Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain.
Sci Rep. 2024 May 11;14(1):10775. doi: 10.1038/s41598-024-61233-w.
Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two-three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1-2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.
利用经验模型准确预测 COVID-19 病例可以使卫生官员在两到三周的时间内为医院的应急情况做好准备,因为从病例报告到患者入院存在延迟。我们研究了戈珀特型经验模型在提前两到三周提供准确预测的能力,以便在病毒传播激增时为应对提供较大的准备窗口。我们使用 2020 年最后一个季度的双周预测来研究预测的稳定性和准确性。使用 2020 年的数据,我们表明,了解和纠正不同国家病例的每日报告结构是实现准确预测的关键。此外,我们发现过滤掉对模型参数变化高度不稳定的预测(约占 20%)可以大大减少偏离预测的数量。然后,我们使用 2021 年的数据对该方法进行了稳健性测试。我们发现,对于这些数据,只有 1-2%的一周预测误差超过 50%。两周预测的误差增加到 3%,只有三周预测的误差达到 10%。