  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 D$^2$PRI, 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
D$^2$PRI model with the extended SIR model exhibit superior performance in
various tasks including network inference, infection prediction and outbreaks
simulation.
