# Forecast infections from a given fit and trajectory of the time-varying reproduction number 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()`. ```r forecast_infections( estimates, R = NULL, model = NULL, samples = NULL, batch_size = 10, backend = "rstan", verbose = interactive() ) ``` ## Arguments - `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. Alternatively accepts a `` containing at least `date` and `value` (integer) variables and optionally `sample`. More (or fewer) days than in the original fit can be simulated. - `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. - `backend`: Character string indicating the backend to use for fitting stan models. Supported arguments are "rstan" (default) or "cmdstanr". - `verbose`: Logical defaults to `interactive()`. If the `progressr` package is available, a progress bar will be shown. ## Returns A list of output as returned by `estimate_infections()` but based on results from the specified scenario rather than fitting. ## Examples ```r # set number of cores to use old_opts <- options() options(mc.cores = ifelse(interactive(), 4, 1)) # get example case counts reported_cases <- example_confirmed[1:50] # fit model to data to recover Rt estimates est <- estimate_infections(reported_cases, generation_time = generation_time_opts(example_generation_time), delays = delay_opts(example_incubation_period + example_reporting_delay), rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7), obs = obs_opts(scale = list(mean = 0.1, sd = 0.01)), gp = NULL, horizon = 0 ) # update Rt trajectory and simulate new infections using it R <- c(rep(NA_real_, 26), rep(0.5, 10), rep(0.8, 14)) sims <- forecast_infections(est, R) plot(sims) # with a data.frame input of samples R_dt <- data.frame( date = seq( min(summary(est, type = "parameters", param = "R")$date), by = "day", length.out = length(R) ), value = R ) sims <- forecast_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 <- forecast_infections(est, R_samples) plot(sims) options(old_opts) ``` ## See Also `dist_spec()` `generation_time_opts()` `delay_opts()` `rt_opts()` `estimate_infections()` `trunc_opts()` `stan_opts()` `obs_opts()` `gp_opts()`

forecast_infections function