Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors
Objective: Lyme disease, caused by Borrelia burgdorferi and transmitted by blacklegged ticks (Ixodes 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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | One Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352771425001478 |
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| author | Yan-Qun Sun Xiao-Yan Zhu Tian-Ci Fan Tian Ma Hong-Han Ge Rui-Fang Shi Xu Wang Wei Li Jie-Yun Yin Ye Tian |
| author_facet | Yan-Qun Sun Xiao-Yan Zhu Tian-Ci Fan Tian Ma Hong-Han Ge Rui-Fang Shi Xu Wang Wei Li Jie-Yun Yin Ye Tian |
| author_sort | Yan-Qun Sun |
| collection | DOAJ |
| description | Objective: Lyme disease, caused by Borrelia burgdorferi and transmitted by blacklegged ticks (Ixodes 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: Lyme disease cases rose from 16,862 in 2001 to 61,802 in 2022, with geographic expansion into southeastern regions. Population density, ecological niche of I. scapularis, 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. |
| format | Article |
| id | doaj-art-2cc22b2628914edf8b93af640bd77a71 |
| institution | DOAJ |
| issn | 2352-7714 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | One Health |
| spelling | doaj-art-2cc22b2628914edf8b93af640bd77a712025-08-20T03:24:03ZengElsevierOne Health2352-77142025-12-012110111110.1016/j.onehlt.2025.101111Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factorsYan-Qun Sun0Xiao-Yan Zhu1Tian-Ci Fan2Tian Ma3Hong-Han Ge4Rui-Fang Shi5Xu Wang6Wei Li7Jie-Yun Yin8Ye Tian9Children's Hospital of Nanjing Medical University, Nanjing, China; Correspondence to: Yan-Qun Sun, Ye Tian, Children's Hospital of Nanjing Medical University, 72 Guangzhou Road, Nanjing 210008, PR China.Suzhou Municipal Center for Disease Control and Prevention, Suzhou, ChinaChildren's Hospital of Nanjing Medical University, Nanjing, ChinaYale Institute for Biospheric Studies, Yale University, New Haven, CT, United States; School of the Environment, Yale University, New Haven, CT, United StatesSchool of Public Health and Health Management, Shandong, Shandong First Medical University, Jinan, ChinaThe Affiliated Yancheng Maternity&Child Health Hospital of Yangzhou University Medical School, Suzhou, ChinaChildren's Hospital of Nanjing Medical University, Nanjing, ChinaChildren's Hospital of Nanjing Medical University, Nanjing, ChinaSchool of Public Health, Medical College of Soochow University, Suzhou, China; Correspondence to: Jie-Yun Yin, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China.Children's Hospital of Nanjing Medical University, Nanjing, China; Correspondence to: Yan-Qun Sun, Ye Tian, Children's Hospital of Nanjing Medical University, 72 Guangzhou Road, Nanjing 210008, PR China.Objective: Lyme disease, caused by Borrelia burgdorferi and transmitted by blacklegged ticks (Ixodes 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: Lyme disease cases rose from 16,862 in 2001 to 61,802 in 2022, with geographic expansion into southeastern regions. Population density, ecological niche of I. scapularis, 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.http://www.sciencedirect.com/science/article/pii/S2352771425001478Lyme diseaseBlacklegged ticksCOVID-19 pandemicMachine learning |
| spellingShingle | Yan-Qun Sun Xiao-Yan Zhu Tian-Ci Fan Tian Ma Hong-Han Ge Rui-Fang Shi Xu Wang Wei Li Jie-Yun Yin Ye Tian Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors One Health Lyme disease Blacklegged ticks COVID-19 pandemic Machine learning |
| title | Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors |
| title_full | Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors |
| title_fullStr | Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors |
| title_full_unstemmed | Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors |
| title_short | Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors |
| title_sort | projecting lyme disease risk in the united states a machine learning approach integrating environmental socioeconomic and vector factors |
| topic | Lyme disease Blacklegged ticks COVID-19 pandemic Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352771425001478 |
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