# Simulate infections using the renewal equation

Source:`R/simulate_infections.R`

`simulate_infections.Rd`

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

`gt_opts()`

(or its alias`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

## Value

A data.table of simulated infections (variable `infections`

) and
reported cases (variable `reported_cases`

) by date.

## 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.

## Examples

```
# \donttest{
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_parameters(example_generation_time)
),
delays = delay_opts(fix_parameters(example_reporting_delay)),
obs = obs_opts(family = "poisson")
)
# }
```