Medical Informatics Home Area, University of California Los Angeles, Los Angeles, CA, USA.
David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Nat Commun. 2021 May 10;12(1):2609. doi: 10.1038/s41467-021-22457-w.
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.
我们筛选了 386 个公开的 COVID-19 预测模型，确定了 7 个具有全球范围并提供公共、按日期版本预测的模型。我们通过外推周数、世界区域和估计月份来检查它们对死亡率的预测性能。我们还评估了预测每日死亡人数峰值时间的能力。在全球范围内，10 月份发布的模型在 6 周时的中位数绝对百分比误差（MAPE）为 7%至 13%，尽管模型中包含人类行为反应和政府干预等复杂因素，但表现出了惊人的良好性能。峰值时间的中位数绝对误差从预测第 1 周的 8 天增加到第 8 周的 29 天，并且首次和后续峰值之间的中位数绝对误差相似。该框架和公共代码库（https://github.com/pyliu47/covidcompare）可用于比较预测结果，并评估未来的预测性能。
