An online spatiotemporal prediction model for dengue fever epidemic in Kaohsiung (Taiwan)

摘要: The emergence and re-emergence of disease epidemics is a complex question that may be influenced by diverse factors, including the space-time dynamics human populations, environmental conditions, associated uncertainties. This study proposes stochastic framework to integrate in form Susceptible-Infected-Recovered (SIR) model, together with uncertain observations, into Bayesian maximum entropy (BME) framework. resulting model (BME-SIR) can used predict spread. Specifically, it was applied obtain prediction dengue fever (DF) epidemic took place Kaohsiung City (Taiwan) during 2002. In implementing SIR parameters were continually updated information on new cases infection incorporated. results obtained show proposed rigorous user-specified initial values unknown parameters, is, transmission recovery rates. general, this provides good characterization spatial diffusion DF epidemic, especially city districts proximal location outbreak. Prediction performance affected various such as virus serotypes intervention, which change diffusion. BME-SIR provide government agencies valuable reference for timely identification, control, prevention spread space time.