A Bayesian Mixture Model for Predicting the COVID-19 Pandemic in the United States

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  • 1 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan;
  • 2 Department of Pediatrics, Michigan Medicine, University of Michigan, Ann Arbor, Michigan;
  • 3 Pediatrics-Pulmonary Medicine, Michigan Medicine, University of Michigan, Ann Arbor, Michigan;
  • 4 Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, Michigan;
  • 5 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan

An outbreak of SARS-CoV-2 has led to a global pandemic affecting virtually every country. As of August 31, 2020, globally, there have been approximately 25,500,000 confirmed cases and 850,000 deaths; in the United States (50 states plus District of Columbia), there have been more than 6,000,000 confirmed cases and 183,000 deaths. We propose a Bayesian mixture model to predict and monitor COVID-19 mortality across the United States. The model captures skewed unimodal (prolonged recovery) or multimodal (multiple surges) curves. The results show that across all states, the first peak dates of mortality varied between April 4, 2020 for Alaska and June 18, 2020 for Arkansas. As of August 31, 2020, 31 states had a clear bimodal curve showing a strong second surge. The peak date for a second surge ranged from July 1, 2020 for Virginia to September 12, 2020 for Hawaii. The first peak for the United States occurred about April 16, 2020—dominated by New York and New Jersey—and a second peak on August 6, 2020—dominated by California, Texas, and Florida. Reliable models for predicting the COVID-19 pandemic are essential to informing resource allocation and intervention strategies. A Bayesian mixture model was able to more accurately predict the shape of the mortality curves across the United States than other models, including the timing of multiple peaks. However, given the dynamic nature of the pandemic, it is important that the results be updated regularly to identify and better monitor future waves, and characterize the epidemiology of the pandemic.

Author Notes

Address correspondence to Niko A. Kaciroti, University of Michigan, 300 North Ingalls Bldg., 10-th Floor #1035NW, Ann Arbor, MI 48109. E-mail: nicola@med.umich.edu

Authors’ addresses: Niko A. Kaciroti, Carey Lumeng, Vikas Parekh, and Matthew L. Boulton, University of Michigan, Ann Arbor, MI, E-mails: nicola@med.umich.edu, clumeng@umich.edu, viparekh@med.umich.edu, and mboulton@umich.edu.

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