ABSTRACT
United States jurisdictions implemented varied policies to slow SARS-CoV-2 transmission. Understanding patterns of these policies alongside individuals behaviors can inform effective outbreak response. To do so, we estimated the time-varying reproduction number (Rt), a weekly measure of real-time transmission using US COVID-19 cases from September 2020-November 2021. We then assessed the association between Rt and policies, personal COVID-19 mitigation behaviors, variants, immunity, and social vulnerability indicators using two multi-level regression models. First, we fit a model with state-level policy stringency according to the Oxford Stringency Index, a composite indicator reflecting the strictness of COVID-19 policies and strength of pandemic-related communication. Our second model included a subset of specific policies. We found that personal mitigation behaviors and vaccination were more strongly associated with decreased transmission than policies. Importantly, transmission was reduced not by a single measure, but by various layered measures. These results underscore the need for policy, behavior change, and risk communication integration to reduce virus transmission during epidemics.
Subject(s)
COVID-19 , Mental DisordersABSTRACT
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. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse 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. f