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

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. Authors 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. Authors 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. Authors 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. Authors 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. Authors 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: https://encyclopedia.pub/video/video_detail/129 (accessed on 01 July 2022).
Kamis A. Machine Learning Models of COVID-19 Cases. Encyclopedia. Available at: https://encyclopedia.pub/video/video_detail/129. Accessed July 01, 2022.
Kamis, Arnold. "Machine Learning Models of COVID-19 Cases," Encyclopedia, https://encyclopedia.pub/video/video_detail/129 (accessed July 01, 2022).
Kamis, A. (2021, December 10). Machine Learning Models of COVID-19 Cases. In Encyclopedia. https://encyclopedia.pub/video/video_detail/129
Kamis, Arnold. ''Machine Learning Models of COVID-19 Cases.'' Encyclopedia. Web. 10 December, 2021.
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