# Forecast Secondary Observations Given a Fit from estimate_secondary

Source:`R/estimate_secondary.R`

`forecast_secondary.Rd`

This function forecasts secondary observations using the output of
`estimate_secondary()`

and either observed primary data or a forecast of
primary observations. See the examples of `estimate_secondary()`

for one use case. It can also be combined with `estimate_infections()`

to
produce a forecast for a secondary observation from a forecast of a primary
observation. See the examples of `estimate_secondary()`

for
example use cases on synthetic data. See
here
for an example of forecasting Covid-19 deaths from Covid-19 cases.

## Usage

```
forecast_secondary(
estimate,
primary,
primary_variable = "reported_cases",
model = NULL,
backend = "rstan",
samples = NULL,
all_dates = FALSE,
CrIs = c(0.2, 0.5, 0.9)
)
```

## Arguments

- estimate
An object of class "estimate_secondary" as produced by

`estimate_secondary()`

.- primary
A

`<data.frame>`

containing at least`date`

and`value`

(integer) variables and optionally`sample`

. Used as the primary observation used to forecast the secondary observations. Alternatively, this may be an object of class "estimate_infections" as produced by`estimate_infections()`

. If`primary`

is of class "estimate_infections" then the internal samples will be filtered to have a minimum date ahead of those observed in the`estimate`

object.- primary_variable
A character string indicating the primary variable, defaulting to "reported_cases". Only used when primary is of class

`<estimate_infections>`

.- model
A compiled stan model as returned by

`rstan::stan_model()`

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

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

`primary`

input when present. If not present the default is to use 1000 samples.- all_dates
Logical, defaults to FALSE. Should a forecast for all dates and not just those in the forecast horizon be returned.

- CrIs
Numeric vector of credible intervals to calculate.

## Value

A list containing: `predictions`

(a `<data.frame>`

ordered by date
with the primary, and secondary observations, and a summary of the forecast
secondary observations. For primary observations in the forecast horizon
when uncertainty is present the median is used), `samples`

a `<data.frame>`

of forecast secondary observation posterior samples, and `forecast`

a summary
of the forecast secondary observation posterior.