smooth.Rt: Smooth real-time reproduction number over larger time periods

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

Smooth real-time reproduction number over larger time periods

Description

Aggregate real-time estimates of R(t) (from the TD or even the SB methods) over larger time windows, while still accounting for the generation time distribution.

Usage

smooth.Rt(res, time.period)

Arguments

res	An object of class R0.R, created by any real-time method (currently implemented: TD and SB).
time.period	Time period to be used for aggregation.

Details

Regrouping Time-Dependant R(t) values, or even Real Time Bayesian most-likely R values (according to R distributions) should take into account the generation time. Results can be plotted exactly the same was as input estimations, except they won't show any goodness-of-fit curve.

Value

A list with components :

R	The estimate of the reproduction ratio.
conf.int	The 95% confidence interval for the R estimate.
GT	Generation time distribution uised in the computation.
epid	Original or augmented epidemic data, depending whether impute.values is set to FALSE or TRUE.
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 las day used for the fit.
data.name	The name of the dataset used.
call	Call used for the function.
method	Method used for fitting.
method.code	Internal code used to designate method.

Author(s)

Pierre-Yves Boelle, Thomas Obadia

Examples

#Loading package
library(R0)

## This script allows for generating a new estimation for RTB and TD methods.
## Estimations used as input are agregated by a time period provided by user.
## Results can be plotted exactly the same was as input estimations,
## except they won't show any goodness of fit curve.
data(Germany.1918)
mGT <- generation.time("gamma", c(3,1.5))
TD <- estimate.R(Germany.1918, mGT, begin=1, end=126, methods="TD", nsim=100)
TD
# Reproduction number estimate using  Time-Dependant  method.
# 2.322239 2.272013 1.998474 1.843703 2.019297 1.867488 1.644993 1.553265 1.553317 1.601317 ...
TD$estimates$TD$Rt.quant
#     Date      R.t. CI.lower.  CI.upper.
# 1      1 2.3222391 1.2000000  2.4000000
# 2      2 2.2720131 2.7500000  6.2500000
# 3      3 1.9984738 2.7500000  6.5000000
# 4      4 1.8437031 0.7368421  1.5789474
# 5      5 2.0192967 3.1666667  6.1666667
# 6      6 1.8674878 1.6923077  3.2307692
# 7      7 1.6449928 0.8928571  1.6428571
# 8      8 1.5532654 1.3043478  2.2608696
# 9      9 1.5533172 1.0571429  1.7428571
# 10    10 1.6013169 1.6666667  2.6666667
# ...

TD.weekly <- smooth.Rt(TD$estimates$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..

TD.weekly$Rt.quant
#    Date      R.t. CI.lower. CI.upper.
# 1     1 1.8784240 1.3571429 2.7380952
# 2     8 1.5809756 1.3311037 2.0100334
# 3    15 1.3569175 1.1700628 1.5308219
# 4    22 1.1316335 0.9961229 1.2445302
# 5    29 0.9615463 0.8365561 1.0453074
# 6    36 0.8118902 0.7132668 0.9365193
# 7    43 0.8045254 0.6596685 0.9325967
# 8    50 0.8395747 0.6776557 1.0402930
# 9    57 0.8542518 0.6490251 1.1086351
# 10   64 0.8258094 0.5836735 1.1142857
# 11   71 0.8543877 0.5224719 1.1460674
# 12   78 0.9776385 0.6228070 1.4912281
# 13   85 0.9517133 0.5304348 1.3652174
# 14   92 0.9272833 0.5045045 1.3423423
# 15   99 0.9635479 0.4875000 1.5125000
# 16  106 0.9508951 0.5000000 1.6670455
# 17  113 0.9827432 0.5281989 1.8122157
# 18  120 0.5843895 0.1103040 0.9490928

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