title: Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.
abstract: BACKGROUND
  The transmission networks of Plasmodium vivax characterize how the parasite transmits from one location to another, which are informative and insightful for public health policy makers to accurately predict the patterns of its geographical spread. However, such networks are not apparent from surveillance data because P. vivax transmission can be affected by many factors, such as the biological characteristics of mosquitoes and the mobility of human beings. Here, we pay special attention to the problem of how to infer the underlying transmission networks of P. vivax based on available tempo-spatial patterns of reported cases.
  
  
  METHODS
  We first define a spatial transmission model, which involves representing both the heterogeneous transmission potential of P. vivax at individual locations and the mobility of infected populations among different locations. Based on the proposed transmission model, we further introduce a recurrent neural network model to infer the transmission networks from surveillance data. Specifically, in this model, we take into account multiple real-world factors, including the length of P. vivax incubation period, the impact of malaria control at different locations, and the total number of imported cases.
  
  
  RESULTS
  We implement our proposed models by focusing on the P. vivax transmission among 62 towns in Yunnan province, People's Republic China, which have been experiencing high malaria transmission in the past years. By conducting scenario analysis with respect to different numbers of imported cases, we can (i) infer the underlying P. vivax transmission networks, (ii) estimate the number of imported cases for each individual town, and (iii) quantify the roles of individual towns in the geographical spread of P. vivax.
  
  
  CONCLUSIONS
  The demonstrated models have presented a general means for inferring the underlying transmission networks from surveillance data. The inferred networks will offer new insights into how to improve the predictability of P. vivax transmission.
authors: Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
authors: Benyun
authors: B
authors: Shi
authors: Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
authors: Jiming
authors: J
authors: Liu
authors: National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China ; Key Laboratory of Parasite and Vector Biology, MOH, Shanghai, China ; WHO Collaborating Center for Malaria, Schistosomiasis and Filariasis, Shanghai, China.
authors: Xiao-Nong
authors: XN
authors: Zhou
authors: Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu, China.
authors: Guo-Jing
authors: GJ
authors: Yang
mesh_terms: D000818:Animals; D000852:Anopheles / Q000821:virology; D002681:China / Q000453:epidemiology; D062306:Geography, Medical; D006801:Humans; D016780:Malaria, Vivax / Q000453:epidemiology* / Q000635:transmission*; D008954:Models, Biological; D010966:Plasmodium vivax; D062211:Spatio-Temporal Analysis