Model Infectious Disease Parameters from Serosurveys
Compare models
Compute confidence interval for fractional polynomial model
Compute confidence interval for local polynomial model
Compute confidence interval for mixture model
Compute confidence interval for penalized_spline_model
Compute confidence interval
Compute confidence interval for Weibull model
Estimate the true sero prevalence using Bayesian estimation
Estimate force of infection
Estimate seroprevalence and foi by combining mixture model and regress...
The Farrington (1990) model.
Returns the powers of the GLM fitted model which has the lowest devian...
A fractional polynomial model.
Hierarchical Bayesian Model
A local polynomial model.
Fit a mixture model to classify serostatus
MSEIR model
Monotonize seroprevalence
Penalized Spline model
Plotting GCV values with respect to different nn-s and h-s parameters.
plot() overloading for result of estimate_from_mixture
plot() overloading for Farrington model
plot() overloading for fractional polynomial model
plot() overloading for hierarchical_bayesian_model
plot() overloading for local polynomial model
plot() overloading for mixture model
plot() overloading for MSEIR model
plot() overloading for penalized spline
plot() overloading for polynomial model
plot() overloading for SIR model
plot() overloading for SIR static model
plot() overloading for SIR sub populations model
plot() overloading for Weibull model
Polynomial models
serosv: model infectious disease parameters
Helper to adjust styling of a plot
Basic SIR model
SIR static model (age-heterogeneous, endemic equilibrium)
SIR Model with Interacting Subpopulations
Generate a dataframe with t, pos and tot columns from t and `s...
The Weibull model.
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>.
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