Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors
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.
Authors
- Hong‐Han Ge (ORCID: https://orcid.org/0009-0004-0368-4599)
- Jieyun Yin (ORCID: https://orcid.org/0000-0002-5265-3930)
- Rui-Fang Shi
- Yan-Qun Sun
- Xu Wang (ORCID: https://orcid.org/0000-0002-1708-9086)
- Xiaoyan Zhu (ORCID: https://orcid.org/0000-0002-7173-7793)
- Tian Ma (ORCID: https://orcid.org/0000-0002-1580-7363)
- Ye Tian (ORCID: https://orcid.org/0000-0002-4156-3200)
- Wei Li (ORCID: https://orcid.org/0000-0003-0887-3116)
- Tianxiang Fan (ORCID: https://orcid.org/0000-0003-3781-924X)
Institutions
- Yale University (US)
- Soochow University (CN)
- Suzhou Municipal Hospital (CN)
- Yancheng Second People's Hospital (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- One Health
- Published
- 2025-06-13
- DOI
- https://doi.org/10.1016/j.onehlt.2025.101111
- Citations
- 1
- Primary Topic
- Vector-borne infectious diseases
- Type
- article
- Field-Weighted Citation Impact
- 2.05
Funders
- Nanjing Medical University
- Medical Science and Technology Development Foundation, Nanjing Municipality Health Bureau