Two important challenges to the use of serological assays for influenza surveillance include the substantial amount of experimental effort involved, and the inherent noisiness of serological data. Here, informed by the observation that log-transformed serological data (obtained from the hemagglutination-inhibition assay) exist in an effectively one-dimensional space, computational methods are developed for accurately and efficiently recovering unmeasured serological data from a sample of measured data, and systematically minimizing noise found in the measured data. Careful application of these methods would enable the collection of better-quality serological data on a greater number of circulating influenza viruses than is currently possible, and improve the ability to identify potential epidemic/pandemic viruses before they become widespread. Although the focus here is on influenza surveillance, the described methods are more widely applicable
