EpiNow2 (version 1.1.0)

regional_epinow: Real-time Rt Estimation, Forecasting and Reporting by Region

Description

Estimates Rt by region. See the documentation for epinow for further information.

Usage

regional_epinow(
  reported_cases,
  target_folder,
  target_date,
  non_zero_points = 2,
  cores = 1,
  summary = TRUE,
  summary_dir,
  region_scale = "Region",
  all_regions_summary = TRUE,
  return_estimates = TRUE,
  max_plot = 10,
  ...
)

Arguments

reported_cases

A data frame of confirmed cases (confirm) by date (date), and region (region).

target_folder

Character string specifying where to save results (will create if not present).

target_date

Date, defaults to maximum found in the data if not specified.

non_zero_points

Numeric, the minimum number of time points with non-zero cases in a region required for that region to be evaluated. Defaults to 2.

cores

Numeric, defaults to 2. The number of cores to use when fitting the stan model.

summary

Logical, should summary measures be calculated.

summary_dir

A character string giving the directory in which to store summary of results.

region_scale

A character string indicating the name to give the regions being summarised.

all_regions_summary

Logical, defaults to TRUE. Should summary plots for all regions be returned rather than just regions of interest.

return_estimates

Logical, defaults to TRUE. Should estimates be returned.

max_plot

Numeric, defaults to 10. A multiplicative upper bound on the number of cases shown on the plot. Based on the maximum number of reported cases.

...

Pass additional arguments to epinow

Value

A list of output stratified at the top level into regional output and across region output summary output

Examples

# NOT RUN {
 
# }
# NOT RUN {
## Construct example distributions
generation_time <- list(mean = EpiNow2::covid_generation_times[1, ]$mean,
                        mean_sd = EpiNow2::covid_generation_times[1, ]$mean_sd,
                        sd = EpiNow2::covid_generation_times[1, ]$sd,
                        sd_sd = EpiNow2::covid_generation_times[1, ]$sd_sd,
                        max = 30)
                          
incubation_period <- list(mean = EpiNow2::covid_incubation_period[1, ]$mean,
                          mean_sd = EpiNow2::covid_incubation_period[1, ]$mean_sd,
                          sd = EpiNow2::covid_incubation_period[1, ]$sd,
                          sd_sd = EpiNow2::covid_incubation_period[1, ]$sd_sd,
                          max = 30)
                   
reporting_delay <- list(mean = log(10),
                        mean_sd = log(2),
                        sd = log(2),
                        sd_sd = log(1.1),
                        max = 30)
                        
## Uses example case vector
cases <- EpiNow2::example_confirmed[1:40]

cases <- data.table::rbindlist(list(
  data.table::copy(cases)[, region := "testland"],
  cases[, region := "realland"]))
  
## Run basic nowcasting pipeline
## Here we reduce the accuracy of the GP approximation in order to reduce runtime
out <- regional_epinow(reported_cases = cases,
                       generation_time = generation_time,
                       delays = list(incubation_period, reporting_delay),
                       adapt_delta = 0.9,
                       samples = 2000, warmup = 200, verbose = TRUE,
                       cores = ifelse(interactive(), 4, 1), chains = 4)
# }