Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks

Abstract

Since the 21st century, the global outbreaks of infectious diseases such as SARS in 2003, H1N1 in 2009, and H7N9 in 2013, have become the critical threat to the public health and a hunting nightmare to the government. Understanding the propagation in large-scale metapopulations and predicting the future outbreaks thus become crucially important for epidemic control and prevention. In the literature, there have been a bulk of studies on modeling intra-city epidemic propagation but with the single population assumption (homogeneity). Some recent works on metapopulation propagation, however, focus on finding specific human mobility physical networks to approximate diseases transmission networks, whose generality to fit different diseases cannot be guaranteed. In this paper, we argue that the intra-city epidemic propagation should be modeled on a metapopulation base, and propose a two-step method for this purpose. The first step is to understand the propagation system by inferring the underlying disease infection network. To this end, we propose a novel network inference model called D2PRI, which reduces the individual network into a sub-population network without information loss, and incorporates the power-law distribution prior and data prior for better performance. The second step is to predict the disease propagation by extending the classic SIR model to a metapopulation SIR model that allows visitors transmission between any two sub-populations. The validity of our model is testified on a real-life clinical report data set about the airborne disease in the Shenzhen city, China. The D2PRI model with the extended SIR model exhibit superior performance in various tasks including network inference, infection prediction and outbreaks simulation.