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#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
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