Spatial prediction of animal plague risk in Qinghai Province, China using MaxEnt modeling: implications for targeted control.

Background: Plague, as a highly infectious and fatal zoonotic disease, has shown a resurgence in multiple regions worldwide in recent years. Qinghai Province, a significant natural plague focus of the plateau type in China, has experienced periodic outbreaks. However, existing research has primarily focused on the spatial prediction of marmot-suitable habitats, with relatively few studies targeting the spatial prediction of animal plague risk zones. Methods: Data on animal plague occurrence sites in Qinghai Province from 1956 to 2022 were collected. Terrain features, bioclimatic variables, land use types, and human disturbance variables related to plague spatial distribution were extracted. A maximum entropy (MaxEnt) model was used to construct a spatial prediction model for animal plague risk zones, and The model's performance was evaluated using the average Area Under the Curve (AUC) and True Skill Statistic (TSS) values on the testing datasets. Based on the model results, the animal plague risk zones were delineated, and the area size and population residing within predicted risk zones were calculated. Results: The Maxent model achieved average AUC and TSS values of 0.827 and 0.5, respectively, on the testing datasets. The predicted animal plague risk zones in Qinghai Province covers 234,000 km Conclusion: This study utilized the MaxEnt model combined with GIS spatial analysis technology to predict the spatial distribution of animal plague risk zones in Qinghai Province and estimate the area size and population residing within predicted risk zones. The findings can provide a scientific basis and decision-making support for targeted plague prevention and control in Qinghai.