Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches.

ABSTRACT:

Background

Measles, a highly contagious viral infection, is resurging in the United States, driven by international importation and declining domestic vaccination coverage. Despite this resurgence, measles outbreaks are still rare events that are difficult to predict. Improved methods to predict outbreaks at the county level would facilitate the optimal allocation of public health resources.

Objective

We aimed to validate and compare extreme gradient boosting (XGBoost) and logistic regression, 2 supervised learning approaches, to predict the US counties most likely to experience measles cases. We also aimed to assess the performance of hybrid versions of these models that incorporated additional predictors generated by 2 clustering algorithms, hierarchical density-based spat

SUBMITTER: Ru B 

PROVIDER: S-EPMC10131820 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature