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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8565| Covid-19 prevalence estimation by random sampling in the wider population - Optimal sample pooling under varying assumptions about true prevalence | |
| Ola Brynildsrud | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2020.05.05.20075275 | |
| https://www.medrxiv.org/content/10.1101/2020.05.05.20075275v1 | |
| The number of confirmed Covid-19 cases in a population is used as a coarse measurement for the burden of disease. However, this number depends heavily on the sampling intensity and the various test criteria used in different jurisdictions. A wide range of sources indicate that a large fraction of cases go undetected. Estimates of the true prevalence of Covid-19 can be made by random sampling in the wider population. Here we use simulations to explore confidence intervals of prevalence estimates under different sampling intensities and degrees of sample pooling. | |
| bioRxiv | |
| 08-05-2020 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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