tetano
Editor, Senior Moderator
EBioMedicine
. 2023 Feb 21;89:104482.
doi: 10.1016/j.ebiom.2023.104482. Online ahead of print.
Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach
Hongru Du[SUP] 1 [/SUP], Ensheng Dong[SUP] 1 [/SUP], Hamada S Badr[SUP] 2 [/SUP], Mary E Petrone[SUP] 3 [/SUP], Nathan D Grubaugh[SUP] 4 [/SUP], Lauren M Gardner[SUP] 5 [/SUP]
Affiliations
Abstract
Background: 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.
Method: 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.
Findings: 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.
Interpretation: 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.
Funding: This work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570.
Keywords: COVID-19; Coronavirus; Deep learning; Forecast; LSTM; Pandemic; Prediction; SARS-CoV-2; State-level; US; Variant frequencies data.
. 2023 Feb 21;89:104482.
doi: 10.1016/j.ebiom.2023.104482. Online ahead of print.
Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach
Hongru Du[SUP] 1 [/SUP], Ensheng Dong[SUP] 1 [/SUP], Hamada S Badr[SUP] 2 [/SUP], Mary E Petrone[SUP] 3 [/SUP], Nathan D Grubaugh[SUP] 4 [/SUP], Lauren M Gardner[SUP] 5 [/SUP]
Affiliations
- PMID: 36821889
- DOI: 10.1016/j.ebiom.2023.104482
Abstract
Background: 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.
Method: 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.
Findings: 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.
Interpretation: 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.
Funding: This work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570.
Keywords: COVID-19; Coronavirus; Deep learning; Forecast; LSTM; Pandemic; Prediction; SARS-CoV-2; State-level; US; Variant frequencies data.