中文摘要：

目的研究肾综合征出血热（HFRS）发病与气象因素和动物宿主的关系并建立合理的数学预报模型。方法首先选取逐月及逐年的气象指标，包括气压、气温、降雨量、相对湿度、日照时数和日照百分率作为代表因素；然后对HFRS与气象因素和动物宿主间的关系进行Pearson、Kendall及Spearman相关分析，最后利用气象因素和包括鼠密度及鼠带病毒率的动物宿主信息作为解释变量进行Bayes判别分析。结果HFRS年发病疫情与鼠密度关系最为密切（r=0．738，P=0．000），而影响鼠密度最显著的气象因素是日照时数、日照百分率和降雨量。其中日照时数与鼠密度呈正相关（r=0．494，P=0．016），而降雨量与鼠密度近似呈负相关（r=-0．350，P=0．101）。利用气象及动物宿主资料预测当年的人间发病强度时，逐步判别分析及全变量判别分析均具有良好的效果。逐步判别分析的组内回代及弃一交叉验证准确率均为82．6％（19／23），而全变量判别分析的组内回代准确率为90．9％（20／22），弃-交叉验证准确率为81．8％（18／22）。当预测下一年的发病强度时，逐步判别分析的组内回代及弃一验证正确率均为86．4％（19／22），而全变量判别分析的组内回代分类正确率为100％（21／21），弃-交叉验证分类正确率仅为57．1％（12／21）。结论气象因素影响动物繁殖及动物间疫情，进而影响人间的HFRS疟情．Bayes诼彤判别分析在预测HFRS疫情方面具有一定实际应用价值。

英文摘要：

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 stcpwise 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. Stcpwise BDA offered useful information in the discr