Prediction of measles cases in US counties: A machine learning approach

IF 4.5 3区医学 Q2 IMMUNOLOGY

Vaccine Pub Date : 2024-09-07 DOI:10.1016/j.vaccine.2024.126289

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引用次数: 0

Abstract

Background. 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.

Methods. 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.

Results. 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.

Conclusions. 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.

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预测美国各县的麻疹病例：机器学习方法。

背景：尽管美国已于 2000 年宣布消灭麻疹，但近年来麻疹爆发的频率却在增加。预测未来病例发生地点的能力有助于美国预防和控制麻疹的工作：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，该模型在 2014 年和 2018 年美国县级麻疹病例数据上进行了训练，并在 2019 年的数据上进行了测试。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）的美国无麻疹病例县和 72%（灵敏性）的美国 2019 年麻疹病例≥1 例的县，占 2019 年所有麻疹病例的 94%。在风险评分最高的前30个县中，该模型准确预测了22个（73%）在2019年出现麻疹病例的县，相当于所有麻疹病例的72%：该机器学习模型准确预测了美国大部分麻疹高风险县，可作为各州和国家卫生机构预防和遏制麻疹工作的框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Vaccine 医学-免疫学

CiteScore

8.70

自引率

5.50%

发文量

992

审稿时长

131 days

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