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Herrera JL, Srinivasan R, Brownstein JS, et al. Disease Surveillance on Complex Social Networks. PLoS Comput Biol. 2016;12(7):e1004928doi: 10.1371/journal.pcbi.1004928.
Herrera, J. L., Srinivasan, R., Brownstein, J. S., Galvani, A. P., & Meyers, L. A. (2016). Disease Surveillance on Complex Social Networks. PLoS computational biology, 12(7), e1004928. https://doi.org/10.1371/journal.pcbi.1004928
Herrera, Jose L, et al. "Disease Surveillance on Complex Social Networks." PLoS computational biology vol. 12,7 (2016): e1004928. doi: https://doi.org/10.1371/journal.pcbi.1004928
Herrera JL, Srinivasan R, Brownstein JS, Galvani AP, Meyers LA. Disease Surveillance on Complex Social Networks. PLoS Comput Biol. 2016 Jul 14;12(7):e1004928. doi: 10.1371/journal.pcbi.1004928. eCollection 2016 Jul. PMID: 27415615; PMCID: PMC4944951.
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PLoS Comput Biol. 2016 Jul 14;12(7):e1004928. doi: 10.1371/journal.pcbi.1004928. eCollection 2016 Jul.

Disease Surveillance on Complex Social Networks.

PLoS computational biology

Jose L Herrera, Ravi Srinivasan, John S Brownstein, Alison P Galvani, Lauren Ancel Meyers

Affiliations

  1. Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America.
  2. Departamento de Cálculo, Escuela Básica de Ingeniería, Facultad de Ingeneiría, Universidad de Los Andes, Mérida, Venezuela.
  3. Applied Research Laboratories, The University of Texas at Austin, Austin, Texas, United States of America.
  4. Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, Texas, United States of America.
  5. Department of Pediatrics, Harvard Medical School and Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
  6. Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut, United States of America.

PMID: 27415615 PMCID: PMC4944951 DOI: 10.1371/journal.pcbi.1004928

Abstract

As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors-sampling the most connected, random, and friends of random individuals-in three complex social networks-a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals-early and accurate detection of epidemic emergence and peak, and general situational awareness-we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0). For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information.

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