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Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity
We present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model’s requirement that varianc...
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| 出版年: | PLoS One |
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| 主要な著者: | , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Public Library of Science
2021
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8282037/ https://ncbi.nlm.nih.gov/pubmed/34264972 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0254313 |
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