Forecasts and alternative scenarios of COVID-19 mortality have been critical inputs for pandemic response efforts, and decision-makers need information about predictive performance. We screen n = 386 public COVID-19 forecasting models, identifying n = 7 that are global in scope and provide public, date-versioned forecasts. We examine their predictive performance for mortality by weeks of extrapolation, world region, and estimation month. We additionally assess prediction of the timing of peak daily mortality. Globally, models released in October show a median absolute percent error (MAPE) of 7 to 13% at six weeks, reflecting surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. Median absolute error for peak timing increased from 8 days at one week of forecasting to 29 days at eight weeks and is similar for first and subsequent peaks. The framework and public codebase (https://github.com/pyliu47/covidcompare) can be used to compare predictions and evaluate predictive performance going forward.

COVID-19 死亡率的预测和替代情景一直是大流行应对工作的关键投入，决策者需要有关预测性能的信息。我们筛选了n = 386 个公共 COVID-19 预测模型，确定了n = 7 个全球范围内的模型，并提供公开的、日期版本的预测。我们通过外推周数、世界地区和估计月份来检查他们对死亡率的预测性能。我们还评估了每日死亡率峰值时间的预测。在全球范围内，10 月份发布的模型显示，六周时的中位绝对百分比误差 (MAPE) 为 7% 至 13%，尽管对人类行为反应和政府干预进行建模非常复杂，但仍表现出令人惊讶的良好表现。峰值时间的中位绝对误差从预测一周时的 8 天增加到八周时的 29 天，并且第一个和后续峰值的情况类似。该框架和公共代码库 (https://github.com/pyliu47/covidcompare) 可用于比较预测并评估未来的预测性能。