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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.

preprint2022arXivOpen access
Mark R BakerElizabeth L HawthorneJessica R Rogge
Other Quantitative Biology
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Open access3 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Mark R BakerElizabeth L HawthorneJessica R Rogge

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