RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies.
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012777 |
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| author | Nathanaël Hozé Margarita Pons-Salort C Jessica E Metcalf Michael White Henrik Salje Simon Cauchemez |
| author_facet | Nathanaël Hozé Margarita Pons-Salort C Jessica E Metcalf Michael White Henrik Salje Simon Cauchemez |
| author_sort | Nathanaël Hozé |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-328f7b032dfa481f8a4b923eb1f3798d |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-328f7b032dfa481f8a4b923eb1f3798d2025-08-20T02:27:06ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-02-01212e101277710.1371/journal.pcbi.1012777RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies.Nathanaël HozéMargarita Pons-SalortC Jessica E MetcalfMichael WhiteHenrik SaljeSimon CauchemezPopulation-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.https://doi.org/10.1371/journal.pcbi.1012777 |
| spellingShingle | Nathanaël Hozé Margarita Pons-Salort C Jessica E Metcalf Michael White Henrik Salje Simon Cauchemez RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies. PLoS Computational Biology |
| title | RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies. |
| title_full | RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies. |
| title_fullStr | RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies. |
| title_full_unstemmed | RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies. |
| title_short | RSero: A user-friendly R package to reconstruct pathogen circulation history from seroprevalence studies. |
| title_sort | rsero a user friendly r package to reconstruct pathogen circulation history from seroprevalence studies |
| url | https://doi.org/10.1371/journal.pcbi.1012777 |
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