Package: serosv 1.2.0.9000

Anh Phan Truong Quynh

serosv: Model Infectious Disease Parameters from Serosurveys

An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.

Authors:Anh Phan Truong Quynh [aut, cre], Nguyen Pham Nguyen The [aut], Long Bui Thanh [aut], Tuyen Huynh [aut], Thinh Ong [aut], Marc Choisy [aut]

serosv_1.2.0.9000.tar.gz
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|manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
serosv/json (API)

# Install 'serosv' in R:
install.packages('serosv', repos = c('https://oucru-modelling.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/oucru-modelling/serosv/issues

Pkgdown/docs site:https://oucru-modelling.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

6.80 score 57 scripts 528 downloads 23 exports 72 dependencies

Last updated from:ae7864fa0c. Checks:11 NOTE, 1 ERROR, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE455
linux-devel-x86_64NOTE437
source / vignettesERROR676
linux-release-arm64NOTE426
linux-release-x86_64NOTE475
macos-release-arm64NOTE277
macos-release-x86_64NOTE644
macos-oldrel-arm64NOTE291
macos-oldrel-x86_64NOTE785
windows-develNOTE647
windows-releaseNOTE594
windows-oldrelNOTE580
wasm-releaseFAIL199

Exports:add_thresholdsage_time_modelcompare_modelscorrect_prevalenceest_foiestimate_from_mixturefarrington_modelfp_modelhierarchical_bayesian_modellp_modelmixture_modelpavapenalized_spline_modelplot_corrected_prevplot_gcvplot_standard_curveplot_titer_qcpolynomial_modelset_plot_stylestandardize_datato_titertransform_dataweibull_model

Dependencies:abindassertthatbackportsBHbootcallrcheckmateclicpp11descdeSolvedistributionaldplyrfarvergenericsggplot2gluegridExtragtablehmsinlineisobandjanitorlabelinglatticelifecyclelocfitloolubridatemagrittrMatrixmatrixStatsmgcvmixdistmvtnormnlmenumDerivotelpatchworkpillarpkgbuildpkgconfigposteriorpROCprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsS7scalesscamsnakecaseStanHeadersstringistringrtensorAtibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithr

Model selection
Generate models comparison data.frame | Generate custom metrics
Rendered frommodel_selection.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2025-02-11

Convert assay readings to titer
Overview | Convert to titer workflow | Config | 4PL function: OD = D + (A - D) / (1 + 10^((log10(conc) - c) * b)) | Assign each sample a "true" concentration (UI/ml) | ~60% positive (conc > 0.1), ~40% negative | Negative control concentrations per plate (one fixed OD per plate per dilution) | Antitoxin | Generate one row per sample × dilution, compute OD via 4PL + noise | Custom models
Rendered fromreading_to_titer.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2026-03-27

Data transformation
Aggregate data
Rendered fromdata_transformation.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2024-05-15

Imperfect serological test
Imperfect test | Fitting corrected data
Rendered fromimperfect_test.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2024-11-26

Input data
Input data format | Data transformation
Rendered fromlinelisting_vs_aggregated.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2025-03-21

Model repeated cross-sectional data
Age-time varying model
Rendered fromrepeated_cross_sectional.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2025-11-18

Model visualization
Visualize model | Customize the plot | Built-in function | ggplot2 functions
Rendered fromvisualizing_model.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2024-05-15

Modeling directly from antibody levels
Mixture model
Rendered frommodel_quantitative_data.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2024-05-15

Nonparametric model
Local estimation by polynomial
Rendered fromnonparametric_model.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2024-05-15

Parametric models
Frequentist methods | Polynomial models | Fractional polynomial model | Nonlinear models | Farrington model | Weibull model | Bayesian methods | Farrington | Log-logistic
Rendered fromparametric_model.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2024-05-15

Semiparametric model
Penalized splines | Penalized likelihood framework | Generalized Linear Mixed Model framework
Rendered fromsemiparametric_model.Rmdusingknitr::rmarkdown

Last update: 2026-03-27
Started: 2024-05-15

Readme and manuals

Help Manual

Help pageTopics
serosv: model infectious disease parametersserosv-package serosv
Visualize positive threshold at different dilution factorsadd_thresholds
Age-time varying seroprevalenceage_time_model
Generate table of metrics for model comparisoncompare_models
Compute confidence interval for time age modelcompute_ci.age_time_model
Compute confidence interval for a model of serosvcompute_ci.default
Compute confidence interval for fractional polynomial modelcompute_ci.fp_model
Compute 95% credible interval for hierarchical Bayesian modelcompute_ci.hierarchical_bayesian_model
Compute confidence interval for local polynomial modelcompute_ci.lp_model
Compute confidence interval for mixture modelcompute_ci.mixture_model
Compute confidence interval for penalized_spline_modelcompute_ci.penalized_spline_model
Compute confidence interval for Weibull modelcompute_ci.weibull_model
Estimate the true sero prevalence using Frequentist/Bayesian estimationcorrect_prevalence
Estimate force of infectionest_foi
Estimate seroprevalence and FOI from a fixed mixture modelestimate_from_mixture
The Farrington (1990) model.farrington_model
Returns the powers of the fractional polynomial model which has the lowest deviance score.find_best_fp_powers
A fractional polynomial model.fp_model
Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated)hav_be_1993_1994
Hepatitis A serological data from Belgium in 2002 (line listing)hav_be_2002
Hepatitis A serological data from Bulgaria in 1964 (aggregated)hav_bg_1964
Hepatitis B serological data from Russia in 1999 (aggregated)hbv_ru_1999
Hepatitis C serological data from Belgium in 2006 (line listing)hcv_be_2006
Hierarchical Bayesian Modelhierarchical_bayesian_model
A local polynomial model.lp_model
Fit a mixture model to classify serostatusmixture_model
Mumps serological data from the UK in 1986 and 1987 (aggregated)mumps_uk_1986_1987
Parvo B19 serological data from Belgium from 2001-2003 (line listing)parvob19_be_2001_2003
Parvo B19 serological data from England and Wales in 1996 (line listing)parvob19_ew_1996
Parvo B19 serological data from Finland from 1997-1998 (line listing)parvob19_fi_1997_1998
Parvo B19 serological data from Italy from 2003-2004 (line listing)parvob19_it_2003_2004
Parvo B19 serological data from Poland from 1995-2004 (line listing)parvob19_pl_1995_2004
Monotonize seroprevalencepava
Penalized Spline modelpenalized_spline_model
Plot output for corrected_prevalenceplot_corrected_prev
Plotting GCV values with respect to different nn-s and h-s parameters.plot_gcv
Visualize standard curves for each plateplot_standard_curve
Quality control plotplot_titer_qc
Plot output for age_time_modelplot.age_time_model
plot() overloading for result of estimate_from_mixtureplot.estimate_from_mixture
plot() overloading for Farrington modelplot.farrington_model
plot() overloading for fractional polynomial modelplot.fp_model
plot() overloading for hierarchical_bayesian_modelplot.hierarchical_bayesian_model
plot() overloading for local polynomial modelplot.lp_model
plot() overloading for mixture modelplot.mixture_model
plot() overloading for penalized splineplot.penalized_spline_model
plot() overloading for polynomial modelplot.polynomial_model
plot() overloading for Weibull modelplot.weibull_model
Polynomial modelspolynomial_model
Predict from the age_time_mdoelpredict.age_time_model
Prediction for serosv Farrington modelpredict.farrington_model
Prediction for serosv fractional polynomial modelpredict.fp_model
Predict from an hierarchical bayesian modelpredict.hierarchical_bayesian_model
Prediction for serosv local polynomial modelpredict.lp_model
Prediction for serosv penalized spline modelpredict.penalized_spline_model
Prediction for serosv polynomial modelpredict.polynomial_model
Prediction for serosv Weibull modelpredict.weibull_model
Rubella - Mumps data from the UK (aggregated)rubella_mumps_uk
Rubella serological data from the UK in 1986 and 1987 (aggregated)rubella_uk_1986_1987
Helper to adjust styling of a plotset_plot_style
Standardize raw serological test data for titer conversionstandardize_data
Tuberculosis serological data from the Netherlands 1966-1973 (aggregated)tb_nl_1966_1973
Convert assay readings to titersto_titer
Aggregate datatransform_data
VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated)vzv_be_1999_2000
VZV serological data from Belgium from 2001-2003 (line listing)vzv_be_2001_2003
VZV and Parvovirus B19 serological data in Belgium (line listing)vzv_parvo_be
The Weibull model.weibull_model