ArticleStatistics in biosciences2023
Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic.
Article in Statistics in biosciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Modeling mumps incidence in China: spatiotemporal clusters and evolving risk factors (2005-2020).BMC public health · 2025Article
- Spatiotemporal trends and driving factors of gonorrhea incidence in China: a 20-year analysis (2003-2022).Frontiers in public health · 2025Article
- Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation-based methods.Health science reports · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
The spatial scan statistics based on the Poisson and binomial models are the most common methods to detect spatial clusters in disease surveillance. These models rely on Monte-Carlo simulation which are time consuming. Moreover, frequently, datasets present over-dispersion which cannot be handled by them. Thus, we have the following goals. First, we propose irregularly shaped spatial scan for the Bell, Poisson, and binomial. The Bell distribution has just one parameter but it is capable of handling over-dispersed datasets. Second, we apply these scan statistics to big maps. A fast version, without Monte-Carlo simulation, for the proposed Poisson and binomial scans is introduced. Intensive simulation studies are carried out to assess the quality of the proposals. In addition, we show the time improvement of the fast scan versions over their traditional ones. Finally, we end the paper with an application on the detection of irregular shape small nodules in a medical image. Supplementary Information: The online version contains supplementary material available at 10.1007/s12561-022-09353-7.
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