File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

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

TitleAn open source tool to infer epidemiological and immunological dynamics from serological data: Serosolver
Authors
Issue Date2020
Citation
PLoS Computational Biology, 2020, v. 16, n. 5, article no. e1007840 How to Cite?
AbstractWe 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 crossreaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.
Persistent Identifierhttp://hdl.handle.net/10722/318828
ISSN
2023 Impact Factor: 3.8
2023 SCImago Journal Rankings: 1.652
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHay, James A.-
dc.contributor.authorMinter, Amanda-
dc.contributor.authorAinslie, Kylie E.C.-
dc.contributor.authorLessler, Justin-
dc.contributor.authorYang, Bingyi-
dc.contributor.authorCummings, Derek A.T.-
dc.contributor.authorKucharski, Adam J.-
dc.contributor.authorRiley, Steven-
dc.date.accessioned2022-10-11T12:24:39Z-
dc.date.available2022-10-11T12:24:39Z-
dc.date.issued2020-
dc.identifier.citationPLoS Computational Biology, 2020, v. 16, n. 5, article no. e1007840-
dc.identifier.issn1553-734X-
dc.identifier.urihttp://hdl.handle.net/10722/318828-
dc.description.abstractWe 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 crossreaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.-
dc.languageeng-
dc.relation.ispartofPLoS Computational Biology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleAn open source tool to infer epidemiological and immunological dynamics from serological data: Serosolver-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pcbi.1007840-
dc.identifier.pmid32365062-
dc.identifier.pmcidPMC7241836-
dc.identifier.scopuseid_2-s2.0-85085263450-
dc.identifier.volume16-
dc.identifier.issue5-
dc.identifier.spagearticle no. e1007840-
dc.identifier.epagearticle no. e1007840-
dc.identifier.eissn1553-7358-
dc.identifier.isiWOS:000538053200011-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats