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Two-part predictive modeling for COVID-19 cases and deaths in the U.S

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  • Teresa-Thuong Le
  • Xiyue Liao

Abstract

COVID-19 prediction has been essential in the aid of prevention and control of the disease. The motivation of this case study is to develop predictive models for COVID-19 cases and deaths based on a cross-sectional data set with a total of 28,955 observations and 18 variables, which is compiled from 5 data sources from Kaggle. A two-part modeling framework, in which the first part is a logistic classifier and the second part includes machine learning or statistical smoothing methods, is introduced to model the highly skewed distribution of COVID-19 cases and deaths. We also aim to understand what factors are most relevant to COVID-19’s occurrence and fatality. Evaluation criteria such as root mean squared error (RMSE) and mean absolute error (MAE) are used. We find that the two-part XGBoost model perform best with predicting the entire distribution of COVID-19 cases and deaths. The most important factors relevant to either COVID-19 cases or deaths include population and the rate of primary care physicians.

Suggested Citation

  • Teresa-Thuong Le & Xiyue Liao, 2024. "Two-part predictive modeling for COVID-19 cases and deaths in the U.S," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0302324
    DOI: 10.1371/journal.pone.0302324
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
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