【Abstract】 Objective To study the relationships of meteorological factors, animal host and hemorrhagic fever with renal syndrome （HFRS） incidence, and construct mathematical model for the forecast of HFRS. Methods Firstly, air pressure, air temperature, relative humidity, precipitation, sunshine duration and sunshine percentage were selected from all meteorological factors of Huludao city. Secondly, Pearson, Kendall and Spearman correlation analyses were used to describe the relationships among meteorological factors, animal host situation including rodent density and viral carriage of rodents and HFRS incidence. Thirdly, Bayesian discrimination analysis (BDA) was adopted to forecast HFRS incidence on the premise of meteorological factors and animal host formation as explanatory variables. Results There was the close relation between rodent density and annual HRFS incidence(r＝0.738, P＝0.000), and the rodent density was also influenced by sunshine duration, sunshine percentage and precipitation.  A positive correlation was found between rodent density and sunshine time(r＝0.494, P＝0.016), and the correlation between rodent density and precipitation was negative(r＝-0.350, P＝0.101). The step wise BDA and all variables discrimination analysis had all good effect on the forecasting of HFRS based on meteorological factors and animal host data. The accuracy rate of fitting and leave?one?out (LOO) cross-validation of stepwise BDA all reached 82.6%(19/23) , however, that of  fitting of all variables BDA was 90.9%(20/22) and 81.8%(18/22) for LOO cross-validation. For next year incidence prediction, the accuracy rates of fitting and LOO cross-validation step-wise were all 86.4%(19/22) for step-wise BDA, while for all variables BDA, its accuracy rate of fitting was 100%(21/21) and that of LOO cross-validation was 57.1%(12/21). Conclusion HFRS incidence was related to animal epidemic situation which was influenced by meteorological factors. Stepwise BDA offered useful information in the discrimination and forecasting of HFRS incidence, which had a good application in the future.