Citation
Hay, James A.; Minter, Amanda; Ainslie, Kylie E. C.; Lessler, Justin; Yang, Bingyi; Cummings, Derek A. T.; Kucharski, Adam J.; & Riley, Steven (2020). An Open Source Tool to Infer Epidemiological and Immunological Dynamics from Serological Data: Serosolver. PLOS Computational Biology, 16(5), e1007840. PMCID: PMC7241836Abstract
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.URL
http://dx.doi.org/10.1371/journal.pcbi.1007840Reference Type
Journal ArticleYear Published
2020Journal Title
PLOS Computational BiologyAuthor(s)
Hay, James A.Minter, Amanda
Ainslie, Kylie E. C.
Lessler, Justin
Yang, Bingyi
Cummings, Derek A. T.
Kucharski, Adam J.
Riley, Steven