ABSTRACT
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.
ABSTRACT
Policymakers need decision tools to determine when to use physical distancing interventions to maximize the control of COVID-19 while minimizing the economic and social costs of these interventions. We develop a pragmatic decision tool to characterize adaptive policies that combine real-time surveillance data with clear decision rules to guide when to trigger, continue, or stop physical distancing interventions during the current pandemic. In model-based experiments, we find that adaptive policies characterized by our proposed approach prevent more deaths and require a shorter overall duration of physical distancing than alternative physical distancing policies. Our proposed approach can readily be extended to more complex models and interventions. One-sentence summariesAdaptive physical distancing policies save more lives with fewer weeks of intervention than policies which prespecify the length and timing of interventions.
ABSTRACT
Transmission of the SAR-COV-2 virus that causes COVID-19 is largely driven by human behavior and person-to-person contact. By staying home, people reduce the probability of contacting an infectious individual, becoming infected, and passing on the virus. One of the most promising sources of data on time use is smartphone location data. We develop a time use driven proportional mixing SEIR model that naturally incorporates time spent at home measured using smartphone location data and allows people of different health statuses to behave differently. We simulate epidemics in almost every county in the United States. The model suggests that Americans behavioral shifts have reduced cases in 55%-86% of counties and for 71%-91% of the population, depending on modeling assumptions. Resuming pre-epidemic behavior would lead to a rapid rise in cases in most counties. Spatial patterns of bending and flattening the curve are robust to modeling assumptions. Depending on epidemic history, county demographics, and behavior within a county, returning those with acquired immunity (assuming it exists) to regular schedules generally helps reduce cumulative COVID-19 cases. The model robustly identifies which counties would experience the greatest share of case reduction relative to continued distancing behavior. The model occasionally mischaracterizes epidemic patterns in counties tightly connected to larger counties that are experiencing large epidemics. Understanding these patterns is critical for prioritizing testing resources and back-to-work planning for the United States.
