Changing Dynamics of COVID-19 in the U.S. with the Emergence of the Delta Variant: Projections of the COVID-19 Simulator

With the recent emergence of the B.1.617.2 (Delta) variant of SARS-CoV-2 in the U.S., many states are seeing rising cases and hospitalizations after a period of steady decline. As We used the COVID-19 Simulator, an interactive online tool that utilizes a validated mathematical model, to simulate the trajectory of COVID-19 at the state level in the U.S. COVID-19 Simulators forecasts are updated weekly and included in the Centers for Disease Control and Prevention (CDC) ensemble model. We employed our model to analyze scenarios where the Delta variant becomes dominant in every state. The combination of high transmissibility of the Delta variant, low vaccination coverage in several regions, and more relaxed attitude towards social distancing is expected to result in as surge in COVID-19 deaths in at least 40 states. In several states - including Idaho, Maine, Montana, Nebraska, North Carolina, Oregon, Puerto Rico, Washington, and West Virginia - the projected daily deaths in 2021 could exceed the prior peak daily deaths under current social distancing behavior and vaccination rate. The number of COVID-19 deaths across the U.S. could exceed 1600 per day. Between August 1, 2021, and December 31, 2021, there could be additional 157,000 COVID-19 deaths across the U.S. Of note, our model projected approximately 20,700 COVID-19 deaths in Texas, 16,000 in California, 12,400 in Florida, 12,000 in North Carolina, and 9,300 in Georgia during this period. In contrast, the projected number of COVID-19 deaths would remain below 200 in New Jersey, Massachusetts, Connecticut, Vermont, and Rhode Island. We project COVID-19 deaths based on the current vaccination rates and social distancing behavior. Our hope is that the findings of this report serve a warning sign and people revert to wearing masks and maintain social distancing to reduce COVID-19 associated deaths in the U.S. Our projections are updated weekly by incorporating vaccination rates and social distancing measures in each state; the latest results can be found at the COVID-19 Simulator website.

Effect of Increased Alcohol Consumption During COVID-19 Pandemic on Alcohol-related Liver Disease: A Modelling Study

ObjectivesThe burden of alcohol-related liver disease (ALD) is surging in the US. There is evidence that alcohol consumption increased during the early periods of the coronavirus disease-2019 (COVID-19) pandemic. We describe the impact of increased alcohol consumption on alcohol-related liver disease. DesignMicrosimulation model SettingModel parameters estimated from publicly available data sources, including national surveys on drug and alcohol use and published studies informing the impact of alcohol consumption on ALD severity. ParticipantsUS residents MethodsWe extended a previously validated microsimulation model that estimated the short- and long-term effect of increased drinking during the COVID-19 pandemic in individuals in the US born between 1950-2012. We modelled short- and long-term outcomes of current drinking patterns during COVID-19 (status quo) using survey data of changes in alcohol consumption in a nationally representative sample between February and April 2020. We compared these outcomes with a counter-factual scenario wherein no COVID-19 occurs, and drinking patterns do not change. Reported outcomes are for individuals aged 18-65. Main outcome measuresALD-related deaths inclusive of HCC mortality, the prevalence and incidence of decompensated cirrhosis and hepatocellular carcinoma, and disability-adjusted life-years (DALYs) ResultsIncreases in alcohol consumption during 2020 due to the COVID-19 pandemic are estimated to result in to 8,200 [95% UI 7,700 - 8,700] additional ALD-related deaths (1% increase compared with the counter-factual scenario), 17,100 [95% UI 16,100 - 18,200] cases of decompensated cirrhosis (2% increase) and 1,100 [95% UI 1,100 - 1,200] cases of HCC (1% increase) between 2020 and 2040. Between 2020 and 2023, alcohol consumption changes due to COVID-19 will lead to 100 [100-200] additional deaths and 2,200 [2,200-2,300] additional decompensations in patients suffering from alcohol-related liver disease. ConclusionsA short-term increase in alcohol consumption during the COVID-19 pandemic can substantially increase long-term ALD-related morbidity and mortality. Our findings highlight the need for individuals and policymakers to make informed decisions to mitigate the impact of high-risk alcohol drinking in the US. Summary Box"O_ST_ABSWhat is already known on this topicC_ST_ABSO_LIThe impact of an increase in alcohol consumption during coronavirus disease 2019 (COVID-19) on drinking trajectory changes and alcohol-related liver diseases is not known. C_LIO_LIStudies have reported increases in hospital admissions for alcohol-related liver disease or pancreatitis potentially related to COVID-19, increases in alcohol consumption, and exacerbation of pre-existing liver injury, though limited evidence exists for the long-term effect of increased drinking on alcohol-related liver cirrhosis and liver cancer in the USA. C_LI Added value of this studyO_LIOur study provides new data on liver disease morbidity and mortality associated with increased consumption of alcohol during the COVID-19 pandemic. C_LIO_LIOur study suggests that drinking changes associated with the COVID-19 pandemic it is expected to lead to increases in both mortality and morbidity in the long term. to 8,200 additional ALD-related deaths, 17,100 cases of decompensated cirrhosis, and 1,100 cases of HCC between 2020 and 2040 2 C_LI

The Effect of NFL and NCAA Football Games on the Spread of COVID-19 in the United States: An Empirical Analysis

ImportanceIn 2020 and early 2021, the National Football League (NFL) and National Collegiate Athletic Association (NCAA) had opted to host games in stadiums across the country. The in-person attendance of games has varied with time and from county to county. There is currently no evidence on whether limited in-person attendance of games has caused a substantial increase in coronavirus disease 2019 (COVID-19) cases. ObjectiveTo assess whether NFL and NCAA football games with limited in-person attendance have contributed to a substantial increase in COVID-19 cases in the counties they were held. DesignIn this time-series cross-sectional study, we matched every county hosting game(s) with in-person attendance (treated) in 2020 and 2021 with a county that has an identical game history for up to 14 days (control). We employed a standard matching method to further refine this matched set so that the treated and matched control counties have similar population size, non-pharmaceutical intervention(s) in place, and COVID-19 trends. We assessed the effect of hosting games with in-person attendance using a difference-in-difference estimator. SettingU.S. counties. ParticipantsU.S. counties hosting NFL and/or NCAA games. ExposureHosting NFL and/or NCAA games. Main outcomes and measuresEstimating the impact of NFL and NCAA games with in-person attendance on new, reported COVID-19 cases per 100,000 residents at the county-level up to 14 days post-game. ResultsThe matching algorithm returned 361 matching sets of counties. The effect of in-person attendance at NFL and NCAA games on community COVID-19 spread is not significant as it did not surpass 5 new daily cases of COVID-19 per 100,000 residents on average. Conclusions and relevanceThis time-series, cross-sectional matching study with a difference-in-differences design did not find an increase in COVID-19 cases per 100,000 residents in the counties where NFL and NCAA games were held with in-person attendance. Our study suggests that NFL and NCAA football games hosted with limited in-person attendance do not cause a significant increase in local COVID-19 cases. Key pointsO_ST_ABSQuestionC_ST_ABSDid NFL and NCAA football games with limited in-person attendance cause a substantia increase in coronavirus disease 2019 (COVID-19) cases in the U.S. counties where the games were held? FindingsThis time-series, cross-sectional study of U.S. counties with NFL and NCAA football games used matching and difference-in-differences design to estimate the effect of games with limited in-person attendance on county-level COVID-19 spread. Our study does not find an increase in county-level COVID-19 cases per 100,000 residents due to NFL and NCAA football games held with limited in-person attendance. MeaningThis study suggests that NFL and NCAA games held with limited in-person attendance do not cause an increase in COVID-19 cases in the counties they are held.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

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.