title: Disease Surveillance on Complex Social Networks.
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.
authors.affiliation: Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America.|Departamento de Cálculo, Escuela Básica de Ingeniería, Facultad de Ingeneiría, Universidad de Los Andes, Mérida, Venezuela.
authors.forename: Jose L
authors.initials: JL
authors.lastname: Herrera
authors.affiliation: Applied Research Laboratories, The University of Texas at Austin, Austin, Texas, United States of America.|Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Ravi
authors.initials: R
authors.lastname: Srinivasan
authors.affiliation: Department of Pediatrics, Harvard Medical School and Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
authors.forename: John S
authors.initials: JS
authors.lastname: Brownstein
authors.affiliation: Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut, United States of America.
authors.forename: Alison P
authors.initials: AP
authors.lastname: Galvani
authors.affiliation: Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Lauren Ancel
authors.initials: LA
authors.lastname: Meyers
mesh_terms: D019295:Computational Biology; D004196:Disease Outbreaks / Q000517:prevention & control / Q000706:statistics & numerical data; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D011634:Public Health / Q000379:methods*; D062486:Public Health Surveillance; D012944:Social Support