title: Inferring disease transmission networks at a metapopulation level.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  A network inference method called NetEpi (Network Epidemic) is developed and evaluated using both synthetic and real-world datasets. The experimental results show that NetEpi can not only recover most of the ground-truth disease transmission networks using only surveillance data, but also find a malaria transmission network based on a real-world dataset. The inferred malaria network can characterize the real-world observations to a certain extent. In addition, it also discloses some hidden phenomenon.
  
  
  CONCLUSIONS
  This research addresses the problem of inferring disease transmission networks at a metapopulation level. Such networks can be useful in several ways: (i) to investigate hidden impact factors that influence epidemic dynamics, (ii) to reveal possible sources of epidemic outbreaks, and (iii) to practically develop and/or improve strategies for controlling the spread of infectious diseases.
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China.
authors.forename: Xiaofei
authors.initials: X
authors.lastname: Yang
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China.
authors.forename: Jiming
authors.initials: J
authors.lastname: Liu
authors.affiliation: National Institute of Parasitic Diseases, China CDC, Shanghai, China.
authors.forename: Xiao-Nong
authors.initials: XN
authors.lastname: Zhou
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China.
authors.forename: William Kw
authors.initials: WK
authors.lastname: Cheung
keywords: Bayesian learning; Disease transmission networks; Metapopulation; Network inference
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