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Developments in statistical inference when assessing spatiotemporal disease clustering with the tau statistic

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Pollington, Timothy, Tildesley, Michael J., Hollingsworth, T. Déirdre and Chapman, Lloyd A. C. (2020) Developments in statistical inference when assessing spatiotemporal disease clustering with the tau statistic. Spatial Statistics . 100438. doi:10.1016/j.spasta.2020.100438

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Official URL: http://dx.doi.org/10.1016/j.spasta.2020.100438

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Abstract

The tau statistic uses geolocation and, usually, symptom onset time to assess global spatiotemporal clustering from epidemiological data. We test different methods that could bias the clustering range estimate based on the statistic or affect its apparent precision, by comparison with a baseline analysis of an open access measles dataset.

From re-analysing this data we find evidence against no clustering and no inhibition, (global envelope test). We develop a tau-specific modification of the Loh & Stein spatial bootstrap sampling method, which gives bootstrap tau estimates with 24% lower sampling error and a 110% higher estimated clustering endpoint than previously published (61⋅0 m vs. 29 m) and an equivalent increase in the clustering area of elevated disease odds by 342%. These differences could have important consequences for control efforts.

Correct practice of graphical hypothesis testing of no clustering and clustering range estimation of the tau statistic are illustrated in the online Graphical abstract. We advocate proper implementation of this useful statistic, ultimately to reduce inaccuracies in control policy decisions made during disease clustering analysis.

Item Type: Journal Article
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Divisions: Faculty of Science > Life Sciences (2010- )
Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH): Spatial analysis (Statistics) , Spatial systems, Geospatial data -- Computer processing , Multivariate analysis -- Graphic methods
Journal or Publication Title: Spatial Statistics
Publisher: Elsevier B.V.
ISSN: 2211-6753
Official Date: 2020
Dates:
DateEvent
2020Published
23 March 2020Available
7 March 2020Accepted
Article Number: 100438
DOI: 10.1016/j.spasta.2020.100438
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
Visiting Professorship VP1-2014-04Leverhulme Trusthttp://dx.doi.org/10.13039/501100000275
EP/M011801/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266

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