Power-law models for infectious disease spread

TL;DR

This paper introduces power-law based models for infectious disease spread that incorporate spatial interaction decay, improving fit and prediction accuracy in disease surveillance data analysis.

Contribution

It extends existing models by embedding power-law decay into both individual-level and aggregated count data frameworks, including a novel formulation for count data based on neighborhood order.

Findings

Power-law models significantly improve fit and prediction accuracy.

The approach is validated on meningococcal and influenza data.

Implementation is available in the R package surveillance.

Abstract

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.