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An unconditional space–time scan statistic for ZIP‐distributed data
Stockholm University, Faculty of Science, Department of Mathematics.ORCID iD: 0000-0002-0927-7183
Stockholm University, Faculty of Science, Department of Mathematics.ORCID iD: 0000-0002-0423-6702
2019 (English)In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 46, no 1, p. 142-159Article in journal (Refereed) Published
Abstract [en]

A scan statistic is proposed for the prospective monitoring of spatiotemporal count data with an excess of zeros. The method that is based on an outbreak model for the zero‐inflated Poisson distribution is shown to be superior to traditional scan statistics based on the Poisson distribution in the presence of structural zeros. The spatial accuracy and the detection timeliness of the proposed scan statistic are investigated by means of simulation, and an application on the weekly cases of Campylobacteriosis in Germany illustrates how the scan statistic could be used to detect emerging disease outbreaks. An implementation of the method is provided in the open‐source R package scanstatistics available on the Comprehensive R Archive Network.

Place, publisher, year, edition, pages
2019. Vol. 46, no 1, p. 142-159
Keywords [en]
disease surveillance, EM algorithm, scan statistic, spatiotemporal, zero‐inflated poisson
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-164870DOI: 10.1111/sjos.12341ISI: 000458557100007OAI: oai:DiVA.org:su-164870DiVA, id: diva2:1280579
Projects
Statistical Modelling, Monitoring and Predictive Analytics against Infectious Disease Outbreaks
Funder
Swedish Research Council, 2015-05182_VRAvailable from: 2019-01-19 Created: 2019-01-19 Last updated: 2019-12-04Bibliographically approved

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Allévius, BenjaminHöhle, Michael
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