Summary of regression results: GLM-2.

The US COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their official COVID-19 communications. As such, the Forecast Hub is a critical centralized resource to promote transparent decision making. While the Forecast Hub has provided valuable predictions focused on accuracy, there is an opportunity to evaluate model performance across social determinants such as race and urbanization level that have been known to play a role in the COVID-19 pandemic. In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. We hope this work will encourage COVID-19 modelers and the CDC to report fairness metrics together with accuracy, and to reflect on the potential harms of the models on specific social groups and contexts.美国新冠疫情预测中心（US COVID-19 Forecast Hub）是一个汇集50余家独立研究团队新冠疫情预测成果的数据库，已被美国疾病控制与预防中心（CDC）用于其官方新冠疫情相关通报工作。因此，该预测中心是推动决策透明化的关键中心化资源。尽管该预测中心已推出诸多以预测精度为核心的高质量预测结果，但仍存在可拓展的研究方向：基于种族、城市化水平等已知对新冠疫情传播产生影响的社会决定因素，对模型性能展开全面评估。本研究对该预测中心的模型预测结果开展了全面的公平性分析，结果显示：基于各类社会决定因素的预测性能存在统计学意义上的显著差异，少数族裔群体及低城市化地区往往伴随更高的预测误差。本研究希望能够推动新冠疫情预测建模者与美国疾控中心（CDC）在发布模型时，同步报告公平性指标与预测精度，并反思模型对特定社会群体及场景可能造成的潜在危害。