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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8258| Refining COVID-19 retrospective diagnosis with continuous serological tests: a Bayesian mixture model | |
| Benjamin Glemain Xavier de Lamballerie marie zins Gianluca Severi Mathilde Touvier Jean-François DELEUZE Nathanael Lapidus Fabrice Carrat | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2023.09.15.23295603 | |
| https://www.medrxiv.org/content/10.1101/2023.09.15.23295603v1 | |
| COVID-19 serological tests with a “positive”, “intermediate” or “negative” result according to predefined thresholds cannot be directly interpreted as a probability of having been infected with SARS-CoV-2. Based on 81,797 continuous anti-spike tests collected in France after the first wave, a Bayesian mixture model was developed to provide a tailored infection probability for each participant. Depending on the serological value and the context (age and administrative region), a negative or a positive test could correspond to a probability of infection as high as 61.9% or as low as 68.0%, respectively. In infected individuals, the model estimated a proportion of “non-responders” of 14.5% (95% CI, 11.2-18.1%), corresponding to a sub-group of persons who exhibited a weaker serological response to SARS-CoV-2. This model allows for an individual interpretation of serological results as a probability of infection, depending on the context and without any notion of threshold. Competing Interest Statement The authors have declared no competing interest. Funding Statement The SAPRIS-SERO study was funded by the ANR (Agence Nationale de la Recherche, #ANR-10-COHO-06), Fondation pour la Recherche Médicale (#20RR052-00), Inserm (Institut National de la Santé et de la Recherche Médicale, #C20-26). The sponsor and funders facilitated data acquisition but did not participate in the study design, analysis, interpretation or drafting. | |
| 15-09-2023 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Refining COVID-19 retrospective diagnosis with continuous serological tests_ a Bayesian mixture model.pdf | 738.29 kB | Adobe PDF | Visualizar/Abrir |