A modelling framework to improve antibody titer estimation from dilution series data: application to RSV Foci Reduction Neutralization Tests

Accurately determining antibody levels from experimental data is important for measuring population immunity, guiding vaccine development and informing public health policy. Based on raw dilution series data from respiratory syncytial virus (RSV) foci reduction neutralization tests (FRNTs), we identified notable bias and uncertainty in neutralizing antibody (nAb) titer estimates between batches when using standard, off-the-shelf methods such as the Karber formula and four-parameter logistic (4PL) model. To address these problems, we developed a Bayesian hierarchical modelling framework for estimating nAb titers, which corrects for batch effects and other sources of experimental variation. The model was shown in both simulations and real data from RSV serosurveys to provide more accurate and unbiased nAb titer estimates than existing approaches. Among all methods evaluated, the Karber formula exhibited the highest variability and led to increased rates of both false positives and false negatives. The 4PL model tended to produce negative estimates in samples with low antibody levels. These inaccurate individual-level estimates, in turn, biased population-level measures, including geometric mean titers (GMTs), seroprevalence, seroconversion rates and fold-rise rates. In contrast, the adjusted estimates from our model demonstrated the highest accuracy. Spearman correlation coefficients between estimated and true titers from simulated data were 0.63 for Karber estimates, 0.87 for 4PL estimates, and 0.96 from our model. This framework can be readily adapted to other antibody assays which produce dilution series data, and can enhance the accuracy and precision of titer estimates across a range of experimental settings.