ArticleInternational journal of health geographics2021
Optimizing the maximum reported cluster size in the spatial scan statistic for survival data.
Article in International journal of health geographics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 17 citations in OpenAlex.
- Spatio-temporal clustering characteristics of hepatitis B in China: an 18-year study.Scientific reports · 2026Article
- Epidemiology, serovar distribution and spatiotemporal patterns of Salmonella foodborne infections in Liaoning Province, China, 2014-2024: an analysis of sentinel surveillance data.Frontiers in public health · 2026Article
- Spatiotemporal clusters and dengue hotspots in the Philippines: a nationwide analysis spanning 2017-2024.Frontiers in public health · 2026Article
- Geographical Distribution, Spatial Directional Trends, and Spatio-Temporal Clusters of the First Rapid and Widespread Lumpy Skin Disease Outbreaks in Thailand.Transboundary and emerging diseases · 2025Article
- Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic.International journal of health geographics · 2023Article
- Article
- Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China.International journal of environmental research and public health · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results.
resultsWe developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data.
conclusionsUsing the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.
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