New methods for analyzing serological data with applications to influenza surveillance

BACKGROUND 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. RESULTS I show that log-transformed serological data exist in an effectively one-dimensional space. I use this result, together with new mechanistic insights into serological assays, to develop computational methods for accurately and efficiently recovering unmeasured serological data from a sample of measured data, for systematically minimizing noise and other types of non-antigenic variation found in the data, and for quantifying and visualizing antigenic variation. The methods can also be applied to data with effective dimensionality greater than one, under certain conditions. CONCLUSION Careful application of the methods developed here 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 and pandemic viruses before they become widespread. Although the focus here is on influenza surveillance, the described methods are more widely applicable.