Inferring spatial transmission of epidemics in networked metapopulations

To uncover the process of spatial invasion of infectious diseases, it is vital to reconstruct the epidemic invasion pathways among subpopulations, which is the central interest of this work. The process inference is a challenging problem due to the complexity of spreading process on networked metapopulation. With the epidemic arrival time (EAT) infected series of each subpopulation, an invasion pathway inference algorithm is proposed based on probability theory and combinatorial mathematics. Simulation results on Barabási-Albert (BA) networked metapopulation are presented to verify the satisfactory performance of the proposed inference algorithm.