Estimation of R0 and Real-Time Reproduction Number from Epidemics
Deviation between theorietical incidence and observed data.
Check incidence vector in the input
Estimate generation time distribution
Estimate R0 from attack rate of an epidemic
Estimate R0 from exponential growth rate of an epidemic
Estimate R0 by Maximum Likelihood
Estimate the time dependent R using a Bayesian method
Estimate the Time-Dependent reproduction number
Estimate reproduction number (R0 or Rt) for one incidence dataset usin...
Joint estimation of R and generation time distribution for the ML meth...
Poisson log-likelihood for an observed epidemic
Generation Time distribution
Scaling of x-axis
Impute censored cases to rebuild longer epidemic vector
Audit input data for common issues
Integrity checks for input parameters
Plot a generation time distribution
Plot the R0/Rt value along with confidence interval
Plot of sensitivity analyses.
Plot the R0/Rt value along with confidence interval
Plot a model fit for R0.R objects
Plot a model fit for R0.sR objects
S3 method for objects of class R0.R or R0.sR
Internal plotfit method for AR estimates
Internal plotfit method for AR estimates
Internal plotfit method for EG, ML and TD estimates
Plot R0 for Attack Rate method
Plot R0 for Exponential Growth method
Plot R0 for Maximum Likelihood method
Plot R0 for Sequential Bayesian method
Plot R0 for Time-Dependent method
Print method for objects of class R0.GT
Print method for objects of class R0.R
Print method for objects of class R0.sR
Estimate reproduction number from exponential growth rate
Sensitivity of R0 to varying generation time distributions
Sensitivity of R0 to time estimation windows
Sensitivity analysis of basic reproduction ratio to begin/end dates
Influenza-like illness simulation (individual-based model)
Epidemic outbreak simulation
Smooth real-time reproduction number over larger time periods
Estimation of reproduction numbers for disease outbreak, based on incidence data. The R0 package implements several documented methods. It is therefore possible to compare estimations according to the methods used. Depending on the methods requested by user, basic reproduction number (commonly denoted as R0) or real-time reproduction number (referred to as R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs will give different graphs depending on the methods requested : basic reproductive number estimations will only show the epidemic curve (collected data) and an adjusted model, whereas real-time methods will also show the R(t) variations throughout the outbreak time period. Sensitivity analysis tools are also provided, and allow for investigating effects of varying Generation Time distribution or time window on estimates.