Correlation analysis and regression model of epidemic factors of hemorrhagic fever with renal syndrome in Heihe city, Heilongjiang Province.

Objective The study was conducted to investigate the correlation between meteorological factors and rat (the main host) density and the incidence rate of hemorrhagic fever with renal syndrome (HFRS) in Heihe city, Heilongjiang province, and establish the regression model. Methods HFRS cases, meteorological data (maximum temperature, minimum temperature, mean temperature, mean relative humidity and rainfall), rat density (Apodemus agrarius and Rattus norvegicus) in Heihe city from 1984 to 2007 were collected. The statistical analysis was performed using Spearman rank correlation and multiple-stepwise regression. Results The analysis showed significant correlation between such variables as mean temperature, mean relative humidity and rainfall in the past six months and the incidence rate of HFRS in the current month in Heihe city, of which the meteorological factors three months ago, four months ago and five months ago (the last quarter) were shown to be of the highest positive correlation coefficient with the incidence rate of HFRS. The rat (main host) density was significantly and positively correlated with the incidence rate of HFRS, and the highest correlation was observed in the data one months ago, two months ago and three months ago. According to the merit-based largest coefficient of determination, the regression model was established as follows: y =-11.359+0.08x6+0.27n1+0.221n2 +0.175x2 (where: y was the incidence rate of HFRS; x6 was the rainfall four months ago; n1 was the host density one month ago; n2 was the host density two months ago; x2 was the mean relative humidity three months ago). The coefficient of determination (R2) was 0.709, the difference being statistically significant (F= 34.703, P= 0.000). Conclusion The regression model was fitting favorably, indicating that it is applicable to perform predictive analysis of the epidemic trend of HFRS based on the combination of meteorological factors and host density data in the region.