Prediction of hot spot areas of hemorrhagic fever with renal syndrome in Hunan Province based on an information quantity model and logistical regression model

Background China's "13(th) 5-Year Plan" (2016-2020) for the prevention and control of sudden acute infectious diseases emphasizes that epidemic monitoring and epidemic focus surveys in key areas are crucial for strengthening national epidemic prevention and building control capacity. Establishing an epidemic hot spot areas and prediction model is an effective means of accurate epidemic monitoring and surveying. Objective: This study predicted hemorrhagic fever with renal syndrome (HFRS) epidemic hot spot areas, based on multi-source environmental variable factors. We calculated the contribution weight of each environmental factor to the morbidity risk, obtained the spatial probability distribution of HFRS risk areas within the study region, and detected and extracted epidemic hot spots, to guide accurate epidemic monitoring as well as prevention and control. Methods: We collected spatial HFRS data, as well as data on various types of natural and human social activity environments in Hunan Province from 2010 to 2014. Using the information quantity method and logistic regression modeling, we constructed a risk-area-prediction model reflecting the epidemic intensity and spatial distribution of HFRS. Results: The areas under the receiver operating characteristic curve of training samples and test samples were 0.840 and 0.816. From 2015 to 2019, HRFS case site verification showed that more than 82% of the cases occurred in high-risk areas. Discussion This research method could accurately predict HFRS hot spot areas and provided an evaluation model for Hunan Province. Therefore, this method could accurately detect HFRS epidemic high-risk areas, and effectively guide epidemic monitoring and surveyance. Author summary Hunan, the main epidemic area of HRFS in China. Hunan has had a cumulative incidence of 117,000 cases since 1963. During this time Hunan experienced two high incidence periods in the 1980s and 1990s. We used an Information quantity + Logistic regression model (I+LR model) to predict high-incidence and potential epidemic HFRS areas. Normalized difference vegetation index(NDVI)contributed most to HFRS risk. Per capita GDP, population size, land-use type, rainfall, elevation, and soil type were all factors found to influence HFRS risk. Our study is useful for risk prediction, prevention, and control of HFRS.