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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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Using global diversity and local features to identify influential social network spreaders
Fu, Y.-H., Huang, C.-Y., & Sun, C. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921700 · Cited by 7
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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