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Social Network Sensors for Early Detection of Contagious Outbreaks
Open accessAbstract
Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. It is known that individuals near the center of a social network are likely to be infected sooner during the course of an outbreak, on average, than those at the periphery. Unfortunately, mapping a whole network to identify central individuals who might be monitored for infection is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals. Such individuals are known to be more central. To evaluate whether such a friend group could indeed provide early detection, we studied a flu outbreak at Harvard College in late 2009. We followed 744 students who were either members of a group of randomly chosen individuals or a group of their friends. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 13.9 days (95% C.I. 9.9-16.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (p<0.05) on day 16 of the epidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. The amount of lead time will depend on features of the outbreak and the network at hand. The method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.
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Cites
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,670
Mack, P., & Rogers, E. (1964) · Encyclopedia of Sport Management · doi:10.2307/3319808 · Cited by 23,677
A set of measures of centrality based upon betweenness
Freeman, L. (1977) · Sociometry · doi:10.2307/3033543 · Cited by 10,557
Stanton, B. F., & Rogers, E. M. (1963) · Journal of Farm Economics · doi:10.2307/1235767 · Cited by 6,726
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Nonlinear Regression Analysis and Its Applications
Bates, D. M., & Watts, D. G. (1988) · Wiley series in probability and statistics · doi:10.1002/9780470316757 · Cited by 4,017
Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., Massari, M., Salmaso, S., Tomba, G. S., Wallinga, J., et al. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050074 · Cited by 2,900
An algorithm for drawing general undirected graphs
Kamada, T., & Kawai, S. (1989) · Information Processing Letters · doi:10.1016/0020-0190(89)90102-6 · Cited by 2,750
Cost-effective outbreak detection in networks
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J. M., & Glance, N. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281239 · Cited by 2,614
The collective dynamics of smoking in a large social network
Christakis, N. A., & Fowler, J. H. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0706154 · Cited by 2,390
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Items 101–200 of 532
Measuring Social Connectedness
Bailey, M., Cao, R., Kuchler, T., Stroebel, J., & Wong, A. (2017) · National Bureau of Economic Research · doi:10.3386/w23608 · Cited by 44
A new method to construct co-author networks
Liu, J., Li, Y., Ruan, Z., Fu, G., Chen, X., Sadiq, R., & Deng, Y. (2014) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.10.006 · Cited by 43
Can Network Theory-Based Targeting Increase Technology Adoption?Preprint
Beaman, L. A., BenYishay, A., Magruder, J., & Mobarak, A. M. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3225815 · Cited by 43
Smieszek, T., & Salathé, M. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-35 · Cited by 42
Immunization Strategies Based on the Overlapping Nodes in Networks with Community Structure
Chakraborty, D., Singh, A. K., & Cherifi, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-42345-6_6 · Cited by 42
Optimizing sentinel surveillance in temporal network epidemiology
Bai, Y., Yang, B. X., Lin, L., Herrera, J. L., Du, Z., & Holme, P. (2017) · Scientific Reports · doi:10.1038/s41598-017-03868-6 · Cited by 41
Endo, A., Uchida, M., Kucharski, A. J., & Funk, S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007589 · Cited by 42
Alexander, M., Forastiere, L., Gupta, S., & Christakis, N. A. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2120742119 · Cited by 42
Self-citationOccupations and their impact on the spreading of COVID-19 in urban communities
Hâncean, M.-G., Lerner, J., Perc, M., Oană, I., Bunaciu, D.-A., Stoica, A. A., & Ghiță, M.-C. (2022) · Scientific Reports · doi:10.1038/s41598-022-18392-5 · Cited by 41
Early warning analysis for social diffusion events
Colbaugh, R., & Glass, K. (2012) · Security Informatics · doi:10.1186/2190-8532-1-18 · Cited by 41
Detecting Changes in Dynamic Events Over Networks
Li, S., Xie, Y., Farajtabar, M., Verma, A., & Song, L. (2017) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2017.2696264 · Cited by 41
Efficient detection of contagious outbreaks in massive metropolitan encounter networks
Sun, L., Axhausen, K. W., Lee, D.-H., & Cebrián, M. (2014) · Scientific Reports · doi:10.1038/srep05099 · Cited by 48
The extraversion continuum in evolutionary perspective: A review of recent theory and evidence
Lukaszewski, A. W., & Rueden, C. R. V. (2015) · Personality and Individual Differences · doi:10.1016/j.paid.2015.01.005 · Cited by 42
Recent Advances in Computational Epidemiology
MARATHE, M. V., & Ramakrishnan, N. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.114 · Cited by 40
Leal-Neto, O. B., Dimech, G. S., Libel, M., de Souza, W. V., Cesse, E. Â. P., Smolinski, M. S., de Oliveira, W. K., & Albuquerque, J. D. O. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.7313 · Cited by 40
Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities
Bucur, D., & Holme, P. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008052 · Cited by 42
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Evaluating Stochastic Seeding Strategies in Networks
Chin, A., Eckles, D., & Ugander, J. H. (2021) · Management Science · doi:10.1287/mnsc.2021.3963 · Cited by 43
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Pileggi, S. F., Fernández-Llatas, C., & Traver, V. (2012) · Future Internet · doi:10.3390/fi4030852 · Cited by 40
Individual-based approach to epidemic processes on arbitrary dynamic contact networks
Rocha, L. E. C., & Masuda, N. (2016) · Scientific Reports · doi:10.1038/srep31456 · Cited by 40
Community Structure in Social Networks: Applications for Epidemiological Modelling
Kitchovitch, S., & Lió, P. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022220 · Cited by 39
A detailed review of the outbreak of COVID-19
Das, K., Pingali, M. S., Paital, B., Panda, F., Pati, S. G., Singh, A. K., Varadwaj, P. K., & Samanta, S. K. (2021) · Frontiers in Bioscience-Landmark · doi:10.52586/4931 · Cited by 43
Identifying influencers from sampled social networks
Tsugawa, S., & Kimura, K. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2018.05.105 · Cited by 38
Influence maximization on temporal networks: a review
Yanchenko, E., Murata, T., & Holme, P. (2024) · Applied Network Science · doi:10.1007/s41109-024-00625-3 · Cited by 37
Your friends have more friends than you do
Han, B., & Srinivasan, A. (2012) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/2248371.2248376 · Cited by 37
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Madotto, A., & Liu, J. (2016) · Scientific Reports · doi:10.1038/srep38994 · Cited by 39
How Physical Proximity Shapes Complex Social Networks
Stopczynski, A., Pentland, A., & Lehmann, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-36116-6 · Cited by 40
Efficient sentinel surveillance strategies for preventing epidemics on networks
Colman, E. R., Holme, P., Sayama, H., & Gershenson, C. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007517 · Cited by 38
Goggins, S., Galyen, K., Petakovic, E., & Laffey, J. M. (2016) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12129 · Cited by 44
Aiello, A. E., Simanek, A. M., Eisenberg, M. C., Walsh, A. R., Davis, B. M., Volz, E., Cheng, C. K., Rainey, J. J., Uzicanin, A., Gao, H., et al. (2016) · Epidemics · doi:10.1016/j.epidem.2016.01.001 · Cited by 35
Efficient vaccination strategies for epidemic control using network information
Yang, Y., McKhann, A., Chen, S., Harling, G., & Onnela, J.-P. “. (2019) · Epidemics · doi:10.1016/j.epidem.2019.03.002 · Cited by 37
Chowell, G., Dahal, S., Tariq, A., Roosa, K. M., Hyman, J. M., & Luo, R. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010602 · Cited by 37
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Sierra‐Arévalo, M., & Papachristos, A. V. (2017) · Annual Review of Law and Social Science · doi:10.1146/annurev-lawsocsci-110615-085017 · Cited by 36
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Atherley, A., Nimmon, L., Teunissen, P. W., Dolmans, D. H. J. M., Hegazi, I., & Hu, W. C. Y. (2020) · Medical Education · doi:10.1111/medu.14382 · Cited by 40
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Hoen, A. G., Hladish, T. J., Eggo, R. M., Lenczner, M. M., Brownstein, J. S., & Ancel, L. W. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3720 · Cited by 34
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Halting SARS-CoV-2 by Targeting High-Contact Individuals
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Owen, L. L. W., Chang, T. H., & Manning, J. R. (2021) · Nature Communications · doi:10.1038/s41467-021-25876-x · Cited by 34
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Hwang, S.-M., Park, D.-E., Yang, Y.-I., Park, S. J., Lee, H.-K., Kim, M. J., & Chun, B. C. (2013) · Japanese Journal of Infectious Diseases · doi:10.7883/yoken.66.359 · Cited by 31
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Endo, A., Uchida, M., Hayashi, N., Liu, Y., Atkins, K. E., Kucharski, A. J., & Funk, S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112605118 · Cited by 32
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Connected but segregated: social networks in rural villages
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Viviano, D. (2024) · The Review of Economic Studies · doi:10.1093/restud/rdae041 · Cited by 20
Objective measures for sentinel surveillance in network epidemiology
Holme, P. (2018) · Physical review. E · doi:10.1103/physreve.98.022313 · Cited by 20
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Link transmission centrality in large-scale social networks
Zhang, Q., Karsai, M., & Vespignani, A. (2018) · EPJ Data Science · doi:10.1140/epjds/s13688-018-0162-8 · Cited by 20
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Zhang, H., Alim, M. A., Thai, M. T., & Nguyen, H. P. N. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7248478 · Cited by 19
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Detecting misinformation in online social networks before it is too late
Zhang, H., Kuhnle, A., Zhang, H., & Thai, M. T. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752288 · Cited by 19
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Stattner, E., Collard, M., & Vidot, N. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2012.06.004 · Cited by 18
Montes, F., Jaramillo, A. M., Meisel, J. D., Dı́az-Guilera, A., Valdivia, J. A., Sarmiento, O. L., & Zarama, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-60239-4 · Cited by 18
Parents’ attitudes toward children’s vaccination as a marker of trust in health systems
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Helbing, D., Balietti, S., Bishop, S. R., & Lukowicz, P. (2011) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2011-01410-7 · Cited by 18
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Krishnamurthy, V. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988133 · Cited by 19
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Vianna, H. D., & Barbosa, J. L. V. (2020) · JUCS - Journal of Universal Computer Science · doi:10.3897/jucs.2020.002 · Cited by 18
Amaku, M., Grisi-Filho, J. H. H., Negreiros, R. L., Dias, R. A., Ferreira, F., Neto, J. S. F., Cipullo, R. I., Marques, F. S., & Ossada, R. (2015) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2015.08.002 · Cited by 18
Using Twitter to Enhance Traffic Incident Awareness
Zhang, S. (2015) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2015.471 · Cited by 17
An Innovative Influenza Vaccination Policy: Targeting Last Season's Patients
Yamin, D., Arieh Gavious, B., Solnik, E., Davidovitch, N., Balicer, R. D., Galvani, A. P., & Pliskin, J. S. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003643 · Cited by 17
Metadata
- Authors
- Christakis, N. A., & Fowler, J. H.
- Affiliations
- Harvard University (US)
- University of California San Diego (US)
- Corresponding author
- Christakis, Nicholas A.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-09-15
- Volume
- 5
- Issue
- 9
- Pages
- e12948
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC2939797
- OpenAlex
- W2140095656
- Cited by
- 535
- References
- 44
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 21 · 2023: 19 · 2022: 33 · 2021: 49 · 2020: 38 · 2019: 31 · 2018: 40 · 2017: 50 · 2016: 40 · 2015: 48 · 2014: 44 · 2013: 36 · 2012: 46
- FWCI
- 24.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Robert Wood Johnson Foundation
- McKnight Foundation
- Harvard University
- National Institute on Aging (P01AG031093)
- National Institute of General Medical Sciences (P41GM103504)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- influenza outbreak, 2009 H1N1 pandemic, Harvard College, network centrality, lead time, disease surveillance
- MeSH terms
- Disease Outbreaks, Female, Humans, Influenza, Human, Male, Social Support, Early Diagnosis, Young Adult, Epidemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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