RESUMEN
During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.
Asunto(s)
COVID-19 , Predicción , Pandemias , SARS-CoV-2 , COVID-19/epidemiología , COVID-19/transmisión , Humanos , Predicción/métodos , Estados Unidos/epidemiología , Pandemias/estadística & datos numéricos , Biología Computacional , Modelos EstadísticosRESUMEN
Multiple COVID-19 vaccines were proven to be safe and effective in curbing severe illness, but despite vaccine availability, vaccination rates were relatively low in the United States (U.S.). To better understand factors associated with low COVID-19 vaccine uptake in the U.S., our study provides a comprehensive, data-driven population-level statistical analysis at the county level. We find that political affiliation, as determined by the proportion of votes received by the Republican candidate in the 2020 presidential election, has the strongest association with our response variable, the percent of the population that received no COVID-19 vaccine. The next strongest association was median household income, which has a negative association. The percentage of Black people and the average number of vehicles per household are positively associated with the percent unvaccinated. In contrast, COVID-19 infection rate, percentage of Latinx people, postsecondary education percentage, median age, and prior non-COVID-19 childhood vaccination coverage are negatively associated with percent unvaccinated. Unlike previous studies, we do not find significant relationships between cable TV news viewership or Twitter misinformation variables with COVID-19 vaccine uptake. These results shed light on some factors that may impact vaccination choice in the U.S. and can be used to target specific populations for educational outreach and vaccine campaign strategies in efforts to increase vaccination uptake.