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Taylor's power law and the statistical modelling of infectious disease surveillance data

Enki, Doyo Gragn; Noufaily, Angela; Farrington, Paddy; Garthwaite, Paul; Andrews, Nick and Charlett, Andre (2016). Taylor's power law and the statistical modelling of infectious disease surveillance data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180(1) pp. 45–72.

DOI (Digital Object Identifier) Link: https://doi.org/10.1111/rssa.12181
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Abstract

Surveillance data collected on several hundred different infectious organisms over 20 years have revealed striking power relationships between their variance and mean in successive time periods. Such patterns are common in ecology, where they are referred to collectively as Taylor's power law. In the paper, these relationships are investigated in detail, with the aim of exploiting them for the descriptive statistical modelling of infectious disease surveillance data. We confirm the existence of variance-to-mean power relationships, with exponent typically between 1 and 2. We investigate skewness-to-mean relationships, which are found broadly to match those expected of Tweedie distributions, and thus confirm the relevance of the Tweedie convergence theorem in this context. We suggest that variance- and skewness-to-mean power laws, when present, should inform statistical modelling of infectious disease surveillance data, notably in descriptive analysis, model building, simulation and interval and threshold estimation, threshold estimation being particularly relevant to outbreak detection.

Item Type: Journal Item
Copyright Holders: 2016 Royal Statistical Society
ISSN: 0964-1998
Keywords: Exponential dispersion model; Infectious disease; Power law; Surveillance; Taylor's law; Tweedie family
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 51861
SWORD Depositor: Jisc Publications-Router
Depositing User: Jisc Publications-Router
Date Deposited: 01 Mar 2018 14:16
Last Modified: 07 Dec 2018 10:58
URI: http://oro.open.ac.uk/id/eprint/51861
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