`R/simulate_infections.R`

`simulate_infections.Rd`

This function simulates infections using an existing fit to observed cases but with a modified
time-varying reproduction number. This can be used to explore forecast models or past counterfactuals.
Simulations can be run in parallel using `future::plan`

.

```
simulate_infections(
estimates,
R = NULL,
model = NULL,
samples = NULL,
batch_size = 10,
verbose = interactive()
)
```

- estimates
The

`estimates`

element of an`epinow`

run that has been done with output = "fit", or the result of`estimate_infections`

with`return_fit`

set to TRUE.- R
A numeric vector of reproduction numbers; these will overwrite the reproduction numbers contained in

`estimates`

, except elements set to NA. If it is longer than the time series of reproduction numbers contained in`estimates`

, the values going beyond the length of estimated reproduction numbers are taken as forecast. Alternatively accepts a data.frame containing at least`date`

and`value`

(integer) variables and optionally`sample`

.- model
A compiled stan model as returned by

`rstan::stan_model`

.- samples
Numeric, number of posterior samples to simulate from. The default is to use all samples in the

`estimates`

input.- batch_size
Numeric, defaults to 10. Size of batches in which to simulate. May decrease run times due to reduced IO costs but this is still being evaluated. If set to NULL then all simulations are done at once.

- verbose
Logical defaults to

`interactive()`

. Should a progress bar (from`progressr`

) be shown.

```
# \donttest{
# set number of cores to use
options(mc.cores = ifelse(interactive(), 4, 1))
# get example case counts
reported_cases <- example_confirmed[1:50]
# set up example generation time
generation_time <- get_generation_time(disease = "SARS-CoV-2", source = "ganyani")
# set delays between infection and case report
incubation_period <- get_incubation_period(disease = "SARS-CoV-2", source = "lauer")
reporting_delay <- list(
mean = convert_to_logmean(2, 1), mean_sd = 0.1,
sd = convert_to_logsd(2, 1), sd_sd = 0.1, max = 15
)
# fit model to data to recover Rt estimates
est <- 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)),
gp = gp_opts(
ls_min = 10, boundary_scale = 1.5, ,
basis_prop = 0.1
),
obs = obs_opts(scale = list(mean = 0.1, sd = 0.01))
)
# update Rt trajectory and simulate new infections using it
R <- c(rep(NA_real_, 40), rep(0.5, 10), rep(0.8, 7))
sims <- simulate_infections(est, R)
plot(sims)
# with a data.frame input of samples
R_dt <- data.frame(
date = summary(est, type = "parameters", param = "R")$date,
value = R
)
sims <- simulate_infections(est, R_dt)
plot(sims)
#' # with a data.frame input of samples
R_samples <- summary(est, type = "samples", param = "R")
R_samples <- R_samples[, .(date, sample, value)][sample <= 1000][date <= "2020-04-10"]
R_samples <- R_samples[date >= "2020-04-01", value := 1.1]
sims <- simulate_infections(est, R_samples)
plot(sims)
# }
```