Function reference • serosv

Dataset

Hepatitis A

hav_be_1993_1994: Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated)

hav_be_2002: Hepatitis A serological data from Belgium in 2002 (line listing)

hav_bg_1964: Hepatitis A serological data from Bulgaria in 1964 (aggregated)

Hepatitis B

hbv_ru_1999: Hepatitis B serological data from Russia in 1999 (aggregated)

Hepatitis C

hcv_be_2006: Hepatitis C serological data from Belgium in 2006 (line listing)

Mumps

mumps_uk_1986_1987: Mumps serological data from the UK in 1986 and 1987 (aggregated)

Parvo B19

parvob19_be_2001_2003: Parvo B19 serological data from Belgium from 2001-2003 (line listing)

parvob19_ew_1996: Parvo B19 serological data from England and Wales in 1996 (line listing)

parvob19_fi_1997_1998: Parvo B19 serological data from Finland from 1997-1998 (line listing)

parvob19_it_2003_2004: Parvo B19 serological data from Italy from 2003-2004 (line listing)

parvob19_pl_1995_2004: Parvo B19 serological data from Poland from 1995-2004 (line listing)

Rubella

rubella_uk_1986_1987: Rubella serological data from the UK in 1986 and 1987 (aggregated)

Tuberculosis

tb_nl_1966_1973: Tuberculosis serological data from the Netherlands 1966-1973 (aggregated)

VZV

vzv_be_1999_2000: VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated)

vzv_be_2001_2003: VZV serological data from Belgium from 2001-2003 (line listing)

Multivariate data

rubella_mumps_uk: Rubella - Mumps data from the UK (aggregated)

vzv_parvo_be: VZV and Parvovirus B19 serological data in Belgium (line listing)

Models

sir_basic_model(): Basic SIR model

sir_static_model(): SIR static model (age-heterogeneous, endemic equilibrium)

sir_subpops_model(): SIR Model with Interacting Subpopulations

mseir_model(): MSEIR model

polynomial_model(): Polynomial models

farrington_model(): The Farrington (1990) model.

weibull_model(): The Weibull model.

fp_model(): A fractional polynomial model.

lp_model(): A local polynomial model.

hierarchical_bayesian_model(): Hierarchical Bayesian Model

penalized_spline_model(): Penalized Spline model

mixture_model(): Fit a mixture model to classify serostatus

estimate_from_mixture(): Estimate seroprevalence and foi by combining mixture model and regression

age_time_model(): Age-time varying seroprevalence Fit age-stratified seroprevalence across multiple time points. Also try to monotonize age (or cohort) - specific seroprevalence.

Plotting functions

plot_gcv(): Plotting GCV values with respect to different nn-s and h-s parameters.

plot(<polynomial_model>): plot() overloading for polynomial model

plot(<farrington_model>): plot() overloading for Farrington model

plot(<weibull_model>): plot() overloading for Weibull model

plot(<fp_model>): plot() overloading for fractional polynomial model

plot(<lp_model>): plot() overloading for local polynomial model

plot(<mseir_model>): plot() overloading for MSEIR model

plot(<sir_basic_model>): plot() overloading for SIR model

plot(<sir_static_model>): plot() overloading for SIR static model

plot(<sir_subpops_model>): plot() overloading for SIR sub populations model

plot(<hierarchical_bayesian_model>): plot() overloading for hierarchical_bayesian_model

plot(<penalized_spline_model>): plot() overloading for penalized spline

plot(<mixture_model>): plot() overloading for mixture model

plot(<estimate_from_mixture>): plot() overloading for result of estimate_from_mixture

plot(<age_time_model>): Plot output for age_time_model

set_plot_style(): Helper to adjust styling of a plot

Other utilities

serosv serosv-package: serosv: model infectious disease parameters

est_foi(): Estimate force of infection

pava(): Monotonize seroprevalence

correct_prevalence(): Estimate the true sero prevalence using Bayesian estimation

transform_data(): Generate a dataframe with `t`, `pos` and `tot` columns from `t` and `seropositive` vectors.

compare_models(): Compare models

compute_ci(): Compute confidence interval

compute_ci.fp_model(): Compute confidence interval for fractional polynomial model

compute_ci.lp_model(): Compute confidence interval for local polynomial model

compute_ci.weibull_model(): Compute confidence interval for Weibull model

compute_ci.penalized_spline_model(): Compute confidence interval for penalized_spline_model

compute_ci.mixture_model(): Compute confidence interval for mixture model

compute_ci.age_time_model(): Compute confidence interval for time age model

find_best_fp_powers(): Returns the powers of the GLM fitted model which has the lowest deviance score.