COVID 19: Open source model for rapid reduction of R to below 1 in high R0 scenarios
We present an open source model that allows quantitative prediction of the effects of testing on the rate of spread of COVID-19 described by R, the reproduction number, and on the degree of quarantine, isolation and lockdown required to limit it. The paper uses the model to quantify the outcomes of different test types and regimes, and to identify strategies and tests that can reduce the rate of spread and R value by a factor of between 1.67 and 33.3, reducing it to between 60% and 3% of the initial value..
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Preprint| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
arXiv.org - (2021) vom: 24. Dez. Zur Gesamtaufnahme - year:2021 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Baker, Mark R [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| PPN (Katalog-ID): |
XAR033300917 |
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| 245 | 1 | 0 | |a COVID 19: Open source model for rapid reduction of R to below 1 in high R0 scenarios |
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| 520 | |a We present an open source model that allows quantitative prediction of the effects of testing on the rate of spread of COVID-19 described by R, the reproduction number, and on the degree of quarantine, isolation and lockdown required to limit it. The paper uses the model to quantify the outcomes of different test types and regimes, and to identify strategies and tests that can reduce the rate of spread and R value by a factor of between 1.67 and 33.3, reducing it to between 60% and 3% of the initial value. | ||
| 700 | 1 | |a Hawthorne, Elizabeth L |e verfasserin |4 aut | |
| 700 | 1 | |a Rogge, Jessica R |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t arXiv.org |g (2021) vom: 24. Dez. |
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