serosim: an R package for simulating serological survey data arising from vaccination, epidemiological and antibody kinetics processes

Abstract

Abstractserosimis an open source R package designed to aid inference of serological surveys, by simulating data arising from user-specified vaccine and infection-generated antibody kinetics processes using a random effects model. Serological surveys are used to assess population immunity by directly measuring individuals’ antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological surveys and new analytical techniques used to interpret them are increasingly widespread. This expansion creates a need for tools to simulate serological surveys and the processes underlying the observed titer values, as this will enable researchers to identify best practices for serological survey design, and provide a standardized framework to evaluate the performance of different inference methods.serosimallows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological survey sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological surveys, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.Author SummaryPublic health researchers use serological surveys to obtain serum samples from individuals and measure antibody levels against one or more pathogens. When paired with appropriate analytical methods, these surveys can be used to determine whether individuals have been previously infected with or vaccinated against those pathogens. However, there is currently a lack of tools to simulate realistic serological survey data from the processes determining these observed antibody levels. We developedserosim, an open source R package which enables users to simulate serological survey data matching their disease system(s) of interest. This package allows users to specify and modify model inputs responsible for generating an individual’s antibody level at various levels, from the within-host processes to the observation process.serosimwill be useful for designing more informative serological surveys, better understanding the processes behind observed serological data, and assessing new serological survey analytical methods.