Chong Paul, Yoon Byung-Jun, Lai Debbie, Carlson Michael, Lee Jarone, He Shuhan
Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA.
Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX 77843, USA.
Patterns (N Y). 2022 Mar 22;3(7):100492. doi: 10.1016/j.patter.2022.100492. eCollection 2022 Jul 8.
Covid Act Now (CAN) developed an epidemiological model that takes various non-pharmaceutical interventions (NPIs) into account and predicts viral spread and subsequent health outcomes. In this study, the projections of the model developed by CAN were back-tested against real-world data, and it was found that the model consistently overestimated hospitalizations and deaths by 25%-100% and 70%-170%, respectively, due in part to an underestimation of the efficacy of NPIs. Other COVID models were also back-tested against historical data, and it was found that all models generally captured the potential magnitude and directionality of the pandemic in the short term. There are limitations to epidemiological models, but understanding these limitations enables these models to be utilized as tools for data-driven decision-making in viral outbreaks. Further, it can be valuable to have multiple, independently developed models to mitigate the inaccuracies of or to correct for the incorrect assumptions made by a particular model.
“立即行动应对新冠疫情”（Covid Act Now，CAN）开发了一种流行病学模型，该模型考虑了各种非药物干预措施（NPIs），并预测病毒传播及后续健康结果。在本研究中，对CAN开发的模型预测结果与实际数据进行了回测，结果发现该模型持续高估住院人数和死亡人数，分别高估了25%-100%和70%-170%，部分原因是对非药物干预措施的效果估计不足。其他新冠模型也针对历史数据进行了回测，结果发现所有模型在短期内总体上都能把握疫情的潜在规模和发展趋势。流行病学模型存在局限性，但了解这些局限性能使这些模型用作病毒爆发时数据驱动决策的工具。此外，拥有多个独立开发的模型以减轻特定模型所产生的不准确之处或纠正其错误假设可能很有价值。
