simulate_infections {EpiNow2}

Simulate infections using the renewal equation

Description

Simulations are done from given initial infections and, potentially time-varying, reproduction numbers. Delays and parameters of the observation model can be specified using the same options as in estimate_infections().

Usage

simulate_infections(
  estimates,
  R,
  initial_infections,
  day_of_week_effect = NULL,
  generation_time = generation_time_opts(),
  delays = delay_opts(),
  truncation = trunc_opts(),
  obs = obs_opts(),
  CrIs = c(0.2, 0.5, 0.9),
  backend = "rstan",
  seeding_time = NULL,
  pop = 0,
  ...
)

Arguments

estimates

deprecated; use forecast_infections() instead

R

a data frame of reproduction numbers (column R) by date (column date). Column R must be numeric and date must be in date format. If not all days between the first and last day in the date are present, it will be assumed that R stays the same until the next given date.

initial_infections

numeric; the initial number of infections (i.e. before R applies). Note that results returned start the day after, i.e. the initial number of infections is not reported again. See also seeding_time

day_of_week_effect

either NULL (no day of the week effect) or a numerical vector of length specified in obs_opts() as week_length (default: 7) if week_effect is set to TRUE. Each element of the vector gives the weight given to reporting on this day (normalised to 1). The default is NULL.

generation_time

A call to generation_time_opts() defining the generation time distribution used. For backwards compatibility a list of summary parameters can also be passed.

delays

A call to delay_opts() defining delay distributions and options. See the documentation of delay_opts() and the examples below for details.

truncation

A call to trunc_opts() defining the truncation of the observed data. Defaults to trunc_opts(), i.e. no truncation. See the estimate_truncation() help file for an approach to estimating this from data where the dist list element returned by estimate_truncation() is used as the truncation argument here, thereby propagating the uncertainty in the estimate.

obs

A list of options as generated by obs_opts() defining the observation model. Defaults to obs_opts().

CrIs

Numeric vector of credible intervals to calculate.

backend

Character string indicating the backend to use for fitting stan models. Supported arguments are "rstan" (default) or "cmdstanr".

seeding_time

Integer; the number of days before the first time point of R; default is NULL, in which case it is set to the maximum of the generation time. The minimum is 1 , i.e. the first reproduction number given applies on the day after the index cases given by initial_infections. If the generation time is longer than 1 day on average, a seeding time of 1 will always lead to an initial decline (as there are no infections before the initial ones). Instead, if this is greater than 1, an initial part of the epidemic (before the first value of R given) of seeding_time days is assumed to have followed exponential growth roughly in line with the growth rate implied by the first value of R.

pop

Integer, defaults to 0. Susceptible population initially present. Used to adjust Rt estimates when otherwise fixed based on the proportion of the population that is susceptible. When set to 0 no population adjustment is done.

...

deprecated; only included for backward compatibility

Details

In order to simulate, all parameters that are specified such as the mean and standard deviation of delays or observation scaling, must be fixed. Uncertain parameters are not allowed.

A previous function called simulate_infections() that simulates from a given model fit has been renamed forecast_infections(). Using simulate_infections() with existing estimates is now deprecated. This option will be removed in the next version.

Value

A data.table of simulated infections (variable infections) and reported cases (variable reported_cases) by date.

Examples

  R <- data.frame(
    date = seq.Date(as.Date("2023-01-01"), length.out = 14, by = "day"),
    R = c(rep(1.2, 7), rep(0.8, 7))
  )
  sim <- simulate_infections(
    R = R,
    initial_infections = 100,
    generation_time = generation_time_opts(
      fix_dist(example_generation_time)
    ),
    delays = delay_opts(fix_dist(example_reporting_delay)),
    obs = obs_opts(family = "poisson")
  )