Research Article: Predicting and containing epidemic risk using on-line friendship networks

Date Published: May 16, 2019

Publisher: Public Library of Science

Author(s): Lorenzo Coviello, Massimo Franceschetti, Manuel García-Herranz, Iyad Rahwan, Naoki Masuda.

http://doi.org/10.1371/journal.pone.0211765

Abstract

To what extent can online social networks predict who is at risk of an infection? Many infections are transmitted through physical encounter between humans, but collecting detailed information about it can be expensive, might invade privacy, or might not even be possible. In this paper, we ask whether online social networks help predict and contain epidemic risk. Using a dataset from a popular online review service which includes over 100 thousand users and spans 4 years of activity, we build a time-varying network that is a proxy of physical encounter between its users (the encounter network) and a static network based on their reported online friendship (the friendship With computer simulations, we compare stochastic infection processes on the two networks, considering infections on the encounter network as the benchmark. First, we show that the friendship network is useful to identify the individuals at risk of infection, despite providing lower accuracy than the ideal case in which the encounters are known. This limited prediction accuracy is not only due to the static nature of the friendship network because a static version of the encounter network provides more accurate prediction of risk than the friendship network. Then, we show that periodical monitoring of the infection spreading on the encounter network allows to correct the infection predicted by a process spreading on the friendly staff ndship network, and achieves high prediction accuracy. Finally, we show that the friendship network contains valuable information to effectively contain epidemic outbreaks even when a limited budget is available for immunization. In particular, a strategy that immunizes random friends of random individuals achieves the same performance as knowing individuals’ encounters at a small additional cost, even if the infection spreads on the encounter network.

Partial Text

The forecast and containment of epidemics is a central theme in public health [1–4]. Events such as the recent ebola epidemic constantly drive the attention and resources of institutions such as the World Health Organization, governments, and researchers [5–7]. Beside biological epidemics, the study of infectious processes is of broad interest as it models the spread of information, behaviors, cultural norms, innovation, as well as the diffusion of computer viruses [8–11]. As it is impossible to study the spread of infectious diseases through controlled experiments, and thanks to advancements in computation, modeling efforts have prevailed [12–14].

Epidemics are complex problems that draw tremendous efforts from Governments and International Organizations. Given the diversity of contexts in which they happen and the varied nature of different diseases, epidemic response presents multiple challenges that need to be addressed in order to curve the thread. In addition, an increasingly connected world has shown in the last decades the fast pace at which epidemics can turn into pandemics—see for example the H1N1 crisis of 2009, the Ebola outbreak in 2014, or the Zika epidemic of 2015. The increased speed and reach have further pointed out the need to develop more and better tools to target resources in a more accurately, timely and efficient manner.

 

Source:

http://doi.org/10.1371/journal.pone.0211765

 

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