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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.
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ObjectivesTo evaluate the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of non-pharmaceutical interventions for COVID19 and being homebound (i.e., refraining from community/workplace interactions). MethodsAn agent-based simulation model to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in- place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020. ResultsCompared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 3.43, 19.8, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 121K-268K, 522K-567K, 5,377K-5,380K, respectively. ConclusionsVoluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place. Three-question Summary BoxO_LIWhat is the current understanding of this subject? Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. C_LIO_LIWhat does this report add to the literature? Our report assessed which intervention strategies provided the best results in terms of both reducing infection outcomes (cases, deaths, etc.) and minimizing their social and economic effects (e.g., number of people homebound, providing childcare, etc.). C_LIO_LIWhat are the implications for public health practice? Voluntary quarantine proved to be the most beneficial in terms of reducing infections and deaths compared to the number of people who were homebound. C_LI
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ImportanceAs the COVID19 spread in the US continues to grow, local and state officials face difficult decisions about when and how to transition to a "new normal." ObjectiveProject the number of COVID19 infections and resulting severe outcomes, and the need for hospital capacity under social distancing, particularly, shelter-in-place and voluntary quarantine. DesignWe developed an agent-based simulation model to project the infection spread. We populated the model using COVID19-specific parameters for the natural history of the disease and data from Georgia on agents interactions and demographics. SettingThe simulation study covered a six-month period, testing different social distancing scenarios, including baselines (no-intervention or school closure only) and combinations of shelter-in-place and voluntary quarantine with different timelines and compliance levels. The outcomes are compared at the state and community levels. Main OutcomesThe number and percentage of cumulative and daily new and symptomatic and asymptomatic infections, hospitalizations, and deaths; COVID19-related demand for hospital beds, ICU beds, and ventilators. ResultsThe combined intervention of shelter-in-place followed by voluntary quarantine reduced peak infections from 180,000 under no intervention and 120,000 under school closure, respectively, to below 80,000, and delayed the peak from April to June or later. Increasing shelter-in-place duration from four to five weeks yielded 3-14% and 4-6% decrease in cumulative infection and fatality rates, respectively. Regardless of the shelter-in-place duration, increasing voluntary quarantine compliance decreased daily new infections from almost 80,000 to 50,000, and decreased cumulative infection rate by 50%. The total number of fatalities ranged from 6,150 to 17,900 under different scenarios. Peak infection date varied across scenarios and counties; on average, increasing shelter-in-place duration delayed the peak day by 7 days across counties. The peak percentage is similar across rural and urban counties. Region D is estimated to have the highest COVID19-related healthcare needs with 7,357 hospital beds, 1,141 ICU beds, and 558 ventilators. Conclusions and RelevanceShelter-in-place followed by voluntary quarantine substantially reduce COVID19 infections, healthcare resource needs, and severe outcomes; delay the peak; and enable better preparedness. Time of the peak is projected to vary across locations, enabling reallocation of health system capacity. KEY POINTSO_ST_ABSQuestionC_ST_ABSHow social distancing strategies impact the spread of COVID19? FindingsExtending shelter-in-place by one week delays the peak by about 8 days but it does not significantly reduce the peak. High compliance with voluntary quarantine following shelter-in-place reduces the peak by 40% in Georgia. MeaningThere needs to be a very strong public messaging about social distancing when shelter-in-place is lifted, to achieve a better match between healthcare capacity and demand, considering different peak times across the communities.