est.R0.TD: Estimate the Time-Dependent reproduction number

In R0: Estimation of R0 and Real-Time Reproduction Number from Epidemics

Estimate the Time-Dependent reproduction number

Description

Estimate the Time-Dependent reproduction number, R(t), as defined by Wallinga & Teunis, by exploring all possible pairs of infectors/infectees across likely transmission trees.

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,
  ...
)

Arguments

epid	Object containing epidemic curve data.
GT	Generation time distribution from generation.time().
import	Vector of imported cases.
n.t0	Initial number of cases at the beginning of the outbreak.
t	Vector of dates at which incidence was observed.
begin	At what time estimation begins (unused by this method, just there for plotting purposes).
end	At what time estimation ends (unused by this method, just there for plotting purposes).
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) confidence level. By default, 2.5% and 97.5%.
correct	Boolean. 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.

Details

For internal use. Called by estimate.R().

The confidence interval is computed by multinomial simulations at each time step, using the expected value of R.

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.

Note

This is the implementation of the method provided by Wallinga & Teunis (2004). Correction for estimation in real time is implemented as in Cauchemez et al, AJE (2006).

If imported cases are provided, they are counted in addition to autonomous cases. The final plot will show overall incidence.

Author(s)

Pierre-Yves Boelle, Thomas Obadia

References

Wallinga, J., and Teunis P. "Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures." American Journal of Epidemiology 160, no. 6 (2004): 509.
Cauchemez S., and Valleron AJ. "Estimating in Real Time the Efficacy of Measures to Control Emerging Communicable Diseases" American Journal of Epidemiology 164, no. 6 (2006): 591.

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)

R0 documentation built on Sept. 26, 2023, 5:10 p.m.