RSero: a user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Population-based serological surveys are a key tool in epidemiology to characterize the level of population immunity and reconstruct the past circulation of pathogens. A variety of serocatalytic models have been developed to estimate the force of infection (FOI) (i.e., the rate at which susceptible individuals become infected) from age-stratified seroprevalence data. However, few tool currently exists to easily implement, combine, and compare these models. Here, we introduce an R package, Rsero, that implements a series of serocatalytic models and estimates the FOI from age-stratified seroprevalence data using Bayesian methods. The package also contains a series of features to perform model comparison and visualise model fit. We introduce new serocatalytic models of successive outbreaks and extend existing models of seroreversion to any transmission model. The different features of the package are illustrated with simulated and real-life data. We show we can identify the correct epidemiological scenario and recover model parameters in different epidemiological settings. We also show how the package can support serosurvey study design in a variety of epidemic situations. This package provides a standard framework to epidemiologists and modellers to study the dynamics of past pathogen circulation from cross-sectional serological survey data.
Editor(s)
Scarpino, Samuel V
Date Issued
2025-02-03
Date Acceptance
2025-01-09
Citation
PLoS Computational Biology, 2025, 21 (2)
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS Computational Biology
Volume
21
Issue
2
Copyright Statement
Copyright: © 2025 Hozé et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39899643
PII: PCOMPBIOL-D-24-00337
Subjects
Seroepidemiologic Studies
Humans
Software
Bayes Theorem
Computational Biology
Computer Simulation
Disease Outbreaks
Communicable Diseases
Publication Status
Published
Coverage Spatial
United States
Article Number
e1012777
Date Publish Online
2025-02-03