Animal Reservoir, Natural and Socioeconomic Variations and the Transmission of Hemorrhagic Fever with Renal Syndrome in Chenzhou, China, 2006–2010

Background: China has the highest incidence of hemorrhagic fever with renal syndrome (HFRS) worldwide. Reported cases account for 90% of the total number of global cases. By 2010, approximately 1.4 million HFRS cases had been reported in China. This study aimed to explore the effect of the rodent reservoir, and natural and socioeconomic variables, on the transmission pattern of HFRS. Methodology/Principal Findings: Data on monthly HFRS cases were collected from 2006 to 2010. Dynamic rodent monitoring data, normalized difference vegetation index (NDVI) data, climate data, and socioeconomic data were also obtained. Principal component analysis was performed, and the time-lag relationships between the extracted principal components and HFRS cases were analyzed. Polynomial distributed lag (PDL) models were used to fit and forecast HFRS transmission. Four principal components were extracted. Component 1 (F1) represented rodent density, the NDVI, and monthly average temperature. Component 2 (F2) represented monthly average rainfall and monthly average relative humidity. Component 3 (F3) represented rodent density and monthly average relative humidity. The last component (F4) represented gross domestic product and the urbanization rate. F2, F3, and F4 were significantly correlated, with the monthly HFRS incidence with lags of 4 months (r = −0.289, P 46 000 people died from HFRS, and the fatality rate was 3.29%. A great deal of interest and excitement has developed recently for understanding the role of the environment in the transmission of HFRS. Our article provides evidence that rodent density and behavior depend on natural factors. Changes in animal reservoirs may lead to the emergence of new epidemics and threats to human health. However, economic development may promote a more residential environment, which could inhibit disease transmission from animals to humans by limiting their contact. We combined data about the rodent reservoir, the natural environment, and socioeconomic factors in the model. The results will be helpful for making and prioritizing preventive measures.