Kujawski Stephanie A, Ru Boshu, Afanador Nelson Lee, Conway James H, Baumgartner Richard, Pawaskar Manjiri
Merck & Co., Inc. Rahway, NJ, USA.
Merck & Co., Inc. Rahway, NJ, USA.
Vaccine. 2024 Dec 2;42(26):126289. doi: 10.1016/j.vaccine.2024.126289. Epub 2024 Sep 7.
Although measles was declared eliminated from the United States in 2000, the frequency of measles outbreaks has increased in recent years. The ability to predict the locations of future cases could aid efforts to prevent and contain measles in the United States.
We estimated county-level measles risk using a machine learning model with 17 predictor variables, which was trained on 2014 and 2018 United States county-level measles case data and tested on data from 2019. We compared the predicted and actual locations of 2019 measles cases.
The model accurately predicted 95 % (specificity) of United States counties without measles cases and 72 % (sensitivity) of the United States counties that experienced ≥1 measles case in 2019, accounting for 94 % of all measles cases in 2019. Among the top 30 counties with the highest risk scores, the model accurately predicted 22 (73 %) counties with a measles case in 2019, corresponding to 72 % of all measles cases.
This machine learning model accurately predicted a majority of the United States counties at high risk for measles and could be used as a framework by state and national health agencies in their measles prevention and containment efforts.
尽管2000年美国宣布已消除麻疹，但近年来麻疹暴发的频率有所增加。预测未来病例的发生地点有助于美国开展麻疹预防和控制工作。
我们使用一个包含17个预测变量的机器学习模型来估计县级麻疹风险，该模型以2014年和2018年美国县级麻疹病例数据进行训练，并以2019年的数据进行测试。我们比较了2019年麻疹病例的预测地点和实际地点。
该模型准确预测了95%（特异性）无麻疹病例的美国县以及72%（敏感性）在2019年出现≥1例麻疹病例的美国县，占2019年所有麻疹病例的94%。在风险评分最高的30个县中，该模型准确预测了2019年有麻疹病例的22个（73%）县，占所有麻疹病例的72%。
这个机器学习模型准确预测了美国大多数麻疹高风险县，可被州和国家卫生机构用作麻疹预防和控制工作的框架。
