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.

Local risk perception enhances epidemic control

“…When gauging infection risk, individuals may consider global information (e.g., from news media) or local first-hand encounters with disease (e.g., infected acquaintances, friends or family members) [30,31]. While traditional compartmental models assume homogeneity in both epidemiological risks and intervention benefits, network-based models provide a tractable framework for studying the complex interplay between contact networks, intervention decision making and disease transmission [34,[45][46][47][48][49][50][51].Here, we investigate the epidemiological impacts of different decision paradigms using a network-based SIR epidemic model, in which individuals also make vaccination or social distancing choices based on their perceived epidemiological risks. Depending on the decision model, they estimate either overall disease prevalence, their number of infected social contacts, or their fraction of infected social contacts.…”

Modelling microbial infection to address global health challenges

“…Such threats underscore the importance of surveillance systems and preparedness plans, which can be informed by modelling (Box 1). Transmission models are able to optimize surveillance systems, accelerate outbreak detection and improve forecasting [53][54][55][56] . For example, a spatial model integrating a variety of surveillance data streams and embedded in a user-friendly platform is currently implemented by the Texas Department of State Health Services to generate real-time influenza forecasts (http://flu.tacc.utexas.edu/).…”

Analytical sociology and computational social science

“…The real-time collection of online data is important because it allows for analyses of issues of high public relevance such as the early detection of outbreaks of contagious diseases based on web search queries and symptom posting on social media [61][62][63]. In an exemplary study on effective vaccination strategies, Mones et al [64] equipped Danish students with mobile phones to collect multidimensional network data from proximate and online interactions.…”