R0 (version 1.2-10)

est.R0.TD: Estimate the time dependent reproduction number

Description

Estimate the time dependent reproduction number according to Wallinga & Teunis.

Usage

est.R0.TD(epid, GT, import = NULL, 
    n.t0 = NULL, t = NULL, 
    begin = NULL, end = NULL, 
    date.first.obs = NULL, 
    time.step = 1, q = c(0.025, 
        0.975), correct = TRUE, 
    nsim = 10000, checked = FALSE, 
    ...)

Value

A list with components:

R

vector of R values.

conf.int

95% confidence interval for estimates.

P

Matrix of who infected whom.

p

Probability of who infected whom (values achieved by normalizing P matrix).

GT

Generation time distribution used in the computation.

epid

Original epidemic data.

import

Vector of imported cases.

pred

Theoretical epidemic data, computed with estimated values of R.

begin

Starting date for the fit.

begin.nb

The number of the first day used in the fit.

end

The end date for the fit.

end.nb

The number of the last day used for the fit.

data.name

Name of the data used in the fit.

call

Call used for the function.

method

Method for estimation.

method.code

Internal code used to designate method.

Arguments

epid

epidemic curve.

GT

generation time distribution.

import

Vector of imported cases.

n.t0

Number of cases at time 0.

t

Vector of dates at which incidence was measured.

begin

At what time estimation begins. Just here for "plot" purposes, not actually used

end

At what time estimation ends. Just here for "plot" purposes, not actually used

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.

q

Quantiles for R(t). By default, 5% and 95%

correct

Correction for cases not yet observed (real time).

nsim

Number of simulations to be run to compute quantiles for R(t)

checked

Internal flag used to check whether integrity checks were ran or not.

...

parameters passed to inner functions

Author

Pierre-Yves Boelle, Thomas Obadia

Details

For internal use. Called by est.R0.

CI is computed by multinomial simulations at each time step with the expected value of R.

References

Wallinga, J., and P. Teunis. "Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures." American Journal of Epidemiology 160, no. 6 (2004): 509.

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)

data(Germany.1918)
mGT<-generation.time("gamma", c(3, 1.5))
TD <- est.R0.TD(Germany.1918, mGT, begin=1, end=126, nsim=100)
# Warning messages:
# 1: In est.R0.TD(Germany.1918, mGT) : Simulations may take several minutes.
# 2: In est.R0.TD(Germany.1918, mGT) : Using initial incidence as initial number of cases.
TD
# Reproduction number estimate using  Time-Dependent  method.
# 2.322239 2.272013 1.998474 1.843703 2.019297 1.867488 1.644993 1.553265 1.553317 1.601317 ...

## An interesting way to look at these results is to agregate initial data by longest time unit,
## such as weekly incidence. This gives a global overview of the epidemic.
TD.weekly <- smooth.Rt(TD, 7)
TD.weekly
# Reproduction number estimate using  Time-Dependant  method.
# 1.878424 1.580976 1.356918 1.131633 0.9615463 0.8118902 0.8045254 0.8395747 0.8542518 0.8258094..
plot(TD.weekly)