Abstract: Objective To explore the application of artificial neural network approach to forecasting the incident rate of hemorrhagic fever with renal syndrome (HFRS) in Shanghai.Methods Approaches of generalized regression neural network (GRNN) and back propagation (BP) neural network were chosen in the study.The HFRS surveillance data on Shanghai population sero-positivity rate of HFRS antibody,and on density and infection rate of host animal plus meteorological data on Shanghai were treated as training samples，and epidemic trend of hemorrhagic fever with renal syndrome was forecasted.Results Two artificial neural network methods integrated all kinds of surveillance data on hemorrhagic fever with renal syndrome in Shanghai with meteorological data on fitting and forecasting HFRS incidence.GRNN neural network in fitting and prediction was better than BP neural network.Conclusion Artificial neural network methods are useful and effective in forecasting the incidence of HFRS in Shanghai, which may remain low in the future.