Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

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 multimodel ensemble forecast that combined predictions from dozens of 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 naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk 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.

White (but Not Black) Americans Continue to See Racism as a Zero-Sum Game; White Conservatives (but Not Moderates or Liberals) See Themselves as Losing.

In a 2011 article in this journal entitled "Whites See Racism as a Zero-Sum Game That They Are Now Losing" (Perspectives on Psychological Science, 6, 215-218), Norton and Sommers assessed Black and White Americans' perceptions of anti-Black and anti-White bias across the previous 6 decades-from the 1950s to the 2000s. They presented two key findings: White (but not Black) respondents perceived decreases in anti-Black bias to be associated with increases in anti-White bias, signaling the perception that racism is a zero-sum game; White respondents rated anti-White bias as more pronounced than anti-Black bias in the 2000s, signaling the perception that they were losing the zero-sum game. We collected new data to examine whether the key findings would be evident nearly a decade later and whether political ideology would moderate perceptions. Liberal, moderate, and conservative White (but not Black) Americans alike believed that racism is a zero-sum game. Liberal White Americans saw racism as a zero-sum game they were winning by a lot, moderate White Americans saw it as a game they were winning by only a little, and conservative White Americans saw it as a game they were losing. This work has clear implications for public policy and behavioral science and lays the groundwork for future research that examines to what extent racial differences in perceptions of racism by political ideology are changing over time.

The United States COVID-19 Forecast Hub dataset.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.