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A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors
We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates mult...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Cold Spring Harbor Laboratory
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8168390/ https://www.ncbi.nlm.nih.gov/pubmed/34075386 http://dx.doi.org/10.1101/2021.05.22.21257643 |
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