ArticleInternational journal of health geographics2011
Detection of arbitrarily-shaped clusters using a neighbor-expanding approach: a case study on murine typhus in south Texas.
Article in International journal of health geographics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A One Health Perspective on the Resurgence of Flea-Borne Typhus in Texas in the 21st Century: Part 1: The Bacteria, the Cat Flea, Urbanization, and Climate Change.Pathogens (Basel, Switzerland) · 2025Article
- A multidisciplinary and structured investigation of three suspected clusters of transverse upper limb reduction defects in France.European journal of epidemiology · 2024Article
- Nonnegligible Seroprevalence and Predictors of Murine Typhus, Japan.Emerging infectious diseases · 2023Article
- Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease.PLoS computational biology · 2022Article
- Space-Time Cluster's Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties.International journal of environmental research and public health · 2021Article
- Significance of major international seaports in the distribution of murine typhus in Taiwan.PLoS neglected tropical diseases · 2017Article
- Approaches to the detection of very small, common, and easily missed outbreaks that together contribute substantially to human Cryptosporidium infection.Epidemiology and infection · 2014Article
- Early malaria resurgence in pre-elimination areas in Kokap Subdistrict, Kulon Progo, Indonesia.Malaria journal · 2014Article
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3 authors.
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Abstract
backgroundKulldorff's spatial scan statistic has been one of the most widely used statistical methods for automatic detection of clusters in spatial data. One limitation of this method lies in the fact that it has to rely on scan windows with predefined shapes in the search process, and therefore it cannot detect cluster with arbitrary shapes. We employ a new neighbor-expanding approach and introduce two new algorithms to detect cluster with arbitrary shapes in spatial data. These two algorithms are called the maximum-likelihood-first (MLF) algorithm and non-greedy growth (NGG) algorithm. We then compare the performance of these two new algorithms with the spatial scan statistic (SaTScan), Tango's flexibly shaped spatial scan statistic (FlexScan), and Duczmal's simulated annealing (SA) method using two datasets. Furthermore, we utilize the methods to examine clusters of murine typhus cases in South Texas from 1996 to 2006.
resultWhen compared with the SaTScan and FlexScan method, the two new algorithms were more flexible and sensitive in detecting the clusters with arbitrary shapes in the test datasets. Clusters detected by the MLF algorithm are statistically more significant than those detected by the NGG algorithm. However, the NGG algorithm appears to be more stable when there are no extreme cluster patterns in the data. For the murine typhus data in South Texas, a large portion of the detected clusters were located in coastal counties where environmental conditions and socioeconomic status of some population groups were at a disadvantage when compared with those in other counties with no clusters of murine typhus cases.
conclusionThe two new algorithms are effective in detecting the location and boundary of spatial clusters with arbitrary shapes. Additional research is needed to better understand the etiology of the concentration of murine typhus cases in some counties in south Texas.
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