Cox processes in time for point patterns and their aggregations
Marina S. Paez (UFRJ)

We propose a Cox process as a model for the temporal pattern of the incidence of cases of events of a certain type and develop associated methods of Bayesian inference, which we implement using an MCMC algorithm. For problems of this kind, the data may consist of the event-times themselves, or counts of the numbers of events in disjoint time-intervals. We explore the consequences of working with different levels of temporal aggregation of the data. We use a simulated example to demonstrate the feasibility of our methodology, which we then apply to data giving daily counts of incident cases of gastrointestinal infections in the county of Hampshire, UK.