Forecast Infections and the Time-Varying Reproduction Number
Source:R/forecast_infections.R
forecast_infections.Rd![[Experimental]](figures/lifecycle-experimental.svg)
Provides optional tools for forecasting cases and Rt estimates using the timeseries methods
(via the EpiSoon package). It requires the Episoon package. Installation instructions for the EpiSoon package are
available here.
forecast_infections(
infections,
rts,
gt_mean,
gt_sd,
gt_max = 30,
ensemble_type = "mean",
forecast_model,
CrIs = c(0.2, 0.5, 0.9),
horizon = 14,
samples = 1000
)Arguments
| infections | A data frame of cases by date of infection containing the following variables: date, mean, sd |
|---|---|
| rts | A data frame of Rt estimates by date of infection containing the following variables: date, mean, sd |
| gt_mean | Numeric, the mean of the gamma distributed generation time. |
| gt_sd | Numeric, the standard deviation of the gamma distributed generation time. |
| gt_max | Numeric, the maximum allowed value of the gamma distributed generation time. |
| ensemble_type | Character string indicating the type of ensemble to use. By default this is an unweighted ensemble ("mean") with no other types currently supported. |
| forecast_model | An uninitialised forecast model function to be passed to |
| CrIs | Numeric vector of credible intervals to calculate. |
| horizon | Numeric, defaults to 14. The horizon over which to forecast Rts and cases. |
| samples | Numeric, the number of forecast samples to take. |
Value
A list of data.tables. The first entry ("samples") contains raw forecast samples and the second entry ("summarised") contains
summarised forecasts.
Examples
# \donttest{
if (requireNamespace("EpiSoon")) {
if (requireNamespace("forecastHybrid")) {
# example case data
reported_cases <- example_confirmed[1:40]
generation_time <- get_generation_time(disease = "SARS-CoV-2", source = "ganyani")
incubation_period <- get_incubation_period(disease = "SARS-CoV-2", source = "lauer")
reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 15)
# estimate Rt and infections from data
out <- estimate_infections(reported_cases,
generation_time = generation_time,
delays = delay_opts(incubation_period, reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1))
)
# forecast Rt and infections from estimates
forecast <- forecast_infections(
infections = out$summarised[variable == "infections"],
rts = out$summarised[variable == "R"],
gt_mean = out$summarised[variable == "gt_mean"]$mean,
gt_sd = out$summarised[variable == "gt_sd"]$mean,
gt_max = 30,
forecast_model = function(y, ...) {
EpiSoon::forecastHybrid_model(
y = y[max(1, length(y) - 21):length(y)],
model_params = list(models = "aefz", weights = "equal"),
forecast_params = list(PI.combination = "mean"), ...
)
},
horizon = 14,
samples = 1000
)
forecast$summarised
}
}
#> Loading required namespace: EpiSoon
#> Loading required namespace: forecastHybrid
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
#> date type forecast_type median mean sd
#> 1: 2020-04-09 case case 4.361257e+04 5.179637e+04 3.842355e+04
#> 2: 2020-04-10 case case 4.480164e+04 5.284711e+04 3.900482e+04
#> 3: 2020-04-11 case case 4.378107e+04 5.091242e+04 3.785541e+04
#> 4: 2020-04-12 case case 4.704245e+04 5.268693e+04 3.809546e+04
#> 5: 2020-04-13 case case 4.496785e+04 5.081469e+04 3.762873e+04
#> 6: 2020-04-14 case case 4.163278e+04 5.002142e+04 3.827178e+04
#> 7: 2020-04-15 case case 4.336286e+04 5.025689e+04 3.782066e+04
#> 8: 2020-04-16 case case 4.736932e+04 5.419490e+04 3.938636e+04
#> 9: 2020-04-17 case case 4.713655e+04 5.382560e+04 3.781845e+04
#> 10: 2020-04-18 case case 4.544226e+04 5.404890e+04 4.055475e+04
#> 11: 2020-04-19 case case 4.299618e+04 5.201827e+04 3.928332e+04
#> 12: 2020-04-20 case case 4.735210e+04 5.639164e+04 4.097216e+04
#> 13: 2020-04-21 case case 4.677755e+04 5.409947e+04 3.851058e+04
#> 14: 2020-04-22 case case 4.540849e+04 5.260037e+04 3.867794e+04
#> 15: 2020-04-09 case ensemble 4.747432e+04 5.100040e+04 2.675306e+04
#> 16: 2020-04-10 case ensemble 4.781060e+04 5.195134e+04 2.814292e+04
#> 17: 2020-04-11 case ensemble 4.510429e+04 5.027671e+04 2.621818e+04
#> 18: 2020-04-12 case ensemble 4.930932e+04 5.175284e+04 2.616195e+04
#> 19: 2020-04-13 case ensemble 4.689482e+04 5.016650e+04 2.615128e+04
#> 20: 2020-04-14 case ensemble 4.696660e+04 5.062006e+04 2.709657e+04
#> 21: 2020-04-15 case ensemble 4.715109e+04 5.092776e+04 2.687568e+04
#> 22: 2020-04-16 case ensemble 4.911730e+04 5.188285e+04 2.678934e+04
#> 23: 2020-04-17 case ensemble 4.877508e+04 5.206228e+04 2.732457e+04
#> 24: 2020-04-18 case ensemble 4.818205e+04 5.259961e+04 2.926484e+04
#> 25: 2020-04-19 case ensemble 4.706834e+04 5.064188e+04 2.723562e+04
#> 26: 2020-04-20 case ensemble 5.085733e+04 5.360783e+04 2.718269e+04
#> 27: 2020-04-21 case ensemble 4.980664e+04 5.273912e+04 2.786295e+04
#> 28: 2020-04-22 case ensemble 4.857368e+04 5.131235e+04 2.665171e+04
#> 29: 2020-04-09 case rt 4.251600e+04 5.020442e+04 3.695308e+04
#> 30: 2020-04-10 case rt 4.116250e+04 5.105558e+04 3.927718e+04
#> 31: 2020-04-11 case rt 4.350750e+04 4.964101e+04 3.582798e+04
#> 32: 2020-04-12 case rt 4.402150e+04 5.081875e+04 3.840132e+04
#> 33: 2020-04-13 case rt 4.265750e+04 4.951830e+04 3.543359e+04
#> 34: 2020-04-14 case rt 4.366500e+04 5.121870e+04 3.799832e+04
#> 35: 2020-04-15 case rt 4.490400e+04 5.159863e+04 3.839141e+04
#> 36: 2020-04-16 case rt 4.391300e+04 4.957079e+04 3.687296e+04
#> 37: 2020-04-17 case rt 4.175000e+04 5.029896e+04 3.891627e+04
#> 38: 2020-04-18 case rt 4.272900e+04 5.115032e+04 3.965696e+04
#> 39: 2020-04-19 case rt 4.147850e+04 4.926549e+04 3.859816e+04
#> 40: 2020-04-20 case rt 4.447100e+04 5.082401e+04 3.692910e+04
#> 41: 2020-04-21 case rt 4.498350e+04 5.137877e+04 3.820308e+04
#> 42: 2020-04-22 case rt 4.283200e+04 5.002434e+04 3.616813e+04
#> 43: 2020-04-09 rt <NA> 7.346717e-01 7.180234e-01 2.933906e-01
#> 44: 2020-04-10 rt <NA> 7.171033e-01 7.253447e-01 2.930715e-01
#> 45: 2020-04-11 rt <NA> 7.099495e-01 7.147906e-01 3.026072e-01
#> 46: 2020-04-12 rt <NA> 7.223387e-01 7.380598e-01 3.051750e-01
#> 47: 2020-04-13 rt <NA> 7.288442e-01 7.260374e-01 3.056252e-01
#> 48: 2020-04-14 rt <NA> 7.081314e-01 7.154773e-01 3.028979e-01
#> 49: 2020-04-15 rt <NA> 7.163385e-01 7.265538e-01 3.110362e-01
#> 50: 2020-04-16 rt <NA> 7.159376e-01 7.163027e-01 2.989394e-01
#> 51: 2020-04-17 rt <NA> 7.302236e-01 7.241897e-01 3.081601e-01
#> 52: 2020-04-18 rt <NA> 6.993316e-01 7.120140e-01 3.122708e-01
#> 53: 2020-04-19 rt <NA> 7.032378e-01 7.096514e-01 3.104571e-01
#> 54: 2020-04-20 rt <NA> 7.348019e-01 7.389002e-01 3.163051e-01
#> 55: 2020-04-21 rt <NA> 7.040942e-01 7.072294e-01 3.194599e-01
#> 56: 2020-04-22 rt <NA> 7.063611e-01 7.139628e-01 2.875781e-01
#> date type forecast_type median mean sd
#> lower_90 lower_50 lower_20 upper_20 upper_50
#> 1: 5.129176e+03 2.236827e+04 3.528216e+04 5.468599e+04 7.311445e+04
#> 2: 5.119948e+03 2.189066e+04 3.524490e+04 5.596248e+04 7.477928e+04
#> 3: 4.886382e+03 2.031358e+04 3.341203e+04 5.418889e+04 7.395538e+04
#> 4: 5.114885e+03 2.290748e+04 3.647069e+04 5.598601e+04 7.504978e+04
#> 5: 4.497218e+03 2.004449e+04 3.400409e+04 5.551702e+04 7.279119e+04
#> 6: 4.720321e+03 2.022811e+04 3.333523e+04 5.353067e+04 7.180133e+04
#> 7: 3.430744e+03 1.982409e+04 3.288970e+04 5.321976e+04 7.350174e+04
#> 8: 5.310500e+03 2.245703e+04 3.655429e+04 5.821979e+04 7.845383e+04
#> 9: 3.939762e+03 2.432756e+04 3.726695e+04 5.780278e+04 7.731172e+04
#> 10: 4.066561e+03 2.265234e+04 3.536220e+04 5.721156e+04 7.609471e+04
#> 11: 4.629057e+03 2.199569e+04 3.416892e+04 5.252650e+04 7.215512e+04
#> 12: 4.606789e+03 2.370075e+04 3.909501e+04 5.996162e+04 8.026918e+04
#> 13: 5.788834e+03 2.329423e+04 3.668288e+04 5.723181e+04 7.856556e+04
#> 14: 4.431385e+03 2.167032e+04 3.526818e+04 5.617901e+04 7.525352e+04
#> 15: 1.297745e+04 3.113864e+04 4.096077e+04 5.470448e+04 6.801334e+04
#> 16: 1.357227e+04 3.183623e+04 4.115922e+04 5.457116e+04 6.913294e+04
#> 17: 1.366846e+04 3.115814e+04 3.941232e+04 5.310812e+04 6.721022e+04
#> 18: 1.393966e+04 3.288385e+04 4.200644e+04 5.578969e+04 6.846140e+04
#> 19: 1.313930e+04 3.069082e+04 4.064896e+04 5.297504e+04 6.767319e+04
#> 20: 1.288592e+04 3.055990e+04 4.089123e+04 5.441642e+04 6.637021e+04
#> 21: 1.318944e+04 3.051531e+04 4.033115e+04 5.372112e+04 6.836773e+04
#> 22: 1.342721e+04 3.135147e+04 4.240787e+04 5.648501e+04 6.815526e+04
#> 23: 1.408098e+04 3.193701e+04 4.190982e+04 5.613876e+04 6.813042e+04
#> 24: 1.214271e+04 3.124942e+04 4.139826e+04 5.590830e+04 6.998525e+04
#> 25: 1.273793e+04 3.087451e+04 4.097213e+04 5.285712e+04 6.467573e+04
#> 26: 1.439047e+04 3.313735e+04 4.404855e+04 5.731895e+04 7.048284e+04
#> 27: 1.317511e+04 3.132226e+04 4.141118e+04 5.717922e+04 7.122244e+04
#> 28: 1.452419e+04 3.103731e+04 4.112941e+04 5.529128e+04 6.836662e+04
#> 29: 4.135050e+03 2.130650e+04 3.371480e+04 5.518300e+04 7.156575e+04
#> 30: 4.539800e+03 2.030025e+04 3.370280e+04 5.331880e+04 7.363075e+04
#> 31: 3.924750e+03 2.109800e+04 3.360540e+04 5.303700e+04 7.029975e+04
#> 32: 3.423550e+03 1.872025e+04 3.325080e+04 5.496920e+04 7.413125e+04
#> 33: 4.293600e+03 2.237275e+04 3.464500e+04 5.229920e+04 6.966650e+04
#> 34: 4.249750e+03 2.076000e+04 3.311660e+04 5.525700e+04 7.660125e+04
#> 35: 4.536250e+03 2.158000e+04 3.432860e+04 5.394360e+04 7.396150e+04
#> 36: 4.112700e+03 1.875775e+04 3.378120e+04 5.420300e+04 7.134300e+04
#> 37: 3.786650e+03 1.951000e+04 3.361880e+04 5.155320e+04 7.122975e+04
#> 38: 4.365200e+03 1.966450e+04 3.261240e+04 5.371360e+04 7.403325e+04
#> 39: 3.345400e+03 1.790500e+04 3.077200e+04 5.181640e+04 7.181175e+04
#> 40: 4.486600e+03 2.235775e+04 3.546080e+04 5.390320e+04 7.264475e+04
#> 41: 3.373400e+03 2.035550e+04 3.501260e+04 5.608780e+04 7.232250e+04
#> 42: 5.725450e+03 2.248850e+04 3.401180e+04 5.143880e+04 7.071550e+04
#> 43: 2.213965e-01 5.206046e-01 6.410478e-01 8.048943e-01 9.106870e-01
#> 44: 2.471009e-01 5.167841e-01 6.506494e-01 7.927585e-01 9.239287e-01
#> 45: 2.361419e-01 5.016504e-01 6.275936e-01 7.886030e-01 9.305690e-01
#> 46: 2.449260e-01 5.305145e-01 6.629040e-01 8.142274e-01 9.351464e-01
#> 47: 2.361393e-01 5.030309e-01 6.413610e-01 8.060481e-01 9.367482e-01
#> 48: 2.336488e-01 4.976808e-01 6.390799e-01 7.808313e-01 9.066795e-01
#> 49: 2.196549e-01 5.038446e-01 6.393382e-01 8.137787e-01 9.382221e-01
#> 50: 2.118239e-01 5.026028e-01 6.384293e-01 7.907604e-01 9.259808e-01
#> 51: 2.237361e-01 5.060775e-01 6.488362e-01 7.986359e-01 9.225689e-01
#> 52: 2.026497e-01 4.893287e-01 6.352176e-01 7.789750e-01 9.102513e-01
#> 53: 2.097345e-01 4.818580e-01 6.312687e-01 7.872157e-01 9.037933e-01
#> 54: 2.366761e-01 5.257139e-01 6.502566e-01 8.144813e-01 9.514994e-01
#> 55: 1.884562e-01 4.829311e-01 6.220819e-01 7.927253e-01 9.313974e-01
#> 56: 2.536168e-01 5.111523e-01 6.267246e-01 7.873295e-01 9.172038e-01
#> lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
#> 1: 1.257240e+05
#> 2: 1.313576e+05
#> 3: 1.240567e+05
#> 4: 1.268394e+05
#> 5: 1.224297e+05
#> 6: 1.237614e+05
#> 7: 1.209838e+05
#> 8: 1.264614e+05
#> 9: 1.246358e+05
#> 10: 1.337441e+05
#> 11: 1.268132e+05
#> 12: 1.360518e+05
#> 13: 1.271142e+05
#> 14: 1.239280e+05
#> 15: 9.883630e+04
#> 16: 1.039497e+05
#> 17: 9.531901e+04
#> 18: 9.977934e+04
#> 19: 9.865056e+04
#> 20: 9.961695e+04
#> 21: 9.989504e+04
#> 22: 1.018635e+05
#> 23: 1.010113e+05
#> 24: 1.059634e+05
#> 25: 1.017310e+05
#> 26: 1.058011e+05
#> 27: 1.017387e+05
#> 28: 9.949105e+04
#> 29: 1.183995e+05
#> 30: 1.299536e+05
#> 31: 1.156523e+05
#> 32: 1.264861e+05
#> 33: 1.173846e+05
#> 34: 1.229697e+05
#> 35: 1.236902e+05
#> 36: 1.201061e+05
#> 37: 1.252907e+05
#> 38: 1.240740e+05
#> 39: 1.247325e+05
#> 40: 1.228692e+05
#> 41: 1.265890e+05
#> 42: 1.185848e+05
#> 43: 1.179072e+00
#> 44: 1.207291e+00
#> 45: 1.208875e+00
#> 46: 1.259279e+00
#> 47: 1.235606e+00
#> 48: 1.254393e+00
#> 49: 1.265936e+00
#> 50: 1.205821e+00
#> 51: 1.241248e+00
#> 52: 1.232185e+00
#> 53: 1.261422e+00
#> 54: 1.292689e+00
#> 55: 1.242198e+00
#> 56: 1.176025e+00
#> upper_90
# }