  Short-time human travel behaviour can be described by a power law with
respect to distance. We incorporate this information in space-time models for
infectious disease surveillance data to better capture the dynamics of disease
spread. Two previously established model classes are extended, which both
decompose disease risk additively into endemic and epidemic components: a
spatio-temporal point process model for individual-level data and a
multivariate time-series model for aggregated count data. In both frameworks, a
power-law decay of spatial interaction is embedded into the epidemic component
and estimated jointly with all other unknown parameters using (penalised)
likelihood inference. Whereas the power law can be based on Euclidean distance
in the point process model, a novel formulation is proposed for count data
where the power law depends on the order of the neighbourhood of discrete
spatial units. The performance of the new approach is investigated by a
reanalysis of individual cases of invasive meningococcal disease in Germany
(2002-2008) and count data on influenza in 140 administrative districts of
Southern Germany (2001-2008). In both applications, the power law substantially
improves model fit and predictions, and is reasonably close to alternative
qualitative formulations, where distance and order of neighbourhood,
respectively, are treated as a factor. Implementation in the R package
surveillance allows the approach to be applied in other settings.
