Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
PLoS Comput Biol. 2022 Oct 6;18(10):e1010602. doi: 10.1371/journal.pcbi.1010602. eCollection 2022 Oct.
We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In our 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model, 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework can be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions.
我们分析了一组用于预测传染病和大流行轨迹的 n 个子流行模型。这些集成模型方法和集成子流行以捕捉复杂时间动态的模型已证明具有强大的预测能力。该建模框架可以描述复杂的传染病模式，包括高原、传染病反弹以及由不同大小的多个峰值组成的传染病波。我们系统地评估了它们在 2020 年 4 月底至 2022 年 2 月底期间对美国 COVID-19 大流行的短期预测中的校准和短期预测性能。我们将它们的性能与两个常用的统计 ARIMA 模型进行了比较。最佳拟合的子流行模型和使用排名前几位的子流行模型构建的三个集成模型在加权区间得分（WIS）和 95%预测区间的覆盖范围方面始终优于 ARIMA 模型，在 10、20 和 30 天的短期预测中。在我们的 30 天预测中，子流行模型的平均 WIS 范围为 377.6 至 421.3，而 ARIMA 模型的范围为 439.29 至 767.05。在 98 次短期预测中，纳入排名前四位的子流行模型的集成模型（Ensemble(4)）在 30 天的预测中，以 WIS 为指标，有 66.3%的时间优于（对数）ARIMA 模型，而 ARIMA 模型有 69.4%的时间优于（对数）ARIMA 模型。在考虑预测不确定性的指标方面，Ensemble(4)始终表现出最佳的性能。该框架可以方便地应用于研究 COVID-19 以外的传染病和大流行的传播，以及自然界和社会中发现的其他受益于短期预测的动态增长过程。
