Azman et al. trained a random forest model on over 1500 confirmed cholera cases to categorize recency of infection from a panel of IgG, IgM and IgA measurements against various antigens and vibriocidal assays, and then used simulations to demonstrate how these machine learning-derived infection states could be used to back-calculate cholera incidence using cross-sectional serology. This evidence concerns the gene CD79A and infection.