`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.

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

- 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`

.- 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.

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.

estimate_secondary