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X. Castro Dopico, S. Muschiol, N. Grinberg, S. Aleman, D. Sheward, L. Hanke, M. Ahl, Linnea Vikström, M. Forsell, J. Coquet, G. McInerney, J. Dillner, G. Bogdanovic, B. Murrell, Jan Albert, C. Wallace, G. K. Karlsson Hedestam
3 1. 1. 2022.

Probabilistic classification of anti‐SARS‐CoV‐2 antibody responses improves seroprevalence estimates

Population‐level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data‐driven manner, leading to uncertainty when classifying low‐titer responses. To improve upon this, we evaluated cutoff‐independent methods for their ability to assign likelihood of SARS‐CoV‐2 seropositivity to individual samples.


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