Work

High-resolution prediction and risk mapping of Haemaphysalis longicornis habitat suitability in the Jiaodong Peninsula, China

Year: 2026

Type: article

Abstract: Ticks are vectors of numerous human pathogens, and accurate predictions of their spatial distribution are essential for the effective prevention and control of tick-borne diseases. High-resolution predictions remain scarce in China. To address this gap, a tick survey was conducted across the Jiaodong Peninsula at 2,647 points during the summer of 2024, integrating high-resolution environmental variables. Seven machine-learning algorithms were used, and ensemble learning was applied, resulting in a habitat-suitability map for Haemaphysalis longicornis at a 10-meter resolution. The ensemble model showed robust predictive accuracy (AUC = 0.8355). Summer normalized difference vegetation index was the primary environmental driver, followed by land use and land cover and winter normalized difference vegetation index. High risk villages accounted for 70.0%, 40.2%, and 16.9% of rural, urban rural fringe, and urban areas, respectively. Ensemble machine-learning provides a robust approach, revealing how ecological factors and human activities shape distribution patterns and risk heterogeneity.

Source: iScience

DOI: 10.1016/j.isci.2026.117723