predict.rtsFit

Predictions cannot be generated directly from an <code>rtsFit</code> object, rather new predictions should be
generated using the original <code>grid</code> object. A message is printed to the user.

Supports modelling real-time case data to facilitate the real-time
surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over
an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts,
and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by
Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation for Gaussian processes
described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2023) <doi:10.1007/s11222-022-10167-2> and the
nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>.

Sam Watson

rts2

Real-Time Disease Surveillance

predict.rtsFit function

<dl><dt>object</dt>
<dd>A <code>rtsFit</code> object.</dd>
<dt>...</dt>
<dd>Further arguments passed from other methods</dd></dl>

Arguments

Predict from a rtsFit object — predict.rtsFit

<dl>

<dt>object</dt>
<dd>A <code>rtsFit</code> object.</dd>


<dt>...</dt>
<dd>Further arguments passed from other methods</dd>

</dl>

predict.rtsFit: Predict from a `rtsFit` object

Description

Usage

Value

Arguments