tetano
Editor, Senior Moderator
Sci Rep
. 2024 Apr 25;14(1):9503.
doi: 10.1038/s41598-024-60060-3. Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model
Benjamin Glemain[SUP] 1 2 [/SUP], Xavier de Lamballerie[SUP] 3 [/SUP], Marie Zins[SUP] 4 5 [/SUP], Gianluca Severi[SUP] 6 7 [/SUP], Mathilde Touvier[SUP] 8 [/SUP], Jean-François Deleuze[SUP] 9 [/SUP]; SAPRIS-SERO study group; Nathanaël Lapidus[SUP] #[/SUP][SUP] 10 11 [/SUP], Fabrice Carrat[SUP] #[/SUP][SUP] 10 11 [/SUP]
Collaborators, Affiliations
The individual results of SARS-CoV-2 serological tests measured after the first pandemic wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus, these results are usually returned as a binary or ternary variable, relying on predefined cut-offs. We propose a Bayesian mixture model to estimate individual infection probabilities, based on 81,797 continuous anti-spike IgG tests from Euroimmun collected in France after the first wave. This approach used serological results as a continuous variable, and was therefore not based on diagnostic cut-offs. Cumulative incidence, which is necessary to compute infection probabilities, was estimated according to age and administrative region. In France, we found that a "negative" or a "positive" test, as classified by the manufacturer, could correspond to a probability of infection as high as 61.8% or as low as 67.7%, respectively. "Indeterminate" tests encompassed probabilities of infection ranging from 10.8 to 96.6%. Our model estimated tailored individual probabilities of SARS-CoV-2 infection based on age, region, and serological result. It can be applied in other contexts, if estimates of cumulative incidence are available.
Keywords: Bayes’ theorem; COVID-19; Mixture model; SARS-CoV-2.
. 2024 Apr 25;14(1):9503.
doi: 10.1038/s41598-024-60060-3. Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model
Benjamin Glemain[SUP] 1 2 [/SUP], Xavier de Lamballerie[SUP] 3 [/SUP], Marie Zins[SUP] 4 5 [/SUP], Gianluca Severi[SUP] 6 7 [/SUP], Mathilde Touvier[SUP] 8 [/SUP], Jean-François Deleuze[SUP] 9 [/SUP]; SAPRIS-SERO study group; Nathanaël Lapidus[SUP] #[/SUP][SUP] 10 11 [/SUP], Fabrice Carrat[SUP] #[/SUP][SUP] 10 11 [/SUP]
Collaborators, Affiliations
- PMID: 38664455
- DOI: 10.1038/s41598-024-60060-3
The individual results of SARS-CoV-2 serological tests measured after the first pandemic wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus, these results are usually returned as a binary or ternary variable, relying on predefined cut-offs. We propose a Bayesian mixture model to estimate individual infection probabilities, based on 81,797 continuous anti-spike IgG tests from Euroimmun collected in France after the first wave. This approach used serological results as a continuous variable, and was therefore not based on diagnostic cut-offs. Cumulative incidence, which is necessary to compute infection probabilities, was estimated according to age and administrative region. In France, we found that a "negative" or a "positive" test, as classified by the manufacturer, could correspond to a probability of infection as high as 61.8% or as low as 67.7%, respectively. "Indeterminate" tests encompassed probabilities of infection ranging from 10.8 to 96.6%. Our model estimated tailored individual probabilities of SARS-CoV-2 infection based on age, region, and serological result. It can be applied in other contexts, if estimates of cumulative incidence are available.
Keywords: Bayes’ theorem; COVID-19; Mixture model; SARS-CoV-2.