title: Mining geographic variations of Plasmodium vivax for active surveillance: a case study in China.
abstract: BACKGROUND
  Geographic variations of an infectious disease characterize the spatial differentiation of disease incidences caused by various impact factors, such as environmental, demographic, and socioeconomic factors. Some factors may directly determine the force of infection of the disease (namely, explicit factors), while many other factors may indirectly affect the number of disease incidences via certain unmeasurable processes (namely, implicit factors). In this study, the impact of heterogeneous factors on geographic variations of Plasmodium vivax incidences is systematically investigate in Tengchong, Yunnan province, China.
  
  
  METHODS
  A space-time model that resembles a P. vivax transmission model and a hidden time-dependent process, is presented by taking into consideration both explicit and implicit factors. Specifically, the transmission model is built upon relevant demographic, environmental, and biophysical factors to describe the local infections of P. vivax. While the hidden time-dependent process is assessed by several socioeconomic factors to account for the imported cases of P. vivax. To quantitatively assess the impact of heterogeneous factors on geographic variations of P. vivax infections, a Markov chain Monte Carlo (MCMC) simulation method is developed to estimate the model parameters by fitting the space-time model to the reported spatial-temporal disease incidences.
  
  
  RESULTS
  Since there is no ground-truth information available, the performance of the MCMC method is first evaluated against a synthetic dataset. The results show that the model parameters can be well estimated using the proposed MCMC method. Then, the proposed model is applied to investigate the geographic variations of P. vivax incidences among all 18 towns in Tengchong, Yunnan province, China. Based on the geographic variations, the 18 towns can be further classify into five groups with similar socioeconomic causality for P. vivax incidences.
  
  
  CONCLUSIONS
  Although this study focuses mainly on the transmission of P. vivax, the proposed space-time model is general and can readily be extended to investigate geographic variations of other diseases. Practically, such a computational model will offer new insights into active surveillance and strategic planning for disease surveillance and control.
authors.affiliation: School of Information Engineering, Nanjing University of Finance & Economics, Wenyuan Road, Nanjing, 210003, China. benyunshi@outlook.com.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China. benyunshi@outlook.com.
authors.forename: Benyun
authors.initials: B
authors.lastname: Shi
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong. csqtan@comp.hkbu.edu.hk.
authors.forename: Qi
authors.initials: Q
authors.lastname: Tan
authors.affiliation: National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Center for Malaria, Schistosomiasis and Filariasis, Shanghai, 200025, China. ipdzhouxn@sh163.net.
authors.forename: Xiao-Nong
authors.initials: XN
authors.lastname: Zhou
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong. jiming@comp.hkbu.edu.hk.
authors.forename: Jiming
authors.initials: J
authors.lastname: Liu
mesh_terms: D002681:China / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D016780:Malaria, Vivax / Q000453:epidemiology* / Q000469:parasitology / Q000635:transmission; D008962:Models, Theoretical; D010966:Plasmodium vivax / Q000502:physiology*