  Spatio-temporal interaction is inherent to cases of infectious diseases and
occurrences of earthquakes, whereas the spread of other events, such as cancer
or crime, is less evident. Statistical significance tests of space-time
clustering usually assess the correlation between the spatial and temporal
(transformed) distances of the events. Although appealing through simplicity,
these classical tests do not adjust for the underlying population nor can they
account for a distance decay of interaction. We propose to use the framework of
an endemic-epidemic point process model to jointly estimate a background event
rate explained by seasonal and areal characteristics, as well as a superposed
epidemic component representing the hypothesis of interest. We illustrate this
new model-based test for space-time interaction by analysing psychiatric
inpatient admissions in Zurich, Switzerland (2007-2012). Several socio-economic
factors were found to be associated with the admission rate, but there was no
evidence of general clustering of the cases.
