title: Mapping and predicting malaria transmission in the People's Republic of China, using integrated biology-driven and statistical models.
abstract: The purpose of this study was to deepen our understanding of Plasmodium vivax malaria transmission patterns in the People's Republic of China (P.R. China). An integrated modeling approach was employed, combining biological and statistical models. A Delphi approach was used to determine environmental factors that govern malaria transmission. Key factors identified (i.e. temperature, rainfall and relative humidity) were utilized for subsequent mapping and modeling purposes. Yearly growing degree days, annual rainfall and effective yearly relative humidity were extracted from a 15-year time series (1981-1995) of daily environmental data readily available for 676 locations in P.R. China. A suite of eight multinomial regression models, ranging from the null model to a fully saturated one were constructed. Two different information criteria were used for model ranking, namely the corrected Akaike's information criterion and the Bayesian information criterion. Mapping was based on model output data, facilitated by using ArcGIS software. Temperature was found to be the most important environmental factor, followed by rainfall and relative humidity in the Delphi evaluation. However, relative humidity was found to be more important than rainfall and temperature in the ranking list according to the three single environmental factor regression models. We conclude that the distribution of the mosquito vector is mainly related to relative humidity, which thus determines the extent of malaria transmission. However, in regions with relative humidity >60%, temperature is the major driver of malaria transmission intensity. By integrating biology-driven models with statistical regression models, reliable risk maps indicating the distribution of transmission and the intensity can be produced. In a next step, we propose to integrate social and health systems factors into our modeling approach, which should provide a platform for rigorous surveillance and monitoring progress towards P. vivax malaria elimination in P.R. China.
authors.affiliation: Jiangsu Institute of Parasitic Diseases, Meiyuan Yangxiang 117, Wuxi 214064, People's Republic of China.
authors.forename: Guo-Jing
authors.initials: GJ
authors.lastname: Yang
authors.forename: Qi
authors.initials: Q
authors.lastname: Gao
authors.forename: Shui-Sen
authors.initials: SS
authors.lastname: Zhou
authors.forename: John B
authors.initials: JB
authors.lastname: Malone
authors.forename: Jennifer C
authors.initials: JC
authors.lastname: McCarroll
authors.forename: Marcel
authors.initials: M
authors.lastname: Tanner
authors.forename: Penelope
authors.initials: P
authors.lastname: Vounatsou
authors.forename: Robert
authors.initials: R
authors.lastname: Bergquist
authors.forename: Jürg
authors.initials: J
authors.lastname: Utzinger
authors.forename: Xiao-Nong
authors.initials: XN
authors.lastname: Zhou
mesh_terms: D001499:Bayes Theorem; D002681:China / Q000453:epidemiology; D002980:Climate; D003697:Delphi Technique; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D040362:Geographic Information Systems; D006801:Humans; D006813:Humidity; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D016780:Malaria, Vivax / Q000453:epidemiology / Q000635:transmission*; D015233:Models, Statistical; D015999:Multivariate Analysis; D016017:Odds Ratio; D011891:Rain; D018570:Risk Assessment / Q000379:methods; D011795:Surveys and Questionnaires