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

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.

Keywords: Infectious disease modeling; Infectious disease outbreak prediction; Machine learning; Measles; Measles epidemiology.