One Million and Counting: Estimates of Deaths in the United States from Ancestral SARS-CoV-2 and Variants

BackgroundOver one million COVID-19 deaths have been recorded in the United States. Sustained global SARS-CoV-2 transmission has led to the emergence of new variants with increased transmissibility, virulence, and/or immune evasion. The specific burden of mortality from each variant over the course of the U.S. COVID-19 epidemic remains unclear. MethodsWe constructed an epidemiologic model using data reported by the CDC on COVID-19 mortality and circulating variant proportions to estimate the number of recorded COVID-19 deaths attributable to each SARS-CoV-2 variant in the U.S. We conducted sensitivity analysis to account for parameter uncertainty. FindingsOf the 1,003,419 COVID-19 deaths recorded as of May 12, 2022, we estimate that 460,124 (46%) were attributable to WHO-designated variants. By U.S. Census Region, the South recorded the most variant deaths per capita (median estimate 158 per 100,000), while the Northeast recorded the fewest (111 per 100,000). Over 40 percent of national COVID-19 deaths were estimated to be caused by the combination of Alpha (median estimate 39,548 deaths), Delta (273,801), and Omicron (117,560). InterpretationSARS-CoV-2 variants that have emerged around the world have imposed a significant mortality burden in the U.S. In addition to national public health strategies, greater efforts are needed to lower the risk of new variants emerging, including through global COVID-19 vaccination, treatment, and outbreak mitigation.

Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

BackgroundThe COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research has suggested that combining forecasts from multiple models into a single "ensemble" forecast can increase the robustness of forecasts. Here we evaluate the real-time application of an open, collaborative ensemble to forecast deaths attributable to COVID-19 in the U.S. MethodsBeginning on April 13, 2020, we collected and combined one- to four-week ahead forecasts of cumulative deaths for U.S. jurisdictions in standardized, probabilistic formats to generate real-time, publicly available ensemble forecasts. We evaluated the point prediction accuracy and calibration of these forecasts compared to reported deaths. ResultsAnalysis of 2,512 ensemble forecasts made April 27 to July 20 with outcomes observed in the weeks ending May 23 through July 25, 2020 revealed precise short-term forecasts, with accuracy deteriorating at longer prediction horizons of up to four weeks. At all prediction horizons, the prediction intervals were well calibrated with 92-96% of observations falling within the rounded 95% prediction intervals. ConclusionsThis analysis demonstrates that real-time, publicly available ensemble forecasts issued in April-July 2020 provided robust short-term predictions of reported COVID-19 deaths in the United States. With the ongoing need for forecasts of impacts and resource needs for the COVID-19 response, the results underscore the importance of combining multiple probabilistic models and assessing forecast skill at different prediction horizons. Careful development, assessment, and communication of ensemble forecasts can provide reliable insight to public health decision makers.