Inferring disease transmission networks at a metapopulation level
To investigate transmission patterns of an infectious disease, e.g., malaria, it is desirable to use the observed surveillance data to discover the underlying (often hidden) disease transmission networks. Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person. However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.
disease transmission networks metapopulation network inference bayesian learning