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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8455| More prevalent, less deadly? Bayesian inference of the COVID19 Infection Fatality Ratio from mortality data | |
| Gustav Delius Benedict Powell Martin Bees George Constable Niall MacKay Jonathan William Pitchford | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2020.04.19.20071811 | |
| https://www.medrxiv.org/content/10.1101/2020.04.19.20071811v1 | |
| We use an established semi-mechanistic Bayesian hierarchical model of the COVID-19 pandemic [1], driven by European mortality data, to estimate the prevalence of immunity. We allow the infection-fatality ratio (IFR) to vary, adapt the model’s priors to better reflect emerging information, and re-evaluate the model fitting in the light of current mortality data. The results indicate that the IFR of COVID-19 may be an order of magnitude smaller than the current consensus, with the corollary that the virus is more prevalent than currently believed. These results emerge from a simple model and ought to be treated with caution. They emphasise the value of rapid community-scale antibody testing when this becomes available. | |
| bioRxiv | |
| 22-04-2020 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| More prevalent, less deadly_ Bayesian inference of the COVID19 Infection Fatality Ratio from mortality data.pdf | 686.92 kB | Adobe PDF | Visualizar/Abrir |