A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan

摘要: Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of most serious vector-borne infectious diseases in tropical and sub-tropical areas. During 2007, particular, there were over 2,000 DF cases Taiwan, which was highest number recorded history Taiwan epidemics. Most studies have focused mainly on temporal patterns its close association with climatic covariates, whereas they understated spatial (spatial dependence clustering) composite space–time effects. The present study proposes a spatio-temporal prediction approach based stochastic Bayesian Maximum Entropy (BME) analysis. Core site-specific knowledge bases are considered, including climate health datasets under conditions uncertainty, functions, Poisson regression model variables contributing to occurrences southern during 2007. results show that outbreaks area highly influenced conditions. Furthermore, analysis can provide required “one-week-ahead” outbreak warnings predictions distributions. Therefore, proposed Disease Control Agency valuable tool timely identify, control, even efficiently prevent spreading across space–time.