Looking back on forward-looking COVID models
© 2022 The Authors..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:3 |
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| Enthalten in: |
Patterns (New York, N.Y.) - 3(2022), 7 vom: 08. Juli, Seite 100492 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chong, Paul [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Revised 19.07.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.patter.2022.100492 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM343660601 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Carlson, Michael |e verfasserin |4 aut | |
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| 700 | 1 | |a He, Shuhan |e verfasserin |4 aut | |
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