R0 (version 1.2-4)

sa.GT: Sensitivity analysis of reproduction ratio with varying GT distribution

Description

Sensitivity analysis of reproduction ratio with varying GT distribution.

Usage

sa.GT(incid, GT.type, GT.mean.range, GT.sd.range, begin = NULL, 
    end = NULL, est.method, t = NULL, date.first.obs = NULL, 
    time.step = 1, ...)

Arguments

incid

incident cases

GT.type

Type of distribution for GT (see GT.R for details)

GT.mean.range

mean used for all GT distributions throughout the simulation

GT.sd.range

Range of standard deviation used for GT distributions. Must be provided as a vector.

begin

begin date of the estimation of epidemic

end

end date of estimation of the epidemic

est.method

Estimation method used for sensitivity analysis. Requires a method computing a proper R0 value (and not an instantaneous R(t))

t

Dates vector to be passed to estimation function

date.first.obs

Optional date of first observation, if t not specified

time.step

Optional. If date of first observation is specified, number of day between each incidence observation

...

parameters passed to inner functions

Value

• A data frame s.a with following components :
• $GT.type
  Distribution law for GT.
• $GT.mean
  Range of means used for tested GTs.
• $GT.sd
  Range of standard deviations used for tested GTs.
• $R
  Computed value for Reproduction Number given GT.type, GT.mean and GT.sd.
• $conf.int[1]
  The lower limit of 95% CI for R.
• $conf.int[2]
  The upper limit of 95% CI for R.

Details

By using different Generation Time (GT) distribution, different estimates of reproduction ratio can be analyzed.

Examples

#Loading package
library(R0)

## Data is taken from the paper by Nishiura for key transmission parameters of an institutional
## outbreak during 1918 influenza pandemic in Germany)
## Here we will test GT with means of 1 to 5, each time with SD constant (1)
## GT and SD can be either fixed value or vectors of values
## Actual value in simulations may differ, as they are adapted according to the distribution type
data(Germany.1918)
tmp<-sa.GT(incid=Germany.1918, GT.type="gamma", GT.mean=seq(1,5,1), GT.sd.range=1, begin=1, end=27,
           est.method="EG")

## Results are stored in a matrix, each line dedicated to a (mean,sd) couple
plot(x=tmp[,"GT.Mean"], xlab="mean GT (days)", y=tmp[,"R"], ylim=c(1.2, 2.1), ylab="R0 (95% CI)",
     type="p", pch=19, col="black", main="Sensitivity of R0 to mean GT")
arrows(x0=as.numeric(tmp[,"GT.Mean"]), y0=as.numeric(tmp[,"CI[lower]"]), 
       y1=as.numeric(tmp[,"CI[upper]"]), angle=90, code=3, col="black", length=0.05)

## One could tweak this example to change sorting of values (per mean, or per standard deviation)
## eg: 'x=tmp[,c('GT.Mean')]' could become 'x=tmp[,c('GT.SD')]'