An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver

• Published in: PLoS computational biology 2020-05, Vol.16 (5), p.e1007840-e1007840
• Main Authors: Hay, James A, Minter, Amanda, Ainslie, Kylie E C, Lessler, Justin, Yang, Bingyi, Cummings, Derek A T, Kucharski, Adam J, Riley, Steven
• Format: Article
• Language: English
• Subjects: Age; Antibodies; Antibodies - blood; Antibodies - immunology; Biology and Life Sciences; Case studies; Cross-reaction; Epidemiology; Health risks; Humans; Immune response; Immunology; Incidence; Infections; Infectious diseases; Influenza; Kinetics; Latent infection; Medicine and Health Sciences; Methods; Models, Biological; Pathogens; Public health; Serology; Software; Viruses
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007840

We present a flexible, open source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.