The Covid-19 pandemic has focussed awareness on the need for good modeling
of infectious disease spread and the need for surveillance which can alert
public health officials to developing adverse events such as clusters
of unusual risk (hot spots). Bayesian models can provide a
dynamically flexible framework for such modeling via recursive Bayesian learning.
In addition, monitoring of events can be facilitated by using posterior functionals
of risk. This talk will address some infectious disease modeling basics and
will review already developed surveillance functionals (SCPO, SKL).
Novel developments in surveillance metrics will be examined including directional
detection, exceedence probability, and exceedence level, related to extreme value theory(EVT).
The relation between model choice and metric evaluation is also explored.
A case study of Covid-19 incidence in South Carolina USA in 2020 will be presented.