Forecasting the Flu: Designing Social Network Sensors for Epidemics

Early detection and modeling of a contagious epidemic can provide important guidance about quelling the contagion, controlling its spread, or the effective design of countermeasures. A topic of recent interest has been to design social network sensors, i.e., identifying a small set of people who can be monitored to provide insight into the emergence of an epidemic in a larger population. We formally pose the problem of designing social network sensors for flu epidemics and identify two different objectives that could be targeted in such sensor design problems. Using the graph theoretic notion of dominators we develop an efficient and effective heuristic for forecasting epidemics at lead time. Using six city-scale datasets generated by extensive microscopic epidemiological simulations involving millions of individuals, we illustrate the practical applicability of our methods and show significant benefits (up to twenty-two days more lead time) compared to other competitors. Most importantly, we demonstrate the use of surrogates or proxies for policy makers for designing social network sensors that require from nonintrusive knowledge of people to more information on the relationship among people. The results show that the more intrusive information we obtain, the longer lead time to predict the flu outbreak up to nine days. ; Supported by the Intelligence Advanced Research Projects Activity (IARPA) via DoI/NBC contract number D12PC000337, the US Government is authorized to reproduce and distribute reprints of this work for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the US Government. ; Unpublished conference paper