ArticleBiometrics2011
A space-time scan statistic for detecting emerging outbreaks.
Article in Biometrics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 46 citations in OpenAlex.
- Assessing spatial variability in observed infectious disease spread in a prospective time-space series.International journal of health geographics · 2025Article
- Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition.Journal of applied statistics · 2023Article
- Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease.PLoS computational biology · 2022Article
- Rapid detection of hot-spots via tensor decomposition with applications to crime rate data.Journal of applied statistics · 2022Article
- Spatiotemporal epidemiology of cryptosporidiosis in the Republic of Ireland, 2008-2017: development of a space-time "cluster recurrence" index.BMC infectious diseases · 2021Article
- Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic.PloS one · 2019Article
- Approaches to the detection of very small, common, and easily missed outbreaks that together contribute substantially to human Cryptosporidium infection.Epidemiology and infection · 2014Article
- Article
- International society for disease surveillance conference 2011: building the future of public health surveillance.Emerging health threats journal · 2011Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a major analytical method for outbreak detection, Kulldorff's space-time scan statistic (2001, Journal of the Royal Statistical Society, Series A 164, 61-72) has been implemented in many syndromic surveillance systems. Since, however, it is based on circular windows in space, it has difficulty correctly detecting actual noncircular clusters. Takahashi et al. (2008, International Journal of Health Geographics 7, 14) proposed a flexible space-time scan statistic with the capability of detecting noncircular areas. It seems to us, however, that the detection of the most likely cluster defined in these space-time scan statistics is not the same as the detection of localized emerging disease outbreaks because the former compares the observed number of cases with the conditional expected number of cases. In this article, we propose a new space-time scan statistic which compares the observed number of cases with the unconditional expected number of cases, takes a time-to-time variation of Poisson mean into account, and implements an outbreak model to capture localized emerging disease outbreaks more timely and correctly. The proposed models are illustrated with data from weekly surveillance of the number of absentees in primary schools in Kitakyushu-shi, Japan, 2006.
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