 twin lambs) became lame during the study, while 14 (5 ewes, 5 single, and 4 twin lambs) were lame initially. The 46 sheep were from 29 family groups; transmission from ewes to lambs was bidirectional. At least 15% of new cases of footrot were attributed to within-family transmission; the occurrence of lameness was higher in single lambs than twin lambs. Estimates suggest at least 4% of transmission resulted from close contact across the flock. Non-family sheep spent only ~0.03 hours/day in contact with others outside their family; lamb-dam contact times were higher in single lambs (≈1.5 h/day) than twin lambs (≈0.9 h/day), which may explain the family-clustered lameness pattern observed. We conclude that while most transmission of lameness is not attributable to close contact alone, for ewes with young lambs some transmission occurs within family groups — likely due to the prolonged contact between lambs and their dam.
@article{Lewis2022_ewes_lambs_lameness, author = {Lewis, Katharine E. and Price, Emily and Croft, Darren P. and Langford, Joss and Ozella, Laura and Cattuto, Ciro and Green, Laura E.}, title = {Social behaviour and transmission of lameness in a flock of ewes and lambs}, journal = {Frontiers in Veterinary Science}, year = {2022}, volume = {9}, pages = {1027020}, doi = {10.3389/fvets.2022.1027020} } -
Journal of the Royal Society Interface 19, 20220164 (2022)Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.
@article{contreras2022impact, author = {Contreras, Diego Andrés AND Colosi, Elisabetta AND Bassignana, Giulia AND Colizza, Vittoria and Barrat, Alain}, title={Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases}, journal={J. R. Soc. Interface}, volume={19}, pages={20220164}, year={2022}} -
The Lancet Infectious Diseases 22(7), 977 (2022)BACKGROUND. Schools were closed extensively in 2020–21 to counter SARS-CoV-2 spread, impacting students' education and wellbeing. With highly contagious variants expanding in Europe, safe options to maintain schools open are urgently needed. By estimating school-specific transmissibility, our study evaluates costs and benefits of different protocols for SARS-CoV-2 control at school. METHODS. We developed an agent-based model of SARS-CoV-2 transmission in schools. We used empirical contact data in a primary and a secondary school and data from pilot screenings in 683 schools during the alpha variant (B.1.1.7) wave in March–June, 2021, in France. We fitted the model to observed school prevalence to estimate the school-specific effective reproductive number for the alpha (Ralpha) and delta (B.1.617.2; Rdelta) variants and performed a cost–benefit analysis examining different intervention protocols. FINDINGS. We estimated Ralpha to be 1·40 (95% CI 1·35–1·45) in the primary school and 1·46 (1·41–1·51) in the secondary school during the spring wave, higher than the time-varying reproductive number estimated from community surveillance. Considering the delta variant and vaccination coverage in Europe as of mid-September, 2021, we estimated Rdelta to be 1·66 (1·60–1·71) in primary schools and 1·10 (1·06–1·14) in secondary schools. Under these conditions, weekly testing of 75% of unvaccinated students (PCR tests on saliva samples in primary schools and lateral flow tests in secondary schools), in addition to symptom-based testing, would reduce cases by 34% (95% CI 32–36) in primary schools and 36% (35–39) in secondary schools compared with symptom-based testing alone. Insufficient adherence was recorded in pilot screening (median ≤53%). Regular testing would also reduce student-days lost up to 80% compared with reactive class closures. Moderate vaccination coverage in students would still benefit from regular testing for additional control—ie, weekly testing 75% of unvaccinated students would reduce cases compared with symptom-based testing only, by 23% in primary schools when 50% of children are vaccinated. INTERPRETATION. The COVID-19 pandemic will probably continue to pose a risk to the safe and normal functioning of schools. Extending vaccination coverage in students, complemented by regular testing with good adherence, are essential steps to keep schools open when highly transmissible variants are circulating.
@article{colosi2022screening, title={Screening and vaccination against COVID-19 to minimise school closure: a modelling study}, author={Colosi, Elisabetta and Bassignana, Giulia and Contreras, Diego Andr{\'e}s and Poirier, Canelle and Bo{\"e}lle, Pierre-Yves and Cauchemez, Simon and Yazdanpanah, Yazdan and Lina, Bruno and Fontanet, Arnaud and Barrat, Alain and others}, journal={The Lancet Infectious Diseases}, volume={22}, number={7}, pages={977--989}, year={2022}, publisher={Elsevier}} -
Communications Physics 5, 64 (2022)How can minorities of individuals overturn social conventions? The theory of critical mass states that when a committed minority reaches a critical size, a cascade of behavioural changes can occur, overturning apparently stable social norms. Evidence comes from theoretical and empirical studies in which minorities of very different sizes, including extremely small ones, manage to bring a system to its tipping point. Here, we explore this diversity of scenarios by introducing group interactions as a crucial element of realism into a model for social convention. We find that the critical mass necessary to trigger behaviour change can be very small if individuals have a limited propensity to change their views. Moreover, the ability of the committed minority to overturn existing norms depends in a complex way on the group size. Our findings reconcile the different sizes of critical mass found in previous investigations and unveil the critical role of groups in such processes. This further highlights the importance of the emerging field of higher-order networks, beyond pairwise interactions.
@article{iacopini2022_comm_phys, author = {Iacopini, Iacopo and Petri, Giovanni and Baronchelli, Andrea and Barrat, Alain}, title = {Group interactions modulate critical mass dynamics in social convention}, journal = {Communications Physics}, volume = {5}, number = {1}, pages = {64}, year = {2022} } -
Applied Animal Behaviour Science 246, 105515 (2022)Sheep are highly social domesticated animals that evolved to live in large and structured groups. As in other group-living species, individuals differ in the level of association they have with others, and these associations often result in lasting and stable social bonds. However, there are substantial gaps in our knowledge of the temporal social dynamics in sheep, and how their social bonds vary in relation to environmental changes. Here, we aimed to assess the social relationships between ewes and lambs, collecting dyadic associations data of 41 ewes and 55 lambs through the use of proximity loggers on a commercial farm. We computed association indices between each pair of animals to estimate the proportion of time any two individuals associated. We first generated an aggregated network of the whole 13-day observation period, and we compared the values of association indices between different types of dyads (i.e., lamb-mother, lamb-ewe non-mother, lambs littermates, lambs non-littermates, ewe-ewe). We generated aggregated contact networks on a daily scale to compare the ego-networks of individuals obtained in successive time windows to determine how stable social associations were over time. As would be expected, the highest values of association indices were found in dyads formed by dams and lambs (0.17 ± 0.11) and by lambs of the same litter (0.32 ± 0.09). Both single-born and twin-born lambs showed high association values with their dams (single-born: 0.24 ± 0.11; twin-born: 0.1 ± 0.05), although twin-born lambs had stronger associations with their littermates compared with those with their mothers (p-value < 0.001). At a temporal level, the flock exhibited periods of high network stability at the beginning and at the end of the study period. However, periods of social instability occurred one-two days after management interventions, such as changes in field size. These transitory periods of social instability were driven by changes in the association patterns of ewes and single born lambs. In contrast, the ego-networks of twin-born lambs remained relatively stable, supported by strong association levels between twins. Thus, the social instability of the social network was not a global one, but some parts of the network remained stable while others underwent important changes. Our study represents a first step to track social associations within an ewe-lamb group using proximity tags and advances our understanding of the social organisation of sheep. We highlight the importance of detecting social network instability as a consequence of different types of perturbations in order to identify the presence of social rearrangements.
@article{OZELLA2022105515, title = {Association networks and social temporal dynamics in ewes and lambs}, journal = {Applied Animal Behaviour Science}, volume = {246}, pages = {105515}, year = {2022}, issn = {0168-1591}, doi = {https://doi.org/10.1016/j.applanim.2021.105515}, url = {https://www.sciencedirect.com/science/article/pii/S0168159121003026}, author = {Laura Ozella and Emily Price and Joss Langford and Kate E. Lewis and Ciro Cattuto and Darren P. Croft}, keywords = {Association indices, Network analysis, Proximity sensors, Sheep, Network stability, Cosine similarity}, }
2021
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Proceedings of the Royal Society B 288, 20211164 (2021)Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. However, going from temporal network data (i.e. a stream of interactions between individuals) to a representation of the social group’s evolution remains a challenge. Indeed, the temporal network at any specific time contains only the interactions taking place at that time and aggregating on successive time-windows also has important limitations. Here, we present a new framework to study the dynamic evolution of social networks based on the idea that social relationships are interdependent: as the time we can invest in social relationships is limited, reinforcing a relationship with someone is done at the expense of our relationships with others. We implement this interdependence in a parsimonious two-parameter model and apply it to several human and non-human primates’ datasets to demonstrate that this model detects even small and short perturbations of the networks that cannot be detected using the standard technique of successive aggregated networks. Our model solves a long-standing problem by providing a simple and natural way to describe the dynamic evolution of social networks, with far-reaching consequences for the study of social networks and social evolution.
@article{doi:10.1098/rspb.2021.1164, author = {Gelardi, Valeria and Le Bail, Didier and Barrat, Alain and Claidiere, Nicolas}, title = {From temporal network data to the dynamics of social relationships}, journal = {Proceedings of the Royal Society B: Biological Sciences}, volume = {288}, number = {1959}, pages = {20211164}, year = {2021}, doi = {10.1098/rspb.2021.1164}, URL = {https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2021.1164}, eprint = {https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2021.1164} } -
EPJ Data Science 10, 46 (2021)Measuring close proximity interactions between individuals can provide key information on social contacts in human communities and related behaviours. This is even more essential in rural settings in low- and middle-income countries where there is a need to understand contact patterns for the implementation of strategies for social protection interventions. We report the quantitative assessment of contact patterns in a village in rural Malawi, based on proximity sensors technology that allows for high-resolution measurements of social contacts. Our results revealed that the community structure of the village was highly correlated with the household membership of the individuals, thus confirming the importance of the family ties within the village. Social contacts within households occurred mainly between adults and children, and adults and adolescents and most of the inter-household social relationships occurred among adults and among adolescents. At the individual level, age and gender social assortment were observed in the inter-household network, and age disassortativity was instead observed in intra-household networks. Moreover, we obtained a clear trend of the daily contact activity of the village. Family members congregated in the early morning, during lunch time and dinner time. In contrast, inter-household contact activity displayed a growth from the morning, reaching a maximum in the afternoon.
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Phys. Rev. E 103, 052304 (2021)Many systems of socioeconomic interests find a convenient representation in the form of temporal networks, i.e., sets of nodes and interactions occurring at specified times. In the corresponding data sets, however, crucial elements coexist with nonessential ones and noise. Several methods have thus been proposed to extract a “network backbone,” i.e., the set of most important links in a network data set. The outcome of such methods can be seen as compressed versions of the original data. However, the question of how to practically use such reduced views of the data has not been tackled: for instance, using them directly in numerical simulations of processes on networks might lead to important biases. Overall, such reduced views of the data might not be actionable without an adequate decompression method. Here, we address this issue by putting forward and exploring several systematic procedures to build surrogate data from various kinds of temporal network backbones. In particular,we explore how much information about the original data needs to be retained alongside the backbone so that the surrogate data can be used in data-driven numerical simulations of spreading processes on a wide range of spreading parameters. We illustrate our results using empirical temporal networks with a broad variety of structures and properties. Our results give hints on how to best summarize complex data sets so that they remain actionable. Moreover, they show how ensembles of surrogate data with similar properties can be obtained from an original single data set, without any modeling assumptions.
@article{PhysRevE.103.052304, title = {Building surrogate temporal network data from observed backbones}, author = {Presigny, Charley and Holme, Petter and Barrat, Alain}, journal = {Phys. Rev. E}, volume = {103}, issue = {5}, pages = {052304}, numpages = {11}, year = {2021}, month = {May}, publisher = {American Physical Society}, doi = {10.1103/PhysRevE.103.052304}, url = {https://link.aps.org/doi/10.1103/PhysRevE.103.052304} } -
J. R. Soc. Interface 18:20201000 (2021)Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and contain re-emergence phenomena. Targeted measures such as case isolation and contact tracing can alleviate the societal cost of lock-downs by containing the spread where and when it occurs. To assess the relative and combined impact of manual contact tracing (MCT) and digital (app-based) contact tracing, we feed a compartmental model for COVID-19 with high-resolution datasets describing contacts between individuals in several contexts. We show that the benefit (epidemic size reduction) is generically linear in the fraction of contacts recalled during MCT and quadratic in the app adoption, with no threshold effect. The cost (number of quarantines) versus benefit curve has a characteristic parabolic shape, independent of the type of tracing, with a potentially high benefit and low cost if app adoption and MCT efficiency are high enough. Benefits are higher and the cost lower if the epidemic reproductive number is lower, showing the importance of combining tracing with additional mitigation measures. The observed phenomenology is qualitatively robust across datasets and parameters. We moreover obtain analytically similar results on simplified models.
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EPJ Data Science 10, 22 (2021)Low-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks – rather than of the network structure itself – with the goal of enabling prediction tasks related to the evolution and outcome of these processes. We achieve this by using a lossless modified supra-adjacency representation of temporal networks and building on standard embedding techniques for static graphs based on random walks. We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks. In particular, we illustrate the performance of our approach for the prediction of nodes’ epidemic states in single instances of a spreading process. We show how framing this task as a supervised multi-label classification task on the embedding vectors allows us to estimate the temporal evolution of the entire system from a partial sampling of nodes at random times, with potential impact for nowcasting infectious disease dynamics.
@article{sato2021_epjds, author = {Sato, Koya and Oka, Mizuki and Barrat, Alain and Cattuto, Ciro}, title = {Predicting partially observed processes on temporal networks by Dynamics-Aware Node Embeddings (DyANE)}, journal = {EPJ Data Science}, volume = {10}, number = {1}, pages = {22}, year = {2021} } -
Nature Communications 12, 1655 (2021)Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.
@article{cencetti2021_nat_comm, author = {Cencetti, Giacomo and Santin, Gabriele and Longa, Antonio and Pigani, Emanuele and Barrat, Alain and Cattuto, Ciro and Lehmann, Sune and Salath{\'e}, Marcel and Lepri, Bruno}, title = {Digital proximity tracing on empirical contact networks for pandemic control}, journal = {Nature Communications}, volume = {12}, number = {1}, pages = {1655}, year = {2021} }
2020
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ACM Transactions on Knowledge Discovery from Data 15(1), 2 (2020)When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). In this article, we tackle this task by introducing a notion of temporal core decomposition where each core is associated with two quantities, its coreness, which quantifies how densely it is connected, and its span, which is a temporal interval: we call such cores span-cores. For a temporal network defined on a discrete temporal domain T, the total number of time intervals included in T is quadratic in |T|, so that the total number of span-cores is potentially quadratic in |T| as well. Our first main contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both their coreness property and their span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly extract the maximal ones without computing all span-cores. Finally, as a third contribution, we introduce the problem of temporal community search, where a set of query vertices is given as input, and the goal is to find a set of densely-connected subgraphs containing the query vertices and covering the whole underlying temporal domain T. We derive a connection between this problem and the problem of finding (maximal) span-cores. Based on this connection, we show how temporal community search can be solved in polynomial-time via dynamic programming, and how the maximal span-cores can be profitably exploited to significantly speed-up the basic algorithm. We provide an extensive experimentation on several real-world temporal networks of widely different origins and characteristics. Our results confirm the efficiency and scalability of the proposed methods. Moreover, we showcase the practical relevance of our techniques in a number of applications on temporal networks, describing face-to-face contacts between individuals in schools. Our experiments highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics, detecting/correcting anomalies in the data, and graph-embedding-based network classification.
@article{10.1145/3418226, author = {Galimberti, Edoardo and Ciaperoni, Martino and Barrat, Alain and Bonchi, Francesco and Cattuto, Ciro and Gullo, Francesco}, title = {Span-core Decomposition for Temporal Networks: Algorithms and Applications}, journal = {ACM Transactions on Knowledge Discovery from Data}, volume = {15}, number = {1}, articleno = {2}, numpages = {44}, year = {2020}, month = {dec}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {1556-4681}, doi = {10.1145/3418226}, url = {https://doi.org/10.1145/3418226}, keywords = {Temporal networks, community search, core decomposition, face-to-face interaction networks, maximal cores} } -
Scientific Reports 10, 12529 (2020).Temporal networks are widely used to represent a vast diversity of systems, including in particular social interactions, and the spreading processes unfolding on top of them. The identification of structures playing important roles in such processes remains largely an open question, despite recent progresses in the case of static networks. Here, we consider as candidate structures the recently introduced concept of span-cores: the span-cores decompose a temporal network into subgraphs of controlled duration and increasing connectivity, generalizing the core-decomposition of static graphs. To assess the relevance of such structures, we explore the effectiveness of strategies aimed either at containing or maximizing the impact of a spread, based respectively on removing span-cores of high cohesiveness or duration to decrease the epidemic risk, or on seeding the process from such structures. The effectiveness of such strategies is assessed in a variety of empirical data sets and compared to baselines that use only static information on the centrality of nodes and static concepts of coreness, as well as to a baseline based on a temporal centrality measure. Our results show that the most stable and cohesive temporal cores play indeed an important role in epidemic processes on temporal networks, and that their nodes are likely to include influential spreaders.
@article{Ciaperoni2020, author = {Ciaperoni, Martino and Galimberti, Edoardo and Bonchi, Francesco and Cattuto, Ciro and Gullo, Francesco and Barrat, Alain}, title = {Relevance of temporal cores for epidemic spread in temporal networks}, journal = {Scientific Reports}, volume = {10}, number = {1}, pages = {12529}, year = {2020}, doi = {10.1038/s41598-020-69464-3}, url = {https://doi.org/10.1038/s41598-020-69464-3}, issn = {2045-2322} } -
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476, 20190737 (2020)Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioural observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Moreover, sensor data yield a reliable social network over short time scales and can be used for long-term studies, showing their important potential for detailed studies of the evolution of animal social groups.
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Applied Animal Behaviour Science, 104964 (2020)Social structures of group-living farm animals can have important implications for animal welfare and productivity. Understanding which factors can have an effect on social behaviour is thus important in order to develop the best management strategies in livestock industries. Here, we studied the social network structure of a flock of 84 Poll Dorset ewes and collecting dyadic associations data through the use of proximity sensors during two study periods. First, we analysed the social structure of ewes at a group-level, by analysing the community structure, and at individual-level, by determining whether the ewes showed social differentiation in their association patterns. Second, we measured for the contribution of genetic relatedness, age, weight, reproductive status and previous management sub grouping on social associations to test for homophily effects. Lastly, we evaluated whether social clustering was influenced by the stocking density of individuals in a field, and by weather parameters, through the use of two climatic indices, the Temperature-Humidity Index (THI) and the Wind Chill Index (WCI). Our results showed that the pairwise associations between ewes are not-random and highly heterogeneous, both in total time spent in contact and in contacts duration. There was no evidence that ewes were subdivided into social communities, and at individual level, they showed markedly differentiated social relationships, demonstrating preferences in social ties. However, the factors that influenced the preferred social interactions between individuals changed over time. In the first study period ewes tended to maintain the social bonds formed in previous management sub grouping, most likely due to a social familiarization resulting from repeated interactions with the same individuals. In the second study period similarity in age influenced the strength of associations among ewes. We found no significant influence of reproductive status, weight (as an indicator of body size) and genetic relatedness on proximity associations in either study period. Moreover, our results showed the tendency of the ewes to form social clusters varied in relation to animals’ density, and Wind Chill Index (WCI). The identification of conditions that modify the social behaviour of sheep is critically important in order to implement management and productivity strategies and our results highlight how flock social structure can change depending on environmental and social contexts.
2019
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PLoS Neglected Tropical Diseases 13(7), e0007565 (2019)Contact patterns strongly influence the dynamics of disease transmission in both human and non-human animal populations. Domestic dogs Canis familiaris are a social species and are a reservoir for several zoonotic infections, yet few studies have empirically determined contact patterns within dog populations. Using high-resolution proximity logging technology, we characterised the contact networks of free-ranging domestic dogs from two settlements (n = 108 dogs, covering >80% of the population in each settlement) in rural Chad. We used these data to simulate the transmission of an infection comparable to rabies and investigated the effects of including observed contact heterogeneities on epidemic outcomes. We found that dog contact networks displayed considerable heterogeneity, particularly in the duration of contacts and that the network had communities that were highly correlated with household membership. Simulations using observed contact networks had smaller epidemic sizes than those that assumed random mixing, demonstrating the unsuitability of homogenous mixing models in predicting epidemic outcomes. When contact heterogeneities were included in simulations, the network position of the individual initially infected had an important effect on epidemic outcomes. The risk of an epidemic occurring was best predicted by the initially infected individual’s ranked degree, while epidemic size was best predicted by the individual’s ranked eigenvector centrality. For dogs in one settlement, we found that ranked eigenvector centrality was correlated with range size. Our results demonstrate that observed heterogeneities in contacts are important for the prediction of epidemiological outcomes in free-ranging domestic dogs. We show that individuals presenting a higher risk for disease transmission can be identified by their network position and provide evidence that observable traits hold potential for informing targeted disease management strategies.
@article{10.1371/journal.pntd.0007565, author = {Wilson-Aggarwal, Jared K. AND Ozella, Laura AND Tizzoni, Michele AND Cattuto, Ciro AND Swan, George J. F. AND Moundai, Tchonfienet AND Silk, Matthew J. AND Zingeser, James A. AND McDonald, Robbie A.}, journal = {PLoS Neglected Tropical Diseases}, publisher = {Public Library of Science}, title = {High-resolution contact networks of free-ranging domestic dogs Canis familiaris and implications for transmission of infection}, year = {2019}, month = {07}, volume = {13}, url = {https://doi.org/10.1371/journal.pntd.0007565}, pages = {1-19}, number = {7}, doi = {10.1371/journal.pntd.0007565} } -
Nature Communications 10, 2485 (2019)Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.
@article{ title = {Simplicial models of social contagion}, author = {Iacopo Iacopini AND Giovanni Petri AND Alain Barrat AND Vito Latora}, journal = {Nature Communications}, volume={10}, pages={2485}, year = {2019} } -
Wellcome Open Research 4, 84 (2019)BACKGROUND: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission. DESIGN AND METHODS: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week. EXPECTED RESULTS: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.
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Journal of Medical Internet Research 21(4), e12251 (2019)BACKGROUND. Over the past several decades, naturally occurring and man-made mass casualty incidents (MCIs) have increased in frequency and number worldwide. To test the impact of such events on medical resources, simulations can provide a safe, controlled setting while replicating the chaotic environment typical of an actual disaster. A standardized method to collect and analyze data from mass casualty exercises is needed to assess preparedness and performance of the health care staff involved. OBJECTIVE. In this study, we aimed to assess the feasibility of using wearable proximity sensors to measure proximity events during an MCI simulation. In the first instance, our objective was to demonstrate how proximity sensors can collect spatial and temporal information about the interactions between medical staff and patients during an MCI exercise in a quasi-autonomous way. In addition, we assessed how the deployment of this technology could help improve future simulations by analyzing the flow of patients in the hospital. METHODS. Data were obtained and collected through the deployment of wearable proximity sensors during an MCI functional exercise. The scenario included 2 areas: the accident site and the Advanced Medical Post, and the exercise lasted 3 hours. A total of 238 participants were involved in the exercise and classified in categories according to their role: 14 medical doctors, 16 nurses, 134 victims, 47 Emergency Medical Services staff members, and 27 health care assistants and other hospital support staff. Each victim was assigned a score related to the severity of his/her injury. Each participant wore a proximity sensor, and in addition, 30 fixed devices were placed in the field hospital. RESULTS. The contact networks show a heterogeneous distribution of the cumulative time spent in proximity by the participants. We obtained contact matrices based on the cumulative time spent in proximity between the victims and rescuers. Our results showed that the time spent in proximity by the health care teams with the victims is related to the severity of the patient’s injury. The analysis of patients’ flow showed that the presence of patients in the rooms of the hospital is consistent with the triage code and diagnosis, and no obvious bottlenecks were found. CONCLUSIONS. Our study shows the feasibility of the use of wearable sensors for tracking close contacts among individuals during an MCI simulation. It represents, to our knowledge, the first example of unsupervised data collection—ie, without the need for the involvement of observers, which could compromise the realism of the exercise—of face-to-face contacts during an MCI exercise. Moreover, by permitting detailed data collection about the simulation, such as data related to the flow of patients in the hospital, such deployment provides highly relevant input for the improvement of MCI resource allocation and management.
@Article{info:doi/10.2196/12251, author = {Ozella, Laura and Gauvin, Laetitia and Carenzo, Luca and Quaggiotto, Marco and Ingrassia, Pier Luigi and Tizzoni, Michele and Panisson, Andr{\'e} and Colombo, Davide and Sapienza, Anna and Kalimeri, Kyriaki and Della Corte, Francesco and Cattuto, Ciro}, title = {Wearable Proximity Sensors for Monitoring a Mass Casualty Incident Exercise: Feasibility Study}, journal = {Journal of Medical Internet Research}, year = {2019}, month = {Apr}, day = {26}, volume = {21}, number = {4}, pages = {e12251}, issn = {1438-8871}, doi = {10.2196/12251}, url = {http://www.jmir.org/2019/4/e12251/}, } -
Nature Communications 10, 220 (2019)In many data sets, information on the structure and temporality of a system coexists with noise and non-essential elements. In networked systems for instance, some edges might be non-essential or exist only by chance. Filtering them out and extracting a set of relevant connections is a non-trivial task. Moreover, mehods put forward until now do not deal with time-resolved network data, which have become increasingly available. Here we develop a method for filtering temporal network data, by defining an adequate temporal null model that allows us to identify pairs of nodes having more interactions than expected given their activities: the significant ties. Moreover, our method can assign a significance to complex structures such as triads of simultaneous interactions, an impossible task for methods based on static representations. Our results hint at ways to represent temporal networks for use in data-driven models.
@article{ title = {The structured backbone of temporal social ties}, author = {Teruyoshi Kobayashi AND Taro Takaguchi AND Alain Barrat}, journal = {Nature Communications}, volume={10}, pages={220}, doi = {10.1038/s41467-018-08160-3}, year = {2019} }
2018
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Proceedings of CIKM 2018, October 22-26, 2018, Torino, ItalyWhen analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). We tackle this task by introducing a notion of temporal core decomposition where each core is associated with its span: we call such cores span-cores. As the total number of time intervals is quadratic in the size of the temporal domain T under analysis, the total number of span-cores is quadratic in |T| as well. Our first contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both the coreness property and the span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly compute the maximal ones without computing all span-cores. Experimentation on several real-world temporal networks confirms the efficiency and scalability of our methods. Applications on temporal networks, gathered by a proximity-sensing infrastructure recording face-to-face interactions in schools, highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics and detecting/correcting anomalies in the data.
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Phys. Rev. E 98, 012317 (2018)Recent advances in data collection have facilitated the access to time-resolved human proximity data that can conveniently be represented as temporal networks of contacts between individuals. While the structural and dynamical information revealed by this type of data is fundamental to investigate how information or diseases propagate in a population, data often suffer from incompleteness, which possibly leads to biased estimations in data-driven models. A major challenge is thus to estimate the outcome of spreading processes occurring on temporal networks built from partial information. To cope with this problem, we devise an approach based on non-negative tensor factorization, a dimensionality reduction technique from multilinear algebra. The key idea is to learn a low-dimensional representation of the temporal network built from partial information and to use it to construct a surrogate network similar to the complete original network. To test our method, we consider several human-proximity networks, on which we perform resampling experiments to simulate a loss of data. Using our approach on the resulting partial networks, we build a surrogate version of the complete network for each. We then compare the outcome of a spreading process on the complete networks (nonaltered by a loss of data) and on the surrogate networks. We observe that the epidemic sizes obtained using the surrogate networks are in good agreement with those measured on the complete networks. Finally, we propose an extension of our framework that can leverage additional data, when available, to improve the surrogate network when the data loss is particularly large.
@article{PhysRevE.98.012317, title = {Estimating the outcome of spreading processes on networks with incomplete information: A dimensionality reduction approach}, author = {Sapienza, Anna and Barrat, Alain and Cattuto, Ciro and Gauvin, Laetitia}, journal = {Phys. Rev. E}, volume = {98}, issue = {1}, pages = {012317}, numpages = {20}, year = {2018}, month = {Jul}, publisher = {American Physical Society}, doi = {10.1103/PhysRevE.98.012317}, url = {https://link.aps.org/doi/10.1103/PhysRevE.98.012317} } -
PLoS ONE 13(6), e0198733 (2018)Describing and understanding close proximity interactions between infant and family members can provide key information on transmission opportunities of respiratory infections within households. Among respiratory infections, pertussis represents a public health priority. Pertussis infection can be particularly harmful to young, unvaccinated infants and for these patients, family members represent the main sources of transmission. Here, we report on the use of wearable proximity sensors based on RFID technology to measure face-to-face proximity between family members within 16 households with infants younger than 6 months for 2–5 consecutive days of data collection. The sensors were deployed over the course of approximately 1 year, in the context of a national research project aimed at the improvement of infant pertussis prevention strategies. We investigated differences in close-range interactions between family members and we assessed whether demographic variables or feeding practices affect contact patterns between parents and infants. A total of 5,958 contact events were recorded between 55 individuals: 16 infants, 4 siblings, 31 parents and 4 grandparents. The aggregated contact networks, obtained for each household, showed a heterogeneous distribution of the cumulative time spent in proximity with the infant by family members. Contact matrices defined by age and by family role showed that most of the contacts occurred between the infant and other family members (70%), while 30% of contacts was among family members (infants excluded). Many contacts were observed between infants and adults, in particular between infant and mother, followed by father, siblings and grandparents. A larger number of contacts and longer contact durations between infant and other family members were observed in families adopting exclusive breastfeeding, compared to families in which the infant receives artificial or mixed feeding. Our results demonstrate how a high-resolution measurement of contact matrices within infants’ households is feasible using wearable proximity sensing devices. Moreover, our findings suggest the mother is responsible for the large majority of the infant’s contact pattern, thus being the main potential source of infection for a transmissible disease. As the contribution to the infants’ contact pattern by other family members is very variable, vaccination against pertussis during pregnancy is probably the best strategy to protect young, unvaccinated infants.
@article{10.1371/journal.pone.0198733, author = {Ozella, Laura AND Gesualdo, Francesco AND Tizzoni, Michele AND Rizzo, Caterina AND Pandolfi, Elisabetta AND Campagna, Ilaria AND Tozzi, Alberto Eugenio AND Cattuto, Ciro}, journal = {PLOS ONE}, publisher = {Public Library of Science}, title = {Close encounters between infants and household members measured through wearable proximity sensors}, year = {2018}, month = {06}, volume = {13}, url = {https://doi.org/10.1371/journal.pone.0198733}, pages = {1-16}, number = {6}, doi = {10.1371/journal.pone.0198733} } -
EPJ Data Science 7:11 (2018)Technological advances have led to a strong increase in the number of data collection efforts aimed at measuring co-presence of individuals at different spatial resolutions. It is however unclear how much co-presence data can inform us on actual face-to-face contacts, of particular interest to study the structure of a population in social groups or for use in data-driven models of information or epidemic spreading processes. Here, we address this issue by leveraging data sets containing high resolution face-to-face contacts as well as a coarser spatial localisation of individuals, both temporally resolved, in various contexts. The co-presence and the face-to-face contact temporal networks share a number of structural and statistical features, but the former is (by definition) much denser than the latter. We thus consider several down-sampling methods that generate surrogate contact networks from the co-presence signal and compare them with the real face-to-face data. We show that these surrogate networks reproduce some features of the real data but are only partially able to identify the most central nodes of the face-to-face network. We then address the issue of using such down-sampled co-presence data in data-driven simulations of epidemic processes, and in identifying efficient containment strategies. We show that the performance of the various sampling methods strongly varies depending on context. We discuss the consequences of our results with respect to data collection strategies and methodologies.
@Article{Genois2018, author = {G{\'e}nois, Mathieu and Barrat, Alain}, title = {Can co-location be used as a proxy for face-to-face contacts?}, journal = {EPJ Data Science}, year = {2018}, month = {May}, day = {08}, volume = {7}, number = {1}, pages = {11}, issn = {2193-1127}, doi = {10.1140/epjds/s13688-018-0140-1}, url = {https://doi.org/10.1140/epjds/s13688-018-0140-1} } -
Phys. Rev. E 97, 012313 (2018)Many progresses in the understanding of epidemic spreading models have been obtained thanks to numerous modeling efforts and analytical and numerical studies, considering host populations with very different structures and properties, including complex and temporal interaction networks. Moreover, a number of recent studies have started to go beyond the assumption of an absence of coupling between the spread of a disease and the structure of the contacts on which it unfolds. Models including awareness of the spread have been proposed, to mimic possible precautionary measures taken by individuals that decrease their risk of infection, but have mostly considered static networks. Here, we adapt such a framework to the more realistic case of temporal networks of interactions between individuals. We study the resulting model by analytical and numerical means on both simple models of temporal networks and empirical time-resolved contact data. Analytical results show that the epidemic threshold is not affected by the awareness but that the prevalence can be significantly decreased. Numerical studies on synthetic temporal networks highlight, however, the presence of very strong finite-size effects, resulting in a significant shift of the effective epidemic threshold in the presence of risk awareness. For empirical contact networks, the awareness mechanism leads as well to a shift in the effective threshold and to a strong reduction of the epidemic prevalence.
@article{PhysRevE.97.012313, title = {Effect of risk perception on epidemic spreading in temporal networks}, author = {Moinet, Antoine and Pastor-Satorras, Romualdo and Barrat, Alain}, journal = {Phys. Rev. E}, volume = {97}, issue = {1}, pages = {012313}, numpages = {11}, year = {2018}, month = {Jan}, publisher = {American Physical Society}, doi = {10.1103/PhysRevE.97.012313}, url = {https://link.aps.org/doi/10.1103/PhysRevE.97.012313} }
2017
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Scientific Reports 7, 9975 (2017)Many datasets describing contacts in a population suffer from incompleteness due to population sampling and underreporting of contacts. Data-driven simulations of spreading processes using such incomplete data lead to an underestimation of the epidemic risk, and it is therefore important to devise methods to correct this bias. We focus here on a non-uniform sampling of the contacts between individuals, aimed at mimicking the results of diaries or surveys, and consider as case studies two datasets collected in different contexts. We show that using surrogate data built using a method developed in the case of uniform population sampling yields an improvement with respect to the use of the sampled data but is strongly limited by the underestimation of the link density in the sampled network. We put forward a second method to build surrogate data that assumes knowledge of the density of links within one of the groups forming the population. We show that it gives very good results when the population is strongly structured, and discuss its limitations in the case of a population with a weaker group structure. These limitations highlight the interest of measurements using wearable sensors able to yield accurate information on the structure and durations of contacts.
@article{Fournet:2017, author={Fournet, Julie and Barrat, Alain}, title={Estimating the epidemic risk using non-uniformly sampled contact data}, journal={Scientific Reports}, volume={7}, pages={9975} } -
Proceedings of the 9th International Conference on Social Informatics (SocInfo 2017), Oxford, UK, September 13-15 2017, LNCS 10539, pp. 536-551 (2017)The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks.
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Proceedings of the 12th International Conference on Computer Supported Collaborative Learning (CSCL), Philadelphia, USA, June 18-22, 2017, p. 519-526 (2017)Integrating newcomers and fostering collaboration between researchers with different disciplinary backgrounds is a challenge for scientific communities. Prior research suggests that both network-driven selection patterns (reciprocity and transitivity) and the active selection of specific others are important. Selecting appropriate collaboration partners may moreover require what we call cognitive group awareness, (i.e. knowledge about the knowledge of others). In a field study at two multi-disciplinary scientific events (Alpine Rendez-Vous 2011 and 2013) including N=287 researchers, we investigated selection patterns, looking specifically at career level and disciplinary background, and included a cognitive group awareness intervention. While we could not completely explain how researchers choose with whom they interact, we found that transitivity and interaction duration are relevant for later collaboration. Cognitive group awareness support was beneficial for fostering interdisciplinary collaboration. Career level was a less relevant factor. We discuss measures for supporting newcomer integration and community buildings based on our findings.
2016
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BMC Infectious Diseases 16, 676 (2016)BACKGROUND. The homogeneous mixing assumption is widely adopted in epidemic modelling for its parsimony and represents the building block of more complex approaches, including very detailed agent-based models. The latter assume homogeneous mixing within schools, workplaces and households, mostly for the lack of detailed information on human contact behaviour within these settings. The recent data availability on high-resolution face-to-face interactions makes it now possible to assess the goodness of this simplified scheme in reproducing relevant aspects of the infection dynamics. METHODS. We consider empirical contact networks gathered in different contexts, as well as synthetic data obtained through realistic models of contacts in structured populations. We perform stochastic spreading simulations on these contact networks and in populations of the same size under a homogeneous mixing hypothesis. We adjust the epidemiological parameters of the latter in order to fit the prevalence curve of the contact epidemic model. We quantify the agreement by comparing epidemic peak times, peak values, and epidemic sizes. RESULTS. Good approximations of the peak times and peak values are obtained with the homogeneous mixing approach, with a median relative difference smaller than 20 % in all cases investigated. Accuracy in reproducing the peak time depends on the setting under study, while for the peak value it is independent of the setting. Recalibration is found to be linear in the epidemic parameters used in the contact data simulations, showing changes across empirical settings but robustness across groups and population sizes. CONCLUSIONS. An adequate rescaling of the epidemiological parameters can yield a good agreement between the epidemic curves obtained with a real contact network and a homogeneous mixing approach in a population of the same size. The use of such recalibrated homogeneous mixing approximations would enhance the accuracy and realism of agent-based simulations and limit the intrinsic biases of the homogeneous mixing.
@Article{Bioglio2016, author = {Bioglio, Livio and G{\'e}nois, Mathieu and Vestergaard, Christian L. and Poletto, Chiara and Barrat, Alain and Colizza, Vittoria}, title = {Recalibrating disease parameters for increasing realism in modeling epidemics in closed settings}, journal = {BMC Infectious Diseases}, volume = {16}, number = {1}, pages = {676}, year = {2016}, issn = {1471-2334}, doi = {10.1186/s12879-016-2003-3}, url = {http://dx.doi.org/10.1186/s12879-016-2003-3} } -
BMC Infectious Diseases 16, 576 (2016)BACKGROUND. Nearly every year Influenza affects most countries worldwide and the risk of a new pandemic is always present. Therefore, influenza is a major concern for public health. School-age individuals are often the most affected group, suggesting that the inclusion in preparedness plans of school closure policies may represent an option for influenza mitigation. However, their applicability remains uncertain and their implementation should carefully be weighed on the basis of cost-benefit considerations. METHODS. We developed an individual-based model for influenza transmission integrating data on sociodemography and time use of the Italian population, face-to-face contacts in schools, and influenza natural history. The model was calibrated on the basis of epidemiological data from the 2009 influenza pandemic and was used to evaluate the effectiveness of three reactive school closure strategies, all based on school absenteeism. RESULTS. In the case of a new influenza pandemic sharing similar features with the 2009 H1N1 pandemic, gradual school closure strategies (i.e., strategies closing classes first, then grades or the entire school) could lead to attack rate reduction up to 20–25 % and to peak weekly incidence reduction up to 50–55 %, at the cost of about three school weeks lost per student. Gradual strategies are quite stable to variations in the start of policy application and to the threshold on student absenteeism triggering class (and school) closures. In the case of a new influenza pandemic showing different characteristics with respect to the 2009 H1N1 pandemic, we found that the most critical features determining the effectiveness of school closure policies are the reproduction number and the age-specific susceptibility to infection, suggesting that these two epidemiological quantities should be estimated early on in the spread of a new pandemic for properly informing response planners. CONCLUSIONS. Our results highlight a potential beneficial effect of reactive gradual school closure policies in mitigating influenza spread, conditioned on the effort that decision makers are willing to afford. Moreover, the suggested strategies are solely based on routinely collected and easily accessible data (such as student absenteeism irrespective of the cause and ILI incidence) and thus they appear to be applicable in real world situations.
@Article{Ciavarella2016, author = {Ciavarella, Constanze and Fumanelli, Laura and Merler, Stefano and Cattuto, Ciro and Ajelli, Marco}, title = {School closure policies at municipality level for mitigating influenza spread: a model-based evaluation}, journal = {BMC Infectious Diseases}, year = {2016}, volume = {16}, number = {1}, pages = {576}, abstract = {Nearly every year Influenza affects most countries worldwide and the risk of a new pandemic is always present. Therefore, influenza is a major concern for public health. School-age individuals are often the most affected group, suggesting that the inclusion in preparedness plans of school closure policies may represent an option for influenza mitigation. However, their applicability remains uncertain and their implementation should carefully be weighed on the basis of cost-benefit considerations.}, issn = {1471-2334}, doi = {10.1186/s12879-016-1918-z}, url = {http://dx.doi.org/10.1186/s12879-016-1918-z} } -
BMC Infectious Diseases 16, 341 (2016)Background Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement – paper diaries vs. wearable proximity sensors – that were applied concurrently to the same population, and we measured acceptability. Methods We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability. Results There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for 60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants’ aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors. Conclusion Reporting and recording were not complete; reporting was particularly incomplete for contacts
@Article{Smieszek2016, author = {Smieszek, Timo and Castell, Stefanie and Barrat, Alain and Cattuto, Ciro and White, Peter J. and Krause, G{\'e}rard}, title = {Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes}, journal = {BMC Infectious Diseases}, volume = {16}, number = {1}, pages = {1--14}, year = {2016}, issn = {1471-2334}, doi = {10.1186/s12879-016-1676-y}, url = {http://dx.doi.org/10.1186/s12879-016-1676-y} } -
European Journal of Applied Mathematics 27, 941 (2016)The ability to directly record human face-to-face interactions increasingly enables the development of detailed data-driven models for the spread of directly transmitted infectious diseases at the scale of individuals. Complete coverage of the contacts occurring in a population is however generally unattainable, due for instance to limited participation rates or experimental constraints in spatial coverage. Here, we study the impact of spatially constrained sampling on our ability to estimate the epidemic risk in a population using such detailed data-driven models. The epidemic risk is quantified by the epidemic threshold of the SIRS model for the propagation of communicable diseases, i.e. the critical value of disease transmissibility above which the disease turns endemic. We verify for both synthetic and empirical data of human interactions that the use of incomplete data sets due to spatial sampling leads to the underestimation of the epidemic risk. The bias is however smaller than the one obtained by uniformly sampling the same fraction of contacts: it depends non-linearly on the fraction of contacts that are recorded, and becomes negligible if this fraction is large enough. Moreover, it depends on the interplay between the timescales of population and spreading dynamics.
@article{EJM:10391502, author = {VESTERGAARD,CHRISTIAN L. and VALDANO,EUGENIO and GÉNOIS,MATHIEU and POLETTO,CHIARA and COLIZZA,VITTORIA and BARRAT,ALAIN}, title = {Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk}, journal = {European Journal of Applied Mathematics}, volume = {27}, year = {2016}, issn = {1469-4425}, pages = {941--957}, numpages = {17}, doi = {10.1017/S0956792516000309}, URL = {https://www.cambridge.org/core/journals/european-journal-of-applied-mathematics/article/impact-of-spatially-constrained-sampling-of-temporal-contact-networks-on-the-evaluation-of-the-epidemic-risk/D9615D2D225FFF04679EDA5064D4141E}, } -
PLoS Computational Biology 12(6), e1005002 (2016)Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.
@article{10.1371/journal.pcbi.1005002, author = {Mastrandrea, Rossana AND Barrat, Alain}, journal = {PLoS Computational Biology}, publisher = {Public Library of Science}, title = {How to Estimate Epidemic Risk from Incomplete Contact Diaries Data?}, year = {2016}, month = {06}, volume = {12}, pages = {1-19}, number = {6}, doi = {10.1371/journal.pcbi.1005002} } -
EPJ Data Science 5:21 (2016)Close proximity interactions between individuals influence how infections spread. Quantifying close contacts in developing world settings, where such data is sparse yet disease burden is high, can provide insights into the design of intervention strategies such as vaccination. Recent technological advances have enabled collection of time-resolved face-to-face human contact data using radio frequency proximity sensors. The acceptability and practicalities of using proximity devices within the developing country setting have not been investigated. We present and analyse data arising from a prospective study of 5 households in rural Kenya, followed through 3 consecutive days. Pre-study focus group discussions with key community groups were held. All residents of selected households carried wearable proximity sensors to collect data on their close (<1.5 metres) interactions. Data collection for residents of three of the 5 households was contemporaneous. Contact matrices and temporal networks for 75 individuals are defined and mixing patterns by age and time of day in household contacts determined. Our study demonstrates the stability of numbers and durations of contacts across days. The contact durations followed a broad distribution consistent with data from other settings. Contacts within households occur mainly among children and between children and adults, and are characterised by daily regular peaks in the morning, midday and evening. Inter-household contacts are between adults and more sporadic when measured over several days. Community feedback indicated privacy as a major concern especially regarding perceptions of non-participants, and that community acceptability required thorough explanation of study tools and procedures. Our results show for a low resource setting how wearable proximity sensors can be used to objectively collect high-resolution temporal data without direct supervision. The methodology appears acceptable in this population following adequate community engagement on study procedures. A target for future investigation is to determine the difference in contact networks within versus between households. We suggest that the results from this study may be used in the design of future studies using similar electronic devices targeting communities, including households and schools, in the developing world context.
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Scientific Reports 6:24593 (2016)Contacts between individuals play an important role in determining how infectious diseases spread. Various methods to gather data on such contacts co-exist, from surveys to wearable sensors. Comparisons of data obtained by different methods in the same context are however scarce, in particular with respect to their use in data-driven models of spreading processes. Here, we use a combined data set describing contacts registered by sensors and friendship relations in the same population to address this issue in a case study. We investigate if the use of the friendship network is equivalent to a sampling procedure performed on the sensor contact network with respect to the outcome of simulations of spreading processes: such an equivalence might indeed give hints on ways to compensate for the incompleteness of contact data deduced from surveys. We show that this is indeed the case for these data, for a specifically designed sampling procedure, in which respondents report their neighbors with a probability depending on their contact time. We study the impact of this specific sampling procedure on several data sets, discuss limitations of our approach and its possible applications in the use of data sets of various origins in data-driven simulations of epidemic processes.
@Article{fournet2016scirep, AUTHOR = {Fournet, Julie and Barrat, Alain}, TITLE = {Epidemic risk from friendship network data: an equivalence with a non-uniform sampling of contact networks}, JOURNAL = {Scientific Reports}, VOLUME = {6}, YEAR = {2016}, PAGES = {24593}, URL = {http://www.nature.com/articles/srep24593}, DOI = {doi:10.1038/srep24593}, }
2015
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Nature Communications 6, 8860 (2015)Data describing human interactions often suffer from incomplete sampling of the underlying population. As a consequence, the study of contagion processes using data-driven models can lead to a severe underestimation of the epidemic risk. Here we present a systematic method to alleviate this issue and obtain a better estimation of the risk in the context of epidemic models informed by high-resolution time-resolved contact data. We consider several such data sets collected in various contexts and perform controlled resampling experiments. We show how the statistical information contained in the resampled data can be used to build a series of surrogate versions of the unknown contacts. We simulate epidemic processes on the resulting reconstructed data sets and show that it is possible to obtain good estimates of the outcome of simulations performed using the complete data set. We discuss limitations and potential improvements of our method.
@Article{ncomms9860, AUTHOR = {G{\'e}nois, Mathieu and Vestergaard, Christian and Cattuto, Ciro and Barrat, Alain}, TITLE = {Compensating for population sampling in simulations of epidemic spread on temporal contact networks}, JOURNAL = {Nature Communications}, VOLUME = {8}, PAGES = {8860}, YEAR = {2015}, URL = {http://www.nature.com/ncomms/2015/151113/ncomms9860/full/ncomms9860.html}, DOI = {10.1038/ncomms9860} } -
BMC Research Notes 2015, 8:426 (2015)BACKGROUND. Hand-hygiene compliance and contacts of health-care workers largely determine the potential paths of pathogen transmission in hospital wards. We explored how the combination of data collected by two automated infrastructures based on wearable sensors and recording (1) use of hydro-alcoholic solution and (2) contacts of health-care workers provide an enhanced view of the risk of transmission events in the ward. METHODS. We perform a proof-of-concept observational study. Detailed data on contact patterns and hand-hygiene compliance of health-care workers were collected by wearable sensors over 12 days in an infectious disease unit of a hospital in Marseilles, France. RESULTS. 10,837 contact events among 10 doctors, 4 nurses, 4 nurses’ aids and 4 housekeeping staff were recorded during the study. Most contacts took place among medical doctors. Aggregate contact durations were highly heterogeneous and the resulting contact network was highly structured. 510 visits of health-care workers to patients’ rooms were recorded, with a low rate of hand-hygiene compliance. Both data sets were used to construct histories and statistics of contacts informed by the use of hydro-alcoholic solution, or lack thereof, of the involved health-care workers. CONCLUSIONS. Hand-hygiene compliance data strongly enrich the information concerning contacts among health-care workers, by assigning a ‘safe’ or ‘at-risk’ value to each contact. The global contact network can thus be divided into ‘at-risk’ and ‘safe’ contact networks. The combined data could be of high relevance for outbreak investigation and to inform data-driven models of nosocomial disease spread.
@Article{mastrandrea2015bmc, AUTHOR = {Mastrandrea, Rossana and Soto-Aladro, Alberto and Brouqui, Philippe and Barrat, Alain}, TITLE = {Enhancing the evaluation of pathogen transmission risk in a hospital by merging hand-hygiene compliance and contact data: a proof-of-concept study}, JOURNAL = {BMC Research Notes}, VOLUME = {8}, YEAR = {2015}, NUMBER = {1}, PAGES = {426}, URL = {http://www.biomedcentral.com/1756-0500/8/426}, DOI = {10.1186/s13104-015-1409-0}, ISSN = {1756-0500}, } -
Network Science 3, 326 (2015)Empirical data on contacts between individuals in social contexts play an important role in providing information for models describing human behavior and how epidemics spread in populations. Here, we analyze data on face-to-face contacts collected in an office building. The statistical properties of contacts are similar to other social situations, but important differences are observed in the contact network structure. In particular, the contact network is strongly shaped by the organization of the offices in departments, which has consequences in the design of accurate agent-based models of epidemic spread. We consider the contact network as a potential substrate for infectious disease spread and show that its sparsity tends to prevent outbreaks of rapidly spreading epidemics. Moreover, we define three typical behaviors according to the fraction f of links each individual shares outside its own department: residents, wanderers and linkers. Linkers (f∼50%) act as bridges in the network and have large betweenness centralities. Thus, a vaccination strategy targeting linkers efficiently prevents large outbreaks. As such a behavior may be spotted a priori in the offices' organization or from surveys, without the full knowledge of the time-resolved contact network, this result may help the design of efficient, low-cost vaccination or social-distancing strategies.
@article{NWS:9950811, author = {GÉNOIS,MATHIEU and VESTERGAARD,CHRISTIAN L. and FOURNET,JULIE and PANISSON,ANDRÉ and BONMARIN,ISABELLE and BARRAT,ALAIN}, title = {Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers}, journal = {Network Science}, volume = {3}, issue = {03}, month = {9}, year = {2015}, issn = {2050-1250}, pages = {326--347}, numpages = {22}, doi = {10.1017/nws.2015.10}, URL = {http://journals.cambridge.org/article_S2050124215000107}, } -
PLoS ONE 10(9), e0136497 (2015)Given their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i) face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii) self-reported friendship surveys, and (iii) online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self-reported contacts and of friendships, and we investigate the correlations between the number of neighbors of individuals in the three networks. Overall, diaries and surveys tend to yield a correct picture of the global structural organization of the contact network, albeit with much less links, and give access to a sort of backbone of the contact network corresponding to the strongest links, i.e., the contacts of longest cumulative durations.
@article{10.1371/journal.pone.0136497, author = {Mastrandrea, Rossana and Fournet, Julie and Barrat, Alain}, title = {Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys}, journal = {PLoS ONE}, volume = {10}, number = {9}, pages = {e0136497}, year = {2015}, month = {09}, publisher = {Public Library of Science}, url = {http://dx.doi.org/10.1371/journal.pone.0136497}, doi = {10.1371/journal.pone.0136497} } -
Proceedings of the 2015 ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, August 25-28 2015.Today, many people spend a lot of time online. Their social interactions captured in online social networks are an important part of the overall personal social profile, in addition to interactions taking place offline. This paper investigates whether relations captured by online social networks can be used as a proxy for the relations in offline social networks, such as networks of human face-to-face (F2F) proximity and coauthorship networks. Particularly, the paper focuses on interactions of computer scientists in online settings (homepages, social networks profiles and connections) and offline settings (scientific collaboration, face-to-face communications during the conferences). We focus on quantitative studies: we investigate the structural similarities and correlations of the induced networks; in addition, we analyze implications between networks. Finally, we provide a qualitative user analysis to find characteristics of good and bad proxies.
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Infection Control and Hospital Epidemiology 36, 254 (2015)OBJECTIVE. Contact patterns and microbiological data contribute to a detailed understanding of infectious disease transmission. We explored the automated collection of high-resolution contact data by wearable sensors combined with virological data to investigate influenza transmission among patients and healthcare workers in a geriatric unit. DESIGN. Proof-of-concept observational study. Detailed information on contact patterns were collected by wearable sensors over 12 days. Systematic nasopharyngeal swabs were taken, analyzed for influenza A and B viruses by real-time polymerase chain reaction, and cultured for phylogenetic analysis. SETTING. An acute-care geriatric unit in a tertiary care hospital. PARTICIPANTS. Patients, nurses, and medical doctors. RESULTS. A total of 18,765 contacts were recorded among 37 patients, 32 nurses, and 15 medical doctors. Most contacts occurred between nurses or between a nurse and a patient. Fifteen individuals had influenza A (H3N2). Among these, 11 study participants were positive at the beginning of the study or at admission, and 3 patients and 1 nurse acquired laboratory-confirmed influenza during the study. Infectious medical doctors and nurses were identified as potential sources of hospital-acquired influenza (HA-Flu) for patients, and infectious patients were identified as likely sources for nurses. Only 1 potential transmission between nurses was observed. CONCLUSIONS. Combining high-resolution contact data and virological data allowed us to identify a potential transmission route in each possible case of HA-Flu. This promising method should be applied for longer periods in larger populations, with more complete use of phylogenetic analyses, for a better understanding of influenza transmission dynamics in a hospital setting.
@article{ICE:9512001, author = {Voirin, Nicolas and Payet, C{\'e}cile and Barrat, Alain and Cattuto, Ciro and Khanafer, Nagham and R{\'e}gis, Corinne and Kim, Byeul-a and Comte, Brigitte and Casalegno, Jean-S{\'e}bastien and Lina, Bruno and Vanhems, Philippe}, title = {Combining High-Resolution Contact Data with Virological Data to Investigate Influenza Transmission in a Tertiary Care Hospital}, journal = {Infection Control \& Hospital Epidemiology}, volume = {36}, pages = {254}, year = {2015}, doi = {10.1017/ice.2014.53}, url = {http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9512001&fileId=S0899823X14000531} } -
Social Science & Medicine 125, 40 (2015)How are social interaction dynamics associated with mental health during early stages of adolescence? The goal of this study is to objectively measure social interactions and evaluate the roles that multiple aspects of the social environment – such as physical activity and food choice – may jointly play in shaping the structure of children's relationships and their mental health. The data in this study are drawn from a longitudinal network-behavior study conducted in 2012 at a private K-8 school in an urban setting in California. We recruited a highly complete network sample of sixth-graders (n = 40, 91% of grade, mean age = 12.3), and examined how two measures of distressed mental health (self-esteem and depressive symptoms) are positionally distributed in an early adolescent interaction network. We ascertained how distressed mental health shapes the structure of relationships over a three-month period, adjusting for relevant dimensions of the social environment. Cross-sectional analyses of interaction networks revealed that self-esteem and depressive symptoms are differentially stratified by gender. Specifically, girls with more depressive symptoms have interactions consistent with social inhibition, while boys' interactions suggest robustness to depressive symptoms. Girls higher in self-esteem tended towards greater sociability. Longitudinal network behavior models indicate that gender similarity and perceived popularity are influential in the formation of social ties. Greater school connectedness predicts the development of self-esteem, though social ties contribute to more self-esteem improvement among students who identify as European-American. Cross-sectional evidence shows associations between distressed mental health and students' network peers. However, there is no evidence that connected students' mental health status becomes more similar in their over time because of their network interactions. These findings suggest that mental health during early adolescence may be less subject to mechanisms of social influence than network research in even slightly older adolescents currently indicates.
@article{Pachucki201540, title = {Mental health and social networks in early adolescence: A dynamic study of objectively-measured social interaction behaviors}, journal = {Social Science & Medicine}, volume = {125}, number = {0}, pages = {40 - 50}, year = {2015}, note = {Special Issue: Social Networks, Health and Mental Health}, issn = {0277-9536}, doi = {http://dx.doi.org/10.1016/j.socscimed.2014.04.015}, url = {http://www.sciencedirect.com/science/article/pii/S0277953614002391}, author = {Mark C. Pachucki and Emily J. Ozer and Alain Barrat and Ciro Cattuto}, keywords = {Social networks}, keywords = {Self-esteem}, keywords = {Depression}, keywords = {Early adolescence}, keywords = {Stochastic actor-based modeling} }
2014
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BMC Infectious Diseases 14, 695 (2014)School environments are thought to play an important role in the community spread of infectious diseases such as influenza because of the high mixing rates of school children. The closure of schools has therefore been proposed as an efficient mitigation strategy. Such measures come however with high associated social and economic costs, making alternative, less disruptive interventions highly desirable. The recent availability of high-resolution contact network data from school environments provides an opportunity to design models of micro-interventions and compare the outcomes of alternative mitigation measures. Methods and results: We model mitigation measures that involve the targeted closure of school classes or grades based on readily available information such as the number of symptomatic infectious children in a class. We focus on the specific case of a primary school for which we have high-resolution data on the close-range interactions of children and teachers. We simulate the spread of an influenza-like illness in this population by using an SEIR model with asymptomatics, and compare the outcomes of different mitigation strategies. We find that targeted class closure affords strong mitigation effects: closing a class for a fixed period of time - equal to the sum of the average infectious and latent durations - whenever two infectious individuals are detected in that class decreases the attack rate by almost 70% and significantly decreases the probability of a severe outbreak. The closure of all classes of the same grade mitigates the spread almost as much as closing the whole school. Conclusions: Our model of targeted class closure strategies based on readily available information on symptomatic subjects and on limited information on mixing patterns, such as the grade structure of the school, show that these strategies might be almost as effective as whole-school closure, at a much lower cost. This may inform public health policies for the management and mitigation of influenza-like outbreaks in the community.
@article{Gemmetto2014, author = {Gemmetto, Valerio and Barrat, Alain and Cattuto, Ciro}, doi = {10.1186/PREACCEPT-6851518521414365}, issn = {1471-2334}, journal = {BMC infectious diseases}, month = {dec}, number = {1}, pages = {695}, pmid = {25551363}, publisher = {BioMed Central Ltd}, title = {Mitigation of infectious disease at school: targeted class closure vs school closure.}, url = {http://www.biomedcentral.com/1471-2334/14/3841}, volume = {14}, year = {2014} } -
Phys. Rev. E 90, 042805 (2014)Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading. Despite the recent wealth of data on temporal networks, little work has been devoted to the understanding of how such heterogeneities can emerge from microscopic mechanisms at the level of nodes and links. Here we show that long-term memory effects are present in the creation and disappearance of links in empirical networks. We thus consider a simple generative modeling framework for temporal networks able to incorporate these memory mechanisms. This allows us to study separately the role of each of these mechanisms in the emergence of heterogeneous network dynamics. In particular, we show analytically and numerically how heterogeneous distributions of contact durations, of intercontact durations, and of numbers of contacts per link emerge. We also study the individual effect of heterogeneities on dynamical processes, such as the paradigmatic susceptible-infected epidemic spreading model. Our results confirm in particular the crucial role of the distributions of intercontact durations and of the numbers of contacts per link.
@article{PhysRevE.90.042805, title = {How memory generates heterogeneous dynamics in temporal networks}, author = {Vestergaard, Christian L. and G'enois, Mathieu and Barrat, Alain}, journal = {Phys. Rev. E}, volume = {90}, issue = {4}, pages = {042805}, numpages = {11}, year = {2014}, month = {Oct}, publisher = {American Physical Society}, doi = {10.1103/PhysRevE.90.042805}, url = {http://link.aps.org/doi/10.1103/PhysRevE.90.042805} } -
PLoS ONE 9(9), e107878 (2014)Face-to-face contacts between individuals contribute to shape social networks and play an important role in determining how infectious diseases can spread within a population. It is thus important to obtain accurate and reliable descriptions of human contact patterns occurring in various day-to-day life contexts. Recent technological advances and the development of wearable sensors able to sense proximity patterns have made it possible to gather data giving access to time-varying contact networks of individuals in specific environments. Here we present and analyze two such data sets describing with high temporal resolution the contact patterns of students in a high school. We define contact matrices describing the contact patterns between students of different classes and show the importance of the class structure. We take advantage of the fact that the two data sets were collected in the same setting during several days in two successive years to perform a longitudinal analysis on two very different timescales. We show the high stability of the contact patterns across days and across years: the statistical distributions of numbers and durations of contacts are the same in different periods, and we observe a very high similarity of the contact matrices measured in different days or different years. The rate of change of the contacts of each individual from one day to the next is also similar in different years. We discuss the interest of the present analysis and data sets for various fields, including in social sciences in order to better understand and model human behavior and interactions in different contexts, and in epidemiology in order to inform models describing the spread of infectious diseases and design targeted containment strategies.
@article{10.1371/journal.pone.0107878, author = {Fournet, Julie and Barrat, Alain}, title = {Contact Patterns among High School Students}, journal = {PLoS ONE}, volume = {9}, number = {9}, pages = {e107878}, year = {2014}, month = {09}, publisher = {Public Library of Science}, url = {http://dx.doi.org/10.1371/journal.pone.0107878}, doi = {10.1371/journal.pone.0107878} } -
PLoS ONE 9(1), e86028 (2014)The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule.
@article{10.1371/journal.pone.0086028, author = {Gauvin, Laetitia and Panisson, Andr{\'e} and Cattuto, Ciro}, title = {Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach}, journal = {PLoS ONE}, volume = {9}, number = {1}, pages = {e86028}, year = {2014}, month = {01}, publisher = {Public Library of Science}, url = {http://dx.doi.org/10.1371/journal.pone.0086028}, doi = {10.1371/journal.pone.0086028} }
2013
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Clinical Microbiology and Infection 20, 10–16 (2014)Thanks to recent technological advances, measuring real-world interactions by the use of mobile devices and wearable sensors has become possible, allowing researchers to gather data on human social interactions in a variety of contexts with high spatial and temporal resolution. Empirical data describing contact networks have thus acquired a high level of detail that may yield new insights into the dynamics of infection transmission between individuals. At the same time, such data bring forth new challenges related to their statistical description and analysis, and to their use in mathematical models. In particular, the integration of highly detailed empirical data in computational frameworks designed to model the spread of infectious diseases raises the issue of assessing which representations of the raw data work best to inform the models. There is an emerging need to strike a balance between simplicity and detail in order to ensure both generalizability and accuracy of predictions. Here, we review recent work on the collection and analysis of highly detailed data on temporal networks of face-to-face human proximity, carried out in the context of the SocioPatterns collaboration. We discuss the various levels of coarse-graining that can be used to represent the data in order to inform models of infectious disease transmission. We also discuss several limitations of the data and future avenues for data collection and modelling efforts in the field of infectious diseases.
@article {CLM:CLM12472, author = {Barrat, A. and Cattuto, C. and Tozzi, A. E. and Vanhems, P. and Voirin, N.}, title = {Measuring contact patterns with wearable sensors: methods, data characteristics and applications to data-driven simulations of infectious diseases}, journal = {Clinical Microbiology and Infection}, volume = {20}, number = {1}, issn = {1469-0691}, url = {http://dx.doi.org/10.1111/1469-0691.12472}, doi = {10.1111/1469-0691.12472}, pages = {10--16}, keywords = {Contact patterns, data-driven, infectious diseases, modelling, numerical simulations, wearable sensors}, year = {2014}, } -
Phys. Rev. E 88, 052812 (2013)The increasing availability of time- and space-resolved data describing human activities and interactions gives insights into both static and dynamic properties of human behavior. In practice, nevertheless, real-world data sets can often be considered as only one realization of a particular event. This highlights a key issue in social network analysis: the statistical significance of estimated properties. In this context, we focus here on the assessment of quantitative features of specific subset of nodes in empirical networks. We present a method of statistical resampling based on bootstrapping groups of nodes under constraints within the empirical network. The method enables us to define acceptance intervals for various null hypotheses concerning relevant properties of the subset of nodes under consideration in order to characterize by a statistical test its behavior as “normal” or not. We apply this method to a high-resolution data set describing the face-to-face proximity of individuals during two colocated scientific conferences. As a case study, we show how to probe whether colocating the two conferences succeeded in bringing together the two corresponding groups of scientists.
@article{PhysRevE.88.052812, title = {Bootstrapping under constraint for the assessment of group behavior in human contact networks}, author = {Tremblay, Nicolas and Barrat, Alain and Forest, Cary and Nornberg, Mark and Pinton, Jean-Fran{\c c}ois and Borgnat, Pierre}, journal = {Phys. Rev. E}, volume = {88}, number = {5}, pages = {052812}, numpages = {15}, year = {2013}, month = {Nov}, doi = {10.1103/PhysRevE.88.052812}, url = {http://link.aps.org/doi/10.1103/PhysRevE.88.052812}, publisher = {American Physical Society} } -
Journal of Theoretical Biology 337, 89-100 (2013)Spreading processes represent a very efficient tool to investigate the structural properties of networks and the relative importance of their constituents, and have been widely used to this aim in static networks. Here we consider simple disease spreading processes on empirical time-varying networks of contacts between individuals, and compare the effect of several immunization strategies on these processes. An immunization strategy is defined as the choice of a set of nodes (individuals) who cannot catch nor transmit the disease. This choice is performed according to a certain ranking of the nodes of the contact network. We consider various ranking strategies, focusing in particular on the role of the training window during which the nodes’ properties are measured in the time-varying network: longer training windows correspond to a larger amount of information collected and could be expected to result in better performances of the immunization strategies. We find instead an unexpected saturation in the efficiency of strategies based on nodes’ characteristics when the length of the training window is increased, showing that a limited amount of information on the contact patterns is sufficient to design efficient immunization strategies. This finding is balanced by the large variations of the contact patterns, which strongly alter the importance of nodes from one period to the next and therefore significantly limit the efficiency of any strategy based on an importance ranking of nodes. We also observe that the efficiency of strategies that include an element of randomness and are based on temporally local information do not perform as well but are largely independent on the amount of information available.
@article{Starnini2013, title = {Immunization strategies for epidemic processes in time-varying contact networks}, journal = {Journal of Theoretical Biology}, volume = {337}, pages = {89--100}, year = {2013}, issn = {0022-5193}, doi = {10.1016/j.jtbi.2013.07.004}, url = {https://www.sciencedirect.com/science/article/pii/S0022519313003251}, author = {Michele Starnini and Anna Machens and Ciro Cattuto and Alain Barrat and Romualdo Pastor-Satorras}, } -
Sci. Rep. 3: 3099 (2013)Dynamical processes on time-varying complex networks are key to understanding and modeling a broad variety of processes in socio-technical systems. Here we focus on empirical temporal networks of human proximity and we aim at understanding the factors that, in simulation, shape the arrival time distribution of simple spreading processes. Abandoning the notion of wall-clock time in favour of node-specific clocks based on activity exposes robust statistical patterns in the arrival times across different social contexts. Using randomization strategies and generative models constrained by data, we show that these patterns can be understood in terms of heterogeneous inter-event time distributions coupled with heterogeneous numbers of events per edge. We also show, both empirically and by using a synthetic dataset, that significant deviations from the above behavior can be caused by the presence of edge classes with strong activity correlations.
@article{Gauvin2013: author = {L. Gauvin and A. Panisson and C. Cattuto and A. Barrat}, title = {Activity clocks: spreading dynamics on temporal networks of human contact}, journal = {Scientific Reports}, volume = {3}, pages = {3099}, year = {2013} } -
Social Networks 35(4):604-613 (2013)We investigate gender homophily in the spatial proximity of children (6–12 years old) in a French primary school, using time-resolved data on face-to-face proximity recorded by means of wearable sensors. For strong ties, i.e., for pairs of children who interact more than a defined threshold, we find statistical evidence of gender preference that increases with grade. For weak ties, conversely, gender homophily is negatively correlated with grade for girls, and positively correlated with grade for boys. This different evolution with grade of weak and strong ties exposes a contrasted picture of gender homophily.
@article{Stehlé2013604, title = {Gender homophily from spatial behavior in a primary school: A sociometric study}, journal = {Social Networks}, volume = {35}, number = {4}, pages = {604 - 613}, year = {2013}, note = {}, issn = {0378-8733}, doi = {http://dx.doi.org/10.1016/j.socnet.2013.08.003}, url = {http://www.sciencedirect.com/science/article/pii/S0378873313000737}, author = {Juliette Stehlé and François Charbonnier and Tristan Picard and Ciro Cattuto and Alain Barrat}, } -
PLoS ONE 8(9), e73970 (2013)BACKGROUND. Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. METHODS AND FINDINGS. We used wearable sensors to detect close-range interactions (“contacts”) between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. CONCLUSIONS. Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.
@article{10.1371/journal.pone.0073970, author = {Vanhems, Philippe and Barrat, Alain and Cattuto, Ciro and Pinton, Jean-Fran{\c c}ois and Khanafer, Nagham and R{\'e}gis, Corinne and Kim, Byeul-a and Comte, Brigitte and Voirin, Nicolas}, title = {Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors}, journal = {PLoS ONE}, volume = {8}, number = {9}, pages = {e73970}, year = {2013}, month = {09}, publisher = {Public Library of Science}, url = {http://dx.doi.org/10.1371/journal.pone.0073970}, doi = {10.1371/journal.pone.0073970} } -
Eur. Phys. J. Special Topics 222, 1295 (2013)The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented level of details and scale. Wearable sensors, in particular, open up a new window on human mobility and proximity in a variety of indoor environments. Here we review stylized facts on the structural and dynamical properties of empirical networks of human face-to-face proximity, measured in three different real-world contexts: an academic conference, a hospital ward, and a museum exhibition. First, we discuss the structure of the aggregated contact networks, that project out the detailed ordering of contact events while preserving temporal heterogeneities in their weights. We show that the structural properties of aggregated networks highlight important differences and unexpected similarities across contexts, and discuss the additional complexity that arises from attributes that are typically associated with nodes in real-world interaction networks, such as role classes in hospitals. We then consider the empirical data at the finest level of detail, i.e., we consider time-dependent networks of face-to-face proximity between individuals. To gain insights on the effects that causal constraints have on spreading processes, we simulate the dynamics of a simple susceptible-infected model over the empirical time-resolved contact data. We show that the spreading pathways for the epidemic process are strongly affected by the temporal structure of the network data, and that the mere knowledge of static aggregated networks leads to erroneous conclusions about the transmission paths on the corresponding dynamical networks.
@article{ year={2013}, issn={1951-6355}, journal={The European Physical Journal Special Topics}, volume={222}, number={6}, doi={10.1140/epjst/e2013-01927-7}, title={Empirical temporal networks of face-to-face human interactions}, url={http://dx.doi.org/10.1140/epjst/e2013-01927-7}, publisher={Springer Berlin Heidelberg}, author={Barrat, A. and Cattuto, C. and Colizza, V. and Gesualdo, F. and Isella, L. and Pandolfi, E. and Pinton, J.-F. and Ravà, L. and Rizzo, C. and Romano, M. and Stehlé, J. and Tozzi, A.E. and Broeck, W.}, pages={1295-1309}, language={English} } -
Proceedings of the 7th International Conference on Weblogs and Social Media (ICWSM-13), July 2013, Boston, USAThe prediction of new links in social networks is a challenging task. In this paper, we focus on predicting links in networks of face-to-face spatial proximity by using information from online social networks, such as co-authorship networks in DBLP, and a number of node level attributes. First, we analyze influence factors for the link prediction task. Then, we propose a novel method that combines information from different networks and node level attributes for the prediction task: We introduce an unsupervised link prediction method based on rooted random walks, and show that it outperforms state-of-the-art unsupervised link prediction methods. We present an evaluation using three real-world datasets.Furthermore, we discuss the impact of our results and of the insights we glean in the field of link prediction and human contact behavior.
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Proc. of the GRADES2013 workshop on Graph Data-management Experiences and Systems at SIGMOD2013Representing and efficiently querying time-varying social network data is a central challenge that needs to be addressed in order to support a variety of emerging applications that leverage high-resolution records of human activities and interactions from mobile devices and wearable sensors. In order to support the needs of specific applications, as well as general tasks related to data curation, cleaning, linking, post-processing, and data analysis, data models and data stores are needed that afford efficient and scalable querying of the data. In particular, it is important to design solutions that allow rich queries that simultaneously involve the topology of the social network, temporal information on the presence and interactions of individual nodes, and node metadata. Here we introduce a data model for time-varying social network data that can be represented as a property graph in the Neo4j graph database. We use time-varying social network data collected by using wearable sensors and study the performance of real-world queries, pointing to strengths, weaknesses and challenges of the proposed approach.
@inproceedings{Cattuto:2013:TSN:2484425.2484442, author = {Cattuto, Ciro and Quaggiotto, Marco and Panisson, Andr{\'e} and Averbuch, Alex}, title = {Time-varying social networks in a graph database: a Neo4j use case}, booktitle = {First International Workshop on Graph Data Management Experiences and Systems}, series = {GRADES '13}, year = {2013}, isbn = {978-1-4503-2188-4}, location = {New York, New York}, pages = {11:1--11:6}, articleno = {11}, numpages = {6}, url = {http://doi.acm.org/10.1145/2484425.2484442}, doi = {10.1145/2484425.2484442}, acmid = {2484442}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Neo4j, graph database, graph databases, social networks, temporal networks, wearable sensors} } -
in "Temporal Networks", Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saramäki, Jari (Eds.)
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BMC Infectious Diseases 13, 185 (2013)BACKGROUND. The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios, designing containment strategies, and evaluating outcomes. However, the integration of highly detailed data sources yields models that are less transparent and general in their applicability. Hence, given a specific disease model, it is crucial to assess which representations of the raw data work best to inform the model, striking a balance between simplicity and detail. METHODS. We consider high-resolution data on the face-to-face interactions of individuals in a pediatric hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the empirical contact patterns. At the most detailed level, we take into account all contacts between individuals and their exact timing and order. Then, we build a hierarchy of coarse-grained representations of the contact patterns that preserve only partially the temporal and structural information available in the data. We compare the dynamics of the SEIR model across these representations. RESULTS. We show that a contact matrix that only contains average contact durations between role classes fails to reproduce the size of the epidemic obtained using the high-resolution contact data and also fails to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that, in the case study presented, this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data. CONCLUSIONS. Our results mark a first step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show in a typical case of a structured population that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management.
@Article{1471-2334-13-185, AUTHOR = {Anna Machens and Francesco Gesualdo and Caterina Rizzo and Alberto Tozzi and Alain Barrat and Ciro Cattuto}, TITLE = {An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices}, JOURNAL = {BMC Infectious Diseases}, VOLUME = {13}, YEAR = {2013}, NUMBER = {1}, PAGES = {185}, URL = {http://www.biomedcentral.com/1471-2334/13/185}, DOI = {10.1186/1471-2334-13-185}, ISSN = {1471-2334} } -
Proceedings of the International Workshop on the Impact of Human Mobility in Pervasive Systems and Applications 2013, San Diego.Mobile devices and wearable sensors are making available records of human mobility and proximity with unprecedented levels of detail. Here we focus on close-range human proximity networks measured by means of wireless wearable sensors in a variety of real-world environments. We show that simple dynamical processes computed over the time-varying proximity networks can uncover important features of the interaction patterns that go beyond standard statistical indicators of heterogeneity and burstiness, and can tell apart datasets that would otherwise look statistically similar. We show that, due to the intrinsic temporal heterogeneity of human dynamics, the characterization of spreading processes over time-varying networks of human contact may benefit from abandoning the notion of wall-clock time in favor of a node-specific notion of time based on the contact activity of individual nodes.
2012
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PLoS Compututational Biology 8(7), e1002616 (2012)Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges.
@article{10.1371/journal.pcbi.1002616, author = {Salathé, Marcel and Bengtsson, Linus and Bodnar, Todd J. and Brewer, Devon D. and Brownstein, John S. and Buckee, Caroline and Campbell, Ellsworth M. and Cattuto, Ciro and Khandelwal, Shashank and Mabry, Patricia L. and Vespignani, Alessandro}, journal = {PLoS Computational Biology}, publisher = {Public Library of Science}, title = {Digital Epidemiology}, year = {2012}, month = {07}, volume = {8}, url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1002616}, pages = {e1002616}, number = {7}, doi = {10.1371/journal.pcbi.1002616}, keywords = {SocioPatterns, Epidemiology} } -
Phys. Rev. E 85, 056115 (2012)Many natural and artificial networks evolve in time. Nodes and connections appear and disappear at various timescales, and their dynamics has profound consequences for any processes in which they are involved. The first empirical analysis of the temporal patterns characterizing dynamic networks are still recent, so that many questions remain open. Here, we study how random walks, as paradigm of dynamical processes, unfold on temporally evolving networks. To this aim, we use empirical dynamical networks of contacts between individuals, and characterize the fundamental quantities that impact any general process taking place upon them. Furthermore, we introduce different randomizing strategies that allow us to single out the role of the different properties of the empirical networks. We show that the random walk exploration is slower on temporal networks than it is on the aggregate projected network, even when the time is properly rescaled. In particular, we point out that a fundamental role is played by the temporal correlations between consecutive contacts present in the data. Finally, we address the consequences of the intrinsically limited duration of many real world dynamical networks. Considering the fundamental prototypical role of the random walk process, we believe that these results could help to shed light on the behavior of more complex dynamics on temporally evolving networks. Preprint version: arXiv:1203.2477
@article{PhysRevE.85.056115, title = {Random Walks on Temporal Networks}, author = {Starnini, Michele and Baronchelli, Andrea and Barrat, Alain and Pastor-Satorras, Romualdo}, journal = {Phys. Rev. E}, volume = {85}, issue = {5}, pages = {056115}, numpages = {12}, year = {2012}, month = {May}, doi = {10.1103/PhysRevE.85.056115}, url = {http://link.aps.org/doi/10.1103/PhysRevE.85.056115}, publisher = {American Physical Society}, keywords = {SocioPatterns} } -
Leonardo, Vol. 45, No. 3, 2012The SocioPatterns sensing platform uses wearable electronic badges to sense close-range proximity among individuals. It was used in an experiential exhibit that simulated a virtual epidemic among the visitors of the INFECTIOUS: STAY AWAY exhibition in the Science Gallery in Dublin, Ireland. The collected data was used to generate a high-resolution visualization that illustrates the variation in contact activity over the course of the exhibition.
@article{Wouter-Van-den-Broeck:2012fk, Title = {The making of Sixty-Nine Days Of Close Encounters At The Science Gallery}, Author = {{Van den Broeck}, Wouter and Quaggiotto, Marco and Isella, Lorenzo and Barrat, Alain and Cattuto, Ciro}, Doi = {10.1162/LEON_a_00377}, Journal = {Leonardo}, Keywords = {SocioPatterns: visualization}, Month = {May}, Number = {3}, Pages = {285-285}, Publisher = {ISAST}, Url = {http://www.mitpressjournals.org/doi/abs/10.1162/LEON_a_00377}, Volume = {45}, Year = {2012} }
2011
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PLoS ONE 6(8), e23176 (2011)BACKGROUND. Little quantitative information is available on the mixing patterns of children in school environments. Describing and understanding contacts between children at school would help quantify the transmission opportunities of respiratory infections and identify situations within schools where the risk of transmission is higher. We report on measurements carried out in a French school (6–12 years children), where we collected data on the time-resolved face-to-face proximity of children and teachers using a proximity-sensing infrastructure based on radio frequency identification devices. METHODS AND FINDINGS. Data on face-to-face interactions were collected on Thursday, October 1st and Friday, October 2nd 2009. We recorded 77,602 contact events between 242 individuals (232 children and 10 teachers). In this setting, each child has on average 323 contacts per day with 47 other children, leading to an average daily interaction time of 176 minutes. Most contacts are brief, but long contacts are also observed. Contacts occur mostly within each class, and each child spends on average three times more time in contact with classmates than with children of other classes. We describe the temporal evolution of the contact network and the trajectories followed by the children in the school, which constrain the contact patterns. We determine an exposure matrix aimed at informing mathematical models. This matrix exhibits a class and age structure which is very different from the homogeneous mixing hypothesis. CONCLUSIONS. We report on important properties of the contact patterns between school children that are relevant for modeling the propagation of diseases and for evaluating control measures. We discuss public health implications related to the management of schools in case of epidemics and pandemics. Our results can help define a prioritization of control measures based on preventive measures, case isolation, classes and school closures, that could reduce the disruption to education during epidemics.
@article{10.1371/journal.pone.0023176, author = {Stehlé, Juliette and Voirin, Nicolas and Barrat, Alain and Cattuto, Ciro and Isella, Lorenzo and Pinton, {Jean-François} and Quaggiotto, Marco and {Van den Broeck}, Wouter and Régis, Corinne and Lina, Bruno and Vanhems, Philippe}, journal = {PLoS ONE}, publisher = {Public Library of Science}, title = {High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School}, year = {2011}, month = {08}, volume = {6}, url = {http://dx.doi.org/10.1371/journal.pone.0023176}, pages = {e23176}, number = {8}, doi = {10.1371/journal.pone.0023176}, keywords = {SocioPatterns} } -
BMC Medicine, 9(87), July 2011.BACKGROUND. The spread of infectious diseases crucially depends on the pattern of contacts between individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. However, there are few empirical studies available that provide estimates of the number and duration of contacts between social groups. Moreover, their space and time resolutions are limited, so that data are not explicit at the person-to-person level, and the dynamic nature of the contacts is disregarded. In this study, we aimed to assess the role of data-driven dynamic contact patterns between individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population. METHODS. We considered high-resolution data about face-to-face interactions between the attendees at a conference, obtained from the deployment of an infrastructure based on radiofrequency identification (RFID) devices that assessed mutual face-to-face proximity. The spread of epidemics along these interactions was simulated using an SEIR (Susceptible, Exposed, Infectious, Recovered) model, using both the dynamic network of contacts defined by the collected data, and two aggregated versions of such networks, to assess the role of the data temporal aspects. RESULTS. We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation that retains only the topology of the contact network fails to reproduce the size of the epidemic. CONCLUSIONS. These results have important implications for understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics. Notes This paper was accompanied by a commentary by Sally Blower and Myong-Hyun Go.
@article{Stehle:2011nx, Author = {Stehlé, Juliette and Voirin, Nicolas and Barrat, Alain and Cattuto, Ciro and Colizza, Vittoria and Isella, Lorenzo and Regis, Corinne and Pinton, {Jean-François} and Khanafer, Nagham and {Van den Broeck}, Wouter and Vanhems, Philippe}, Doi = {10.1186/1741-7015-9-87}, Journal = {BMC Medicine}, Keywords = {SocioPatterns}, Month = {July}, Number = {87}, Title = {Simulation of an SEIR Infectious Disease Model on the Dynamic Contact Network of Conference Attendees}, Url = {http://www.biomedcentral.com/1741-7015/9/87}, Volume = {9}, Year = {2011}, Keywords = {SocioPatterns} } -
Ad Hoc Networks 10, 1532-1543 (2012, available online June 2011), doi: 10.1016/ j.adhoc.2011.06.003We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.
@article{Panisson2011, title = {On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks}, journal = {Ad Hoc Networks}, volume = 10, pages= {1532--1543}, year = {2012}, issn = {1570-8705}, doi = {10.1016/j.adhoc.2011.06.003}, url = {http://www.sciencedirect.com/science/article/pii/S1570870511001272}, author = {Panisson, André and Barrat, Alain and Cattuto, Ciro and Wouter {Van den Broeck}, Wouter and Ruffo, Giancarlo and Schifanella, Rossano}, keywords = {SocioPatterns; Mobile Networks; Opportunistic/Delay-Tolerant Protocols; Data Diffusion} } -
PLoS ONE 6(2), e17144 (2011)BACKGROUND. Nosocomial infections place a substantial burden on health care systems and represent one of the major issues in current public health, requiring notable efforts for its prevention. Understanding the dynamics of infection transmission in a hospital setting is essential for tailoring interventions and predicting the spread among individuals. Mathematical models need to be informed with accurate data on contacts among individuals. METHODS AND FINDINGS. We used wearable active Radio-Frequency Identification Devices (RFID) to detect face-to-face contacts among individuals with a spatial resolution of about 1.5 meters, and a time resolution of 20 seconds. The study was conducted in a general pediatrics hospital ward, during a one-week period, and included 119 participants, with 51 health care workers, 37 patients, and 31 caregivers. Nearly 16,000 contacts were recorded during the study period, with a median of approximately 20 contacts per participants per day. Overall, 25% of the contacts involved a ward assistant, 23% a nurse, 22% a patient, 22% a caregiver, and 8% a physician. The majority of contacts were of brief duration, but long and frequent contacts especially between patients and caregivers were also found. In the setting under study, caregivers do not represent a significant potential for infection spread to a large number of individuals, as their interactions mainly involve the corresponding patient. Nurses would deserve priority in prevention strategies due to their central role in the potential propagation paths of infections. CONCLUSIONS. Our study shows the feasibility of accurate and reproducible measures of the pattern of contacts in a hospital setting. The obtained results are particularly useful for the study of the spread of respiratory infections, for monitoring critical patterns, and for setting up tailored prevention strategies. Proximity-sensing technology should be considered as a valuable tool for measuring such patterns and evaluating nosocomial prevention strategies in specific settings.
@article{10.1371/journal.pone.0017144, Title = {Close Encounters in a Pediatric Ward: Measuring Face-to-Face Proximity and Mixing Patterns with Wearable Sensors}, Author = {Isella, Lorenzo and Romano, Mariateresa and Barrat, Alain and Cattuto, Ciro and Colizza, Vittoria and {Van den Broeck}, Wouter and Gesualdo, Francesco and Pandolfi, Elisabetta and Ravà, Lucilla and Rizzo, Caterina and Tozzi, Alberto Eugenio}, Journal = {PLoS ONE}, Year = {2011}, Month = {02}, Number = {2}, Volume = {6}, Pages = {e17144}, Publisher = {Public Library of Science}, Doi = {10.1371/journal.pone.0017144}, Url = {http://dx.doi.org/10.1371%2Fjournal.pone.0017144}, Keywords = {SocioPatterns} }
2010
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Journal of Theoretical Biology 271 (2011) 166-180.The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing differences and similarities. We use our data to investigate the dynamics of a susceptible–infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks.
@article{Isella:2011qo, title = {What's in a Crowd? Analysis of Face-to-Face Behavioral Networks}, journal = {Journal of Theoretical Biology}, volume = 271, number = 1, pages = {166--180}, year = 2011, issn = {0022-5193}, doi = {DOI: 10.1016/j.jtbi.2010.11.033}, url = {http://www.sciencedirect.com/science/article/B6WMD-51M60KS-2/2/cb31bee32b340b3044c724b88779a60e}, author = {Isella, Lorenzo and Stehlé, Juliette and Barrat, Alain and Cattuto, Ciro and Pinton, {Jean-François} and {Van den Broeck}, Wouter}, keywords = {SocioPatterns} } -
Proceedings of the 3rd International ICST Conference on Electronic Healthcare for the 21st century (eHealth 2010). Casablanca, Morocco, December 2010.We describe the experimental deployment of a network of wearable sensors that allows the tracking of the location and mutual proximity of individuals in a hospital ward, in real time and at a large scale. In the course of the deployment, all individuals accessing the premises were monitored for a period of one week, including health care personnel, patients, visitors and tutors. The data collected yields a rich dynamical picture of the contact patterns between individuals and between categories of individuals. As an example, here we show that by constructing a cumulative weighted contact network aggregating the dynamical data on the entire duration of the deployment, it is possible to reliably uncover persistent relations among individuals.
@inproceedings{Barrat:2010tw, Title = {Wearable Sensor Networks for Measuring Face-to-Face Contact Patterns in Healthcare Settings}, Author = {Barrat, Alain and Cattuto, Ciro and Colizza, Vittoria and Isella, Lorenzo and Rizzo, Caterina and Tozzi, Alberto E. and {Van den Broeck}, Wouter}, Booktitle = {Proceedings of the 3rd International ICST Conference on Electronic Healthcare for the 21st century (eHealth 2010)}, Editor = {Szomszor, Martin and Kostkova, Patty}, Keywords = {SocioPatterns}, Year = {2011}} -
Proceedings of the 9th International Semantic Web Conference (ISWC'10), 2010.Popularity and spread of online social networking in recent years has given a great momentum to the study of dynamics and patterns of social interactions. However, these studies have often been confined to the online world, neglecting its interdependencies with the offline world. This is mainly due to the lack of real data that spans across this divide. The Live Social Semantics application is a novel platform that dissolves this divide, by collecting and integrating data about people from (a) their online social networks and tagging activities from popular social networking sites, (b) their publications and co-authorship networks from semantic repositories, and (c) their real-world face-to-face contacts with other attendees collected via a network of wearable active sensors. This paper investigates the data collected by this application during its deployment at three major conferences, where it was used by more than 400 people. Our analyses show the robustness of the patterns of contacts at various conferences, and the influence of various personal properties (e.g. seniority, conference attendance) on social networking patterns.
@inproceedings{sdc_iswc10, booktitle = {Proceedings of the 9th International Semantic Web Conference (ISWC '10)}, title = {Social Dynamics in Conferences: Analysis of Data from the Live Social Semantics Application.}, author = {Barrat, Alain and Cattuto, Ciro and Szomszor, Martin and {Van den Broeck}, Wouter and Alani, Harith}, year = {2010}, url = {http://iswc2010.semanticweb.org/accepted-papers/410}, keywords = {SocioPatterns, Live Social Semantics} } -
In: Proceedings of the 3rd International Conference on Developments in eSystems Engineering (DeSE 2010), 6-8 September 2010, London.Large medical conferences offer opportunities for participants to find industry exhibitors that offer products and services relevant to their professional interests. Companies often invest significant effort in promotions that encourage participants to spend time at their stand (e.g. providing free gifts, leaflets, running competitions) and register some contact details. Attendees will use the conference to find others who also share similar professional interests, as well as keep up to date with developments on products such has pharmaceuticals and medical equipment. From both perspectives, a number of improvements can be made to enhance the overall experience by using existing active RFID technology: Vendors would be able to more closely monitor the success of their promotions with statistics on the stand's visitors, as well as find more potential customers by using real-time visualizations; Participants would be able to log their social interactions, keeping an electronic history of the people they have met. The SocioPatterns project and Live Social Semantics experiments have recently demonstrated a scalable and robust infrastructure that would support these kinds of improvements. In this paper, we propose an infrastructure that provides enhanced social interaction services for vendors and participants by using small active RFID badges worn by attendees and attached to fixed locations.
@inproceedings{oro22980, booktitle = {Proceedings of the 3rd International Conference on Developments in eSystems Engineering (DeSE 2010)}, title = {Providing Enhanced Social Interaction Services for Industry Exhibitors at large Medical Conferences}, author = {Szomszor, Martin and Kostkova, Patty and Cattuto, Ciro and {Van den Broeck}, Wouter and Barrat, Alain and Alani, Harith}, year = {2010}, url = {http://oro.open.ac.uk/22980/}, keywords = {SocioPatterns} } -
PLoS ONE 5(7), e11596 (2010)Background Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. Methods and Findings We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Conclusions Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.
@article{10.1371/journal.pone.0011596, author = {Cattuto, Ciro and {Van den Broeck}, Wouter and Barrat, Alain and Colizza, Vittoria and Pinton, {Jean-François} and Vespignani, Alessandro}, journal = {PLoS ONE}, publisher = {Public Library of Science}, title = {Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks}, year = {2010}, month = {07}, volume = {5}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0011596}, pages = {e11596}, number = {7}, doi = {10.1371/journal.pone.0011596}, keywords = {SocioPatterns} } -
In Aroyo et al., editors, The Semantic Web: Research and Applications, volume 6089 of Lecture Notes in Computer Science, pages 196–210. Springer Berlin / Heidelberg, 2010. doi: 10.1007/978-3-642-13489-0_14.The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes the Live Social Semantics application, with a focus on how data from Web 2.0 sources can be used to automatically generate Profiles of Interest. We evaluate and discuss the results of its two deployments, assessing the accuracy of profiles generated, the willingness to link to external social networking sites, and the feedback given through user questionnaires.
@incollection {springerlink:10.1007/978-3-642-13489-0_14, title = {Semantics, Sensors, and the Social Web: The Live Social Semantics Experiments}, author = {Szomszor, Martin and Cattuto, Ciro and {Van den Broeck}, Wouter and Barrat, Alain and Alani, Harith}, affiliation = {City University London City eHealth Research Centre UK}, booktitle = {The Semantic Web: Research and Applications}, series = {Lecture Notes in Computer Science}, editor = {Aroyo, Lora and Antoniou, Grigoris and Hyvönen, Eero and {ten Teije}, Annette and Stuckenschmidt, Heiner and Cabral, Liliana and Tudorache, Tania}, publisher = {Springer Berlin / Heidelberg}, pages = {196--210}, volume = {6089}, doi = {10.1007/978-3-642-13489-0_14}, year = {2010}, keywords = {Live Social Semantics, SocioPatterns} } -
Proceedings of the First International Workshop on Communication, Collaboration and Social Networking in Pervasive Computing Environments (PerCol'10), 2010.We describe a novel application that integrates real-world data on the face-to-face proximity of individuals with their identities and contacts in on-line social networks. This application was successfully deployed at two conference gatherings, ESWC09 and HT2009, and actively used by hundreds of people. Personal profiles of the participants were automatically generated using several Web 2.0 systems and semantic data sources, and integrated in real-time with face-toface proximity relations detected using RFID-enabled badges. The integration of these heterogeneous data sources enables various services that enhance the experience of conference attendees, allowing them to explore their social neighborhood and to connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in these deployments.
@inproceedings{Van-den-Broeck:2010lh, title = {The Live Social Semantics Application: a Platform for Integrating Face-to-Face Presence with On-Line Social Networking}, booktitle = {First International Workshop on Communication, Collaboration and Social Networking in Pervasive Computing Environments (PerCol'10)}, month = {April}, author = {{Van den Broeck}, Wouter and Cattuto, Ciro and Barrat, Alain and Szomszor, Martin and Correndo, Gianluca and Alani, Harith}, year = {2010}, url = {http://eprints.ecs.soton.ac.uk/18371/}, keywords = {Live Social Semantics, SocioPatterns} }
2008
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Proceedings of the 8th International Semantic Web Conference (ISWC'09), 2009.Social interactions are one of the key factors to the success of conferences and similar community gatherings. This paper describes a novel application that integrates data from the semantic web, online social networks, and a real-world contact sensing platform. This application was successfully deployed at ESWC09, and actively used by 139 people. Personal profiles of the participants were automatically generated using several Web 2.0 systems and semantic academic data sources, and integrated in real-time with face-to-face contact networks derived from wearable sensors. Integration of all these heterogeneous data layers made it possible to offer various services to conference attendees to enhance their social experience such as visualisation of contact data, and a site to explore and connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in this deployment
@inproceedings{ecs17772, booktitle = {8th International Semantic Web Conference (ISWC'09)}, month = {October}, title = {Live Social Semantics}, author = {Alani, Harith and Szomszor, Martin and Cattuto, Ciro and {Van den Broeck} Wouter and Correndo, Gianluca and Barrat, Alain}, year = {2009}, url = {http://eprints.ecs.soton.ac.uk/17772/}, keywords = {SocioPatterns} } -
Informal arXiv.org publication, Nov 2008.In this paper we present an experimental framework to gather data on face-to-face social interactions between individuals, with a high spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess contacts with one another by exchanging low-power radio packets. When individuals wear the beacons as a badge, a persistent radio contact between the RFID devices can be used as a proxy for a social interaction between individuals. We present the results of a pilot study recently performed during a conference, and a subsequent preliminary data analysis, that provides an assessment of our method and highlights its versatility and applicability in many areas concerned with human dynamics.
@ARTICLE{2008arXiv0811.4170B, author = {Barrat, Alain and Cattuto, Ciro and Colizza, Vittoria and Pinton, {Jean-François} and {Van den Broeck}, Wouter and Vespignani, Alessandro}, title = {High Resolution Dynamical Mapping of Social Interactions With Active RFID}, journal = {ArXiv e-prints}, archivePrefix = {arXiv}, eprint = {0811.4170}, primaryClass = {cs.CY}, keywords = {Computer Science - Computers and Society, Computer Science - Human-Computer Interaction, Physics - Physics and Society}, year = 2008, month = nov, adsurl = {http://adsabs.harvard.edu/abs/2008arXiv0811.4170B}, adsnote = {Provided by the SAO/NASA Astrophysics Data System}, keywords = {SocioPatterns} }