Sun Yan-Qun, Zhu Xiao-Yan, Fan Tian-Ci, Ma Tian, Ge Hong-Han, Shi Rui-Fang, Wang Xu, Li Wei, Yin Jie-Yun, Tian Ye
Children's Hospital of Nanjing Medical University, Nanjing, China.
Suzhou Municipal Center for Disease Control and Prevention, Suzhou, China.
One Health. 2025 Jun 13;21:101111. doi: 10.1016/j.onehlt.2025.101111. eCollection 2025 Dec.
Lyme disease, caused by and transmitted by blacklegged ticks ( 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.
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.
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 , 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.
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.
莱姆病由黑腿蜱（ 物种）引起并通过其传播，是美国最常见的媒介传播疾病。其时空动态受环境和社会经济因素影响，但新冠疫情对莱姆病的影响仍不明确。
我们分析了县级莱姆病监测数据（2001 - 2022年）以及环境、社会经济和蜱虫媒介数据。使用机器学习模型（随机森林、增强回归树和XGBoost）和夏普利值附加解释（SHAP），我们评估了关键预测因素对莱姆病风险的影响。将2020 - 2022年的预测病例与实际报告进行比较，以评估疫情的影响。
莱姆病病例从2001年的16,862例增至2022年的61,802例，地理范围扩展到东南部地区。人口密度、 的生态位和最高温度是疾病风险的关键预测因素。新冠疫情严重扰乱了报告动态，2020年和2021年的病例分别比预测低43.9%（95%置信区间：41.2 - 46.7%）和22.0%（95%置信区间：19.5 - 24.5%）——这种下降在东北部最为明显，且与封锁期间医疗服务可及性降低和户外活动减少有关。
我们的研究结果突出了环境、社会经济和行为因素在莱姆病动态中的复杂相互作用，包括新冠疫情对疾病报告的重大影响。这些见解强调了需要综合的、数据驱动的公共卫生策略来降低美国的莱姆病风险。
