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 (columndate
). ColumnR
must be numeric anddate
must be in date format. If not all days between the first and last day in thedate
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 alsoseeding_time
- day_of_week_effect
either
NULL
(no day of the week effect) or a numerical vector of length specified inobs_opts()
asweek_length
(default: 7) ifweek_effect
is set to TRUE. Each element of the vector gives the weight given to reporting on this day (normalised to 1). The default isNULL
.- generation_time
A call to
gt_opts()
(or its aliasgeneration_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 ofdelay_opts()
and the examples below for details.- truncation
A call to
trunc_opts()
defining the truncation of the observed data. Defaults totrunc_opts()
, i.e. no truncation. See theestimate_truncation()
help file for an approach to estimating this from data where thedist
list element returned byestimate_truncation()
is used as thetruncation
argument here, thereby propagating the uncertainty in the estimate.- obs
A list of options as generated by
obs_opts()
defining the observation model. Defaults toobs_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 isNULL
, 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 byinitial_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) ofseeding_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")
)
# }