A Bayesian Mixture Model for Predicting the COVID-19 Related Mortality in the United States.

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.

Pathways to Severe COVID-19 for People with Obesity.

Increased morbidity and mortality from coronavirus disease 2019 (COVID-19) in people with obesity have illuminated the intersection of obesity with impaired responses to infections. Although data on mechanisms by which COVID-19 impacts health are being rapidly generated, there is a critical need to better understand the pulmonary, vascular, metabolic, and immunologic aspects that drive the increased risk for complications from COVID-19 in people with obesity. This review provides a broad overview of the intersection between COVID-19 and the physiology of obesity in order to highlight potential mechanisms by which COVID-19 disease severity is increased by obesity and identify areas for future investigation toward developing tailored therapy for people with obesity who develop COVID-19.