R0 (version 1.2-6)

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

Description

Smooth real-time reproduction number over larger time period

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 computations.

Value

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.

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.

Examples

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