Work

Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors

Year: 2025

Type: article

Abstract: Objective: species), is the most common vector-borne disease in the United States. Its spatiotemporal dynamics are influenced by environmental and socioeconomic factors, yet the impacts of the COVID-19 pandemic on Lyme disease remain unclear. Methods: We analyzed county-level Lyme disease surveillance data (2001-2022) alongside environmental, socioeconomic, and tick vector data. Using machine learning models (Random Forest, Boosted Regression Trees, and XGBoost) and Shapley Additive Explanations (SHAP), we evaluated the influence of key predictors on Lyme disease risk. Predicted cases for 2020-2022 were compared with actual reports to assess the pandemic's effects. Results: , and maximum temperature were presented as the key predictors of disease risk. The COVID-19 pandemic severely disrupted reporting dynamics, with 2020 and 2021 cases falling 43.9 % (95 % CI: 41.2-46.7 %) and 22.0 % (95 % CI: 19.5-24.5 %) below predictions, respectively-a decline most pronounced in the Northeast and linked to reduced healthcare access and outdoor activity during lockdowns. Conclusion: Our findings highlight the complex interactions of environmental, socioeconomic, and behavioral factors in Lyme disease dynamics, including the significant impact of the COVID-19 pandemic on disease reporting. These insights underscore the need for integrated, data-driven public health strategies to mitigate Lyme disease risk in the United States.

Source: One Health

DOI: 10.1016/j.onehlt.2025.101111