Estimate the time varying R0 - using EpiEstim

estimate_R0(
  cases = NULL,
  generation_times = NULL,
  rt_prior = NULL,
  windows = NULL,
  gt_samples = 100,
  rt_samples = 100,
  min_est_date = NULL,
  forecast_model = NULL,
  horizon = 0
)

Arguments

cases	A dataframe containing a list of local cases with the following variables: `date`, `cases`, and `import_status`
generation_times	A matrix with columns representing samples and rows representing the probability of the generation timebeing on that day.
rt_prior	A list defining the reproduction number prior containing the mean (`mean_prior`) and standard deviation (`std_prior`)
windows	Numeric vector, windows over which to estimate time-varying R. The best performing window will be selected per serial interval sample by default (based on which window best forecasts current cases).
gt_samples	Numeric, the number of samples to take from the generaiton times supplied
rt_samples	Numeric, the number of samples to take from the estimated R distribution for each time point.
min_est_date	Date to begin estimation.
forecast_model	An uninitialised bsts model passed to `EpiSoon::forecast_rt` to be used for forecasting future Rt values. An example of the required structure is: `function(ss, y){bsts::AddSemilocalLinearTrend(ss, y = y)}`.
horizon	Numeric, defaults to 0. The horizon over which to forecast Rts and cases.

Value

A list of data.table's containing the date and summarised R estimate and optionally a case forecast

Examples


## Nowcast Rts                  
estimates <- estimate_R0(cases = EpiSoon::example_obs_cases, 
                         generation_times = as.matrix(EpiNow::covid_generation_times[,2]), 
                         rt_prior = list(mean_prior = 2.6, std_prior = 2),
                         windows = c(1, 3, 7), rt_samples = 10, gt_samples = 1)
                         
                         
estimates$rts
#>            date         R sample   crps window rt_type
#>   1: 2020-01-24 0.3179646      1   0.09      3 nowcast
#>   2: 2020-01-24 1.0212079      2   0.09      3 nowcast
#>   3: 2020-01-24 1.4533755      3   0.09      3 nowcast
#>   4: 2020-01-24 1.6329530      4   0.09      3 nowcast
#>   5: 2020-01-24 1.6330397      5   0.09      3 nowcast
#>  ---                                                  
#> 586: 2020-03-22 1.4251245      6 166.20      1 nowcast
#> 587: 2020-03-22 1.4328383      7 166.20      1 nowcast
#> 588: 2020-03-22 1.4339116      8 166.20      1 nowcast
#> 589: 2020-03-22 1.4488785      9 166.20      1 nowcast
#> 590: 2020-03-22 1.4631037     10 166.20      1 nowcast
 
if (FALSE) { 
## Nowcast Rts, forecast Rts and the forecast cases
estimates <- estimate_R0(cases = EpiSoon::example_obs_cases, 
                         generation_times = as.matrix(EpiNow::covid_generation_times[,1]), 
                         rt_prior = list(mean_prior = 2.6, std_prior = 2),
                         windows = c(1, 3, 7), rt_samples = 10, gt_samples = 20,
                         min_est_date =  as.Date("2020-02-18"),
                         forecast_model = function(...){
                              EpiSoon::fable_model(model = fable::ETS(y ~ trend("A")), ...)
                              },
                         horizon = 14)
                                           
## Rt estimates and forecasts
estimates$rts



## Case forecasts
estimates$cases
}