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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...

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Detalles Bibliográficos
Autores principales: Böttcher, Lucas, D'Orsogna, Maria R., Chou, Tom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607147/
https://www.ncbi.nlm.nih.gov/pubmed/34802274
http://dx.doi.org/10.1098/rsta.2021.0121

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