Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA.
EBioMedicine. 2023 Mar;89:104482. doi: 10.1016/j.ebiom.2023.104482. Epub 2023 Feb 21.
Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term.
Here we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1-4 weeks. The model is heavily data driven, and relies on epidemiological, mobility, survey, climate, demographic, and SARS-CoV-2 variant frequencies data. We implement a rigorous and robust evaluation of our model-specifically we report on weekly performance over a one-year period based on multiple error metrics, and explicitly assess how our model performance varies over space, chronological time, and different outbreak phases.
The proposed model is shown to consistently outperform the CDC ensemble model for all evaluation metrics in multiple spatiotemporal settings, especially for the longer-term (3 and 4 weeks ahead) forecast horizon. Our case study also highlights the potential value of variant frequencies data for use in short-term forecasting to identify forthcoming surges driven by new variants.
Based on our findings, the proposed forecasting framework improves upon the available state-of-the-art forecasting tools currently used to support public health decision making with respect to COVID-19 risk.
This work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570.
自 2020 年 1 月 21 日美国报告首例 COVID-19 病例以来，科学界一直在应用各种技术来预测病例和死亡人数。迄今为止，即使是在短期内，提供准确且稳健的高空间分辨率预测仍然具有挑战性。
在这里，我们提出了一种新颖的多阶段深度学习模型，用于预测每个美国州每周的 COVID-19 病例和死亡人数，预测期为 1-4 周。该模型高度依赖数据，依赖于流行病学、流动性、调查、气候、人口统计和 SARS-CoV-2 变体频率数据。我们对我们的模型进行了严格和稳健的评估-具体来说，我们根据多个错误指标报告了为期一年的每周性能，并明确评估了我们的模型性能如何随空间、时间顺序和不同的爆发阶段而变化。
所提出的模型在多个时空设置下，所有评估指标均显示出优于 CDC 综合模型的一致性表现，尤其是对于较长的预测期（3 周和 4 周）。我们的案例研究还强调了变体频率数据在短期预测中的潜在价值，以识别由新变体驱动的即将到来的激增。
根据我们的发现，所提出的预测框架改进了当前用于支持 COVID-19 风险公共卫生决策的可用最先进的预测工具。
这项工作得到了 NSF 快速反应研究（RAPID）赠款的资助，赠款 ID 为 2108526，以及 CDC 合同 #75D30120C09570。
