Machine Learning Models of COVID-19 Cases
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  • Release Date: 2021-12-10
  • COVID-19
  • pandemic
  • regime
  • lockdowns
  • machine learning
Video Introduction

This video is adapted from 10.3390/app112311227

The purpose of this video is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Researchers novel contribution is that they have obtained highly accurate models focused on two different regimes, lockdown and reopen, modeling each regime separately. The predictor variables include aggregated individual movement as well as state population density, health rank, climate temperature, and political color. Researchers apply a variety of machine learning methods to each regime: Multiple Regression, Ridge Regression, Elastic Net Regression, Generalized Additive Model, Gradient Boosted Machine, Regression Tree, Neural Network, and Random Forest. Researchers discover that Gradient Boosted Machines are the most accurate in both regimes. The best models achieve a variance explained of 95.2% in the lockdown regime and 99.2% in the reopen regime. Researchers describe the influence of the predictor variables as they change from regime to regime. Notably, they identify individual person movement, as tracked by GPS data, to be an important predictor variable. Researchers conclude that government lockdowns are an extremely important method for keeping people safe. Implications and questions for future research are discussed.

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Kamis, A. Machine Learning Models of COVID-19 Cases. Encyclopedia. Available online: (accessed on 14 April 2024).
Kamis A. Machine Learning Models of COVID-19 Cases. Encyclopedia. Available at: Accessed April 14, 2024.
Kamis, Arnold. "Machine Learning Models of COVID-19 Cases" Encyclopedia, (accessed April 14, 2024).
Kamis, A. (2021, December 10). Machine Learning Models of COVID-19 Cases. In Encyclopedia.
Kamis, Arnold. "Machine Learning Models of COVID-19 Cases." Encyclopedia. Web. 10 December, 2021.