Predicting future tropical theileriosis risk in China using tick distribution and climate models.

Tropical theileriosis, caused by Theileria annulata, is a tick-borne protozoan disease with high morbidity and mortality rates. While climate change and expanding livestock trade may increase transmission risks, the spatial distribution dynamics of tropical theileriosis in China under future climate scenarios remain poorly understood. This study aimed to predict the future distribution dynamics of tropical theileriosis in China by integrating climate variables and tick vector distribution data. We developed two MaxEnt-based prediction frameworks: one incorporating 19 climatic variables alone, and another combining these with distribution data of four major tick vectors (H. scupense, H. anatolicum, H. detritum, and H. asiaticum). Risk zones were projected across three periods (2021-2041, 2041-2060, and 2061-2080) under three climate scenarios (SSP126, SSP245, and SSP585). Model performance was evaluated using the Area Under Curve (AUC) metric. The results showed that integration of tick vector distribution data improved model prediction accuracy (AUC: 0.874-0.882). Current high-risk zones, predominantly in central and northwestern China, showed strong correlation with H. detritum distribution. Under future climate scenarios, the model projected a contraction of tick vector habitable areas and disease risk zones. The most substantial reduction (14.39 %) was predicted for 2061-2080 under the SSP126 scenario. This study provides a systematic assessment of tropical theileriosis risk dynamics in China under climate change scenarios. The improved prediction accuracy achieved through vector distribution integration emphasizes the importance of combining vector ecology with climate data in disease risk modeling. These findings support the development of targeted prevention strategies that account for both vector distribution patterns and regional climate characteristics. Future disease management planning should prioritize vector surveillance and formulate relevant policies to effectively reduce the risk of disease transmission.