Package: serosv 1.0.0.9000

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]

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serosv.pdf |serosv.html✨
serosv/json (API)
NEWS

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

Peer review:

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

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

On CRAN:

6.28 score 20 scripts 318 downloads 25 exports 60 dependencies

Last updated 2 months agofrom:f78ef0e3f8. Checks:OK: 2 NOTE: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-win-x86_64NOTENov 07 2024
R-4.5-linux-x86_64OKNov 07 2024
R-4.4-win-x86_64NOTENov 07 2024
R-4.4-mac-x86_64NOTENov 07 2024
R-4.4-mac-aarch64NOTENov 07 2024
R-4.3-win-x86_64NOTENov 07 2024
R-4.3-mac-x86_64NOTENov 07 2024
R-4.3-mac-aarch64NOTENov 07 2024

Exports:compute_cicompute_ci.fp_modelcompute_ci.lp_modelcompute_ci.mixture_modelcompute_ci.penalized_spline_modelcompute_ci.weibull_modelest_foiestimate_from_mixturefarrington_modelfind_best_fp_powersfp_modelhierarchical_bayesian_modellp_modelmixture_modelmseir_modelpavapenalized_spline_modelplot_gcvpolynomial_modelset_plot_stylesir_basic_modelsir_static_modelsir_subpops_modeltransform_dataweibull_model

Dependencies:abindbackportsBHbootcallrcheckmateclicolorspacedescdeSolvedistributionaldplyrfansifarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecyclelocfitloomagrittrMASSMatrixmatrixStatsmgcvmixdistmunsellnlmenumDerivpatchworkpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsscalesStanHeaderstensorAtibbletidyselectutf8vctrsviridisLitewithr

Data transformation

Rendered fromdata_transformation.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-09-25
Started: 2024-05-15

Hierarchical Bayesian models

Rendered fromhierarchical_model.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-09-30
Started: 2024-05-15

Model visualization

Rendered fromvisualizing_model.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-09-25
Started: 2024-05-15

Modeling directly from antibody levels

Rendered frommodel_quantitative_data.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-09-25
Started: 2024-05-15

Nonparametric model

Rendered fromnonparametric_model.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-09-25
Started: 2024-05-15

Parametric models

Rendered fromparametric_model.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-10-08
Started: 2024-05-15

Semiparametric model

Rendered fromsemiparametric_model.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-09-30
Started: 2024-05-15

SIR model

Rendered fromsir_model.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-09-30
Started: 2024-09-30

Readme and manuals

Help Manual

Help pageTopics
serosv: model infectious disease parametersserosv-package serosv
Compute confidence intervalcompute_ci
Compute confidence interval for fractional polynomial modelcompute_ci.fp_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 force of infectionest_foi
Estimate seroprevalence and foi by combining mixture model and regressionestimate_from_mixture
The Farrington (1990) model.farrington_model
Returns the powers of the GLM fitted 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
MSEIR modelmseir_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
Plotting GCV values with respect to different nn-s and h-s parameters.plot_gcv
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 MSEIR modelplot.mseir_model
plot() overloading for penalized splineplot.penalized_spline_model
plot() overloading for polynomial modelplot.polynomial_model
plot() overloading for SIR modelplot.sir_basic_model
plot() overloading for SIR static modelplot.sir_static_model
plot() overloading for SIR sub populations modelplot.sir_subpops_model
plot() overloading for Weibull modelplot.weibull_model
Polynomial modelspolynomial_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
Basic SIR modelsir_basic_model
SIR static model (age-heterogeneous, endemic equilibrium)sir_static_model
SIR Model with Interacting Subpopulationssir_subpops_model
Tuberculosis serological data from the Netherlands 1966-1973 (aggregated)tb_nl_1966_1973
Generate a dataframe with `t`, `pos` and `tot` columns from `t` and `seropositive` vectors.transform_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