search

epiverse-trace / serofoi

Estimates the Force-of-Infection of a given pathogen from population based sero-prevalence studies
https://epiverse-trace.github.io/serofoi/
Other
17 stars 4 forks source link
antibodies bayesian-methods epidemiology epiverse r r-package serological-surveys stan-language

readme

serofoi: force-of-infection from population based serosurveys with age-disaggregated data

License: MIT R-CMD-check Codecov test coverage lifecycle-maturing

serofoi is an R package to estimate the Force-of-Infection (FoI) of a given pathogen from age-disaggregated population-based cross-sectional serosurveys, using a Bayesian framework. The package provides a set of features for assessing model fitting, convergence and visualisation.

serofoi relies on the rstan package, which provides an R interface for the Stan programming language for statistical Bayesian modelling. Particularly, serofoi relies on the use of a Hamiltonian Monte Carlo (HMC) algorithm implemented by Stan for Markov chain Monte Carlo (MCMC) sampling. The implemented methods are outlined in (Cucunubá et al. 2017) and (Carrera et al. 2020) (see FoI Models for further details)

serofoi is part of the Epiverse Initiative.

Installation

You can install the development version of serofoi from GitHub with:

if(!require("pak")) install.packages("pak")
pak::pak("epiverse-trace/serofoi")

Quick start

serofoi provides a minimal serosurvey dataset, serodata, that can be used to test out the package.

# Load example dataset chagas2012 included with the package
data(chagas2012)
head(chagas2012, 5)
#>       survey total counts age_min age_max tsur country  test         antibody
#> 1 COL-035-93    34      0       1       1 2012     COL ELISA IgG anti-T.cruzi
#> 2 COL-035-93    25      0       2       2 2012     COL ELISA IgG anti-T.cruzi
#> 3 COL-035-93    35      1       3       3 2012     COL ELISA IgG anti-T.cruzi
#> 4 COL-035-93    29      0       4       4 2012     COL ELISA IgG anti-T.cruzi
#> 5 COL-035-93    36      0       5       5 2012     COL ELISA IgG anti-T.cruzi

The function prepare_serodata will prepare the entry data for the use of the modelling module; this function computes the sample size, the years of birth and the binomial confidence interval for each age group in the provided dataset. A visualisation of the prepared seroprevalence data can be obtained using the function plot_seroprev:

serodata_test <- prepare_serodata(chagas2012)
plot_seroprev(serodata_test, size_text = 15)

Contributions

Contributors to the project include:

Package vignettes

More details on how to use serofoi can be found in the online documentation as package vignettes, under Get Started, An Introduction to FoI Models and Real-life Use Cases for serofoi

Help

To report a bug please open an issue.

Contribute

Contributions to serofoi are welcomed. Please follow the package contributing guide.

Code of conduct

Please note that the serofoi project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

References

<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">

Carrera, Jean-Paul, Zulma M. Cucunubá, Karen Neira, Ben Lambert, Yaneth Pittí, Jesus Liscano, Jorge L. Garzón, et al. 2020. “Endemic and Epidemic Human Alphavirus Infections in Eastern Panama: An Analysis of Population-Based Cross-Sectional Surveys.” *The American Journal of Tropical Medicine and Hygiene* 103 (6): 2429–37. .
Cucunubá, Zulma M, Pierre Nouvellet, Lesong Conteh, Mauricio Javier Vera, Victor Manuel Angulo, Juan Carlos Dib, Gabriel Jaime Parra -Henao, and María Gloria Basáñez. 2017. “Modelling Historical Changes in the Force-of-Infection of Chagas Disease to Inform Control and Elimination Programmes: Application in Colombia.” *BMJ Global Health* 2 (3): e000345. .