A Data-driven inference algorithm for epidemic pathways using surveillance reports in 2009 outbreak of influenza A (H1N1)

In this paper, we propose an epidemiological infective-hospitalized (IH) model and adopt a heuristic algorithm to predict the transition of infective individuals, which optimizes, at the metapopulation level, the IH model´s approximation to the surveillance reports of (cumulative) laboratory confirmed cases. Applying to the data of the 2009 outbreak of a new strain of influenza A (H1N1) in the United States, we obtain the invasion tree along which the virus spreads from the source state reporting the first confirmed case to infect other states. Basically, the surveillance-data-based inference of invasion tree agrees with real epidemic pathways observed in outbreaks of influenza A (H1N1), which verifies the validity of our heuristic inference algorithm.