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)
)

## 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.

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.