Spatiotemporal models for biosurveillance

It is useful to know the geographical density of a transmittable disease in order to plan interventions and to predict its future developments. This can be difficult where there is a lack of co-ordinated statistics, which is often the case where diseases like malaria or tuberculosis are endemic. In such situations it is possible to combine irregular updates from a variety of less consistent sources. We are looking at the use of spatiotemporal state space models for biosurveillance, for use when there are irregular updates about disease counts.