ArticlePloS one2010
Social network sensors for early detection of contagious outbreaks.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers.
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Who cites it
149 citing papers in PubMed.
- Trial
- Design and methods of a social network isolation study for reducing respiratory infection transmission: The eX-FLU cluster randomized trial.Epidemics · 2016Trial
- Social network targeting to maximise population behaviour change: a cluster randomised controlled trial.Lancet (London, England) · 2015Trial
- Bivalent impact of social networks on overarming: Insights on the alignment between social and individual interests.Science advances · 2026Article
- The effect of personality on likelihood of contracting SARS-CoV-2 in the United States.Personality and individual differences · 2026Article
- Modelling the spread of infectious diseases in public transport systems under varying demand patterns and capacity constraints.Scientific reports · 2025Article
- Assessing the impact of disease incidence and immunization on the resilience of complex networks during epidemics.Infectious Disease Modelling · 2025Article
- Article
- Novel sampling strategy for regular nucleic acid testing in low risk areas during epidemics.Scientific reports · 2024Article
- Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.Scientific reports · 2024Article
- Causal inference over stochastic networks.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Vaccination homophily in ego contact networks during the COVID-19 pandemic.Scientific reports · 2024Article
- Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors.Scientific reports · 2024Article
- Induction of social contagion for diverse outcomes in structured experiments in isolated villages.Science (New York, N.Y.) · 2024Article
- COVID-19 hotspot detection in a university setting.PloS one · 2024Article
- Contagion and Interpersonal Influence: Distinguishing Mechanisms of Behavior Change Using Social Network Theory.Connections (Toronto, Ont.) · 2024Article
- Homophily and social mixing in a small community: Implications for infectious disease transmission.PloS one · 2024Article
- Causal Inference for Social Network Data.Journal of the American Statistical Association · 2024Article
- The enmity paradox.Scientific reports · 2023Article
- Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts.The Journal of infectious diseases · 2023Article
89 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
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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