Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA : a deep learning approach
Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:89 |
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| Enthalten in: |
EBioMedicine - 89(2023) vom: 01. März, Seite 104482 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Du, Hongru [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 14.03.2023 Date Revised 24.03.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ebiom.2023.104482 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a FUNDING: This work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Coronavirus | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Forecast | |
| 650 | 4 | |a LSTM | |
| 650 | 4 | |a Pandemic | |
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| 650 | 4 | |a SARS-CoV-2 | |
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| 700 | 1 | |a Badr, Hamada S |e verfasserin |4 aut | |
| 700 | 1 | |a Petrone, Mary E |e verfasserin |4 aut | |
| 700 | 1 | |a Grubaugh, Nathan D |e verfasserin |4 aut | |
| 700 | 1 | |a Gardner, Lauren M |e verfasserin |4 aut | |
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