The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control : the value and limitations of early models
Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March-June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number ([Formula: see text]) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ([Formula: see text]) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:288 |
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| Enthalten in: |
Proceedings. Biological sciences - 288(2021), 1957 vom: 25. Aug., Seite 20210811 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Childs, Marissa L [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 31.08.2021 Date Revised 08.11.2023 published: Print-Electronic UpdateOf: medRxiv. 2020 May 06;:. - PMID 32511583 Citation Status MEDLINE |
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| doi: |
10.1098/rspb.2021.0811 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM329722085 |
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| 100 | 1 | |a Childs, Marissa L |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control |b the value and limitations of early models |
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| 500 | |a Date Revised 08.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a UpdateOf: medRxiv. 2020 May 06;:. - PMID 32511583 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March-June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number ([Formula: see text]) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ([Formula: see text]) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a SARS-CoV-2 | |
| 650 | 4 | |a epidemics | |
| 650 | 4 | |a mathematical models | |
| 650 | 4 | |a non-pharmaceutical interventions | |
| 650 | 4 | |a reproduction number | |
| 700 | 1 | |a Kain, Morgan P |e verfasserin |4 aut | |
| 700 | 1 | |a Harris, Mallory J |e verfasserin |4 aut | |
| 700 | 1 | |a Kirk, Devin |e verfasserin |4 aut | |
| 700 | 1 | |a Couper, Lisa |e verfasserin |4 aut | |
| 700 | 1 | |a Nova, Nicole |e verfasserin |4 aut | |
| 700 | 1 | |a Delwel, Isabel |e verfasserin |4 aut | |
| 700 | 1 | |a Ritchie, Jacob |e verfasserin |4 aut | |
| 700 | 1 | |a Becker, Alexander D |e verfasserin |4 aut | |
| 700 | 1 | |a Mordecai, Erin A |e verfasserin |4 aut | |
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