HFMD Cases Prediction Using Transfer One-Step-Ahead Learning

Hand, foot and mouth disease (HFMD) is a susceptible viral infectious disease to infants and children, which led to millions of cases and hundreds of deaths annually in China. Existing predictive methods commonly learn the development patterns from historical observations. However, almost all these methods are neglect the immediate impact of exogenous factors on HFMD transmission. To solve the limitation, we consider the approximately unidirectional influences from temperature to confirmed cases and then propose a transfer one-step-ahead learning (Tr-OSH) method to establish their association. The Tr-OSH method first extract the unidirectional representation from temperature observations, and subsequently transfer the obtained representation for HFMD cases prediction. Moreover, we notice the independent correlation of each time step and period, and generate the independent representation by the relevance to upcoming values. Intensive experiments on real-world HFMD datasets demonstrate that our Tr-OSH method much efficaciously improves prediction accuracy.