Meteorological factors-based spatio-temporal mapping and predicting malaria in central China.

Despite significant reductions in the overall burden of malaria in the 20th century, this disease still represents a significant public health problem in China, especially in central areas. Understanding the spatio-temporal distribution of malaria is essential in the planning and implementing of effective control measures. In this study, normalized meteorological factors were incorporated in spatio-temporal models. Seven models were established in WinBUGS software by using Bayesian hierarchical models and Markov Chain Monte Carlo methods. M₁, M₂, and M₃ modeled separate meteorological factors, and M₃, which modeled rainfall performed better than M₁ and M₂, which modeled average temperature and relative humidity, respectively. M₇ was the best fitting models on the basis of based on deviance information criterion and predicting errors. The results showed that the way rainfall influencing malaria incidence was different from other factors, which could be interpreted as rainfall having a greater influence than other factors.