Current and Future Risk Distribution of Visceral Leishmaniasis in Shaanxi Province, China: An Ecological Niche Modeling Approach

<b><i>Background:</i></b> Visceral leishmaniasis (VL) is a vector-borne zoonotic disease that remains endemic and poses an ongoing public health concern in western and central China. In recent years, several areas in Shaanxi Province have reported a resurgence of VL. This highlights the need for spatial risk assessments under current and future climatic conditions. <b><i>Methods:</i></b> This study employed the maximum entropy (MaxEnt) ecological niche modeling approach to predict the potential distribution of VL in Shaanxi Province. Historical VL case report data from 2005 to 2024 were integrated with environmental, climatic, and socioeconomic variables to identify key contributing factors and assess both current risk areas and projected future VL distributions under four Shared Socioeconomic Pathways (SSPs) across four future periods in the model. <b><i>Results:</i></b> The MaxEnt model showed good performance, with the area under the receiver operating characteristic curve values of 0.902 for the training data and 0.862 for the testing data. The most important contributing factors were annual mean temperature, mean temperature of the warmest quarter, and gross domestic product to VL suitability distribution. Under the current climatic conditions, the potential risk area accounted for approximately 25.28% of Shaanxi Province, with 0.97% classified as high-risk regions. These risk areas were primarily located in Hancheng, Weinan, and Shangluo City, Shaanxi Province. Under future climate scenarios, especially the high-emission pathway, high-risk regions are projected to expand significantly, particularly in northern Shaanxi. <b><i>Conclusions:</i></b> This study identified the influence of climatic, environmental, and socioeconomic factors on the distribution of VL in Shaanxi Province and revealed the substantial expansion of ecologically suitable areas under future climate change. The risk maps and ecological insights generated in the study can assist public health authorities in optimizing vector surveillance, region-specific surveillance, early warning, and targeted control efforts, particularly in the context of climate change.