ArticleNature communications2026
A Bayesian modelling framework to improve antibody titer estimation applied to RSV dilution series data.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurately measuring antibody levels is important for assessing population immunity and guiding vaccine development. We identify batch-level biases and experimental noise in neutralizing antibody (nAb) titer estimates from respiratory syncytial virus (RSV) foci reduction neutralization tests (FRNTs) when using off-the-shelf methods such as the Kärber formula and four-parameter logistic (4PL) model. To address this, we develop a Bayesian hierarchical model (BHM) to estimate nAb titers, correcting for batch effects and other sources of experimental variation. We evaluate model performance using both simulated and experimental FRNT data. In simulation, nAb titers are most accurate using the BHM (Spearman
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