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Power-law models for infectious disease spread


Meyer, Sebastian; Held, Leonhard (2014). Power-law models for infectious disease spread. Annals of Applied Statistics, 8(3):1612-1639.

Abstract

Short-time human travel behaviour can be well 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 space-time point process model for individual point-referenced data, and a multivariate time series model for aggregated count data. In both frameworks, the power-law spread is embedded into the epidemic component and its decay parameter is estimated simultaneously with all other unknown parameters using (penalised) likelihood inference. The performance of the new approach is investigated by a re-analysis 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 formulations substantially improve model fit and predictions. Implementation in the R package surveillance allows to apply the approach in other settings.

Short-time human travel behaviour can be well 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 space-time point process model for individual point-referenced data, and a multivariate time series model for aggregated count data. In both frameworks, the power-law spread is embedded into the epidemic component and its decay parameter is estimated simultaneously with all other unknown parameters using (penalised) likelihood inference. The performance of the new approach is investigated by a re-analysis 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 formulations substantially improve model fit and predictions. Implementation in the R package surveillance allows to apply the approach in other settings.

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3 citations in Web of Science®
4 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2014
Deposited On:22 Jan 2014 16:46
Last Modified:05 Apr 2016 17:26
Publisher:Institute of Mathematical Statistics
Number of Pages:35
ISSN:1932-6157
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1214/14-AOAS743
Official URL:http://projecteuclid.org/euclid.aoas/1414091227
Related URLs:http://arxiv.org/abs/1308.5115
Permanent URL: https://doi.org/10.5167/uzh-89321

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