scholarly journals Irregularly Shaped Cluster Detection Using a CPSO Distribution-Free Spatial Scan Statistic

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 24863-24872 ◽  
Author(s):  
Zhongliang Fu ◽  
Kaichun Zhou ◽  
Yangjie Sun ◽  
Yutao Han
Keyword(s):  
Cluster Detection ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Distribution Free ◽  
Spatial Scan

Evaluation of hotspot cluster detection using spatial scan statistic based on exact counting

2019 ◽  
Vol 2 (1) ◽  
pp. 241-262 ◽  
Author(s):  
Fumio Ishioka ◽  
Jun Kawahara ◽  
Masahiro Mizuta ◽  
Shin-ichi Minato ◽  
Koji Kurihara
Keyword(s):  
Cluster Detection ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Exact Counting ◽  
Spatial Scan

scholarly journals A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1819
Author(s):  
Wongsa Laohasiriwong ◽  
Nattapong Puttanapong ◽  
Amornrat Luenam
Keyword(s):  
Spatial Heterogeneity ◽  
Respiratory Diseases ◽  
Pearson Correlation ◽  
Cluster Detection ◽  
Detection Methods ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Local Cluster ◽  
Chronic Respiratory Diseases ◽  
Spatial Scan

Background: The Centers for Disease Control and Prevention reported that deaths from chronic respiratory diseases (CRDs) in Thailand increased by almost 13% in 2010, along with an increased burden related to the disease. Evaluating the geographical heterogeneity of CRDs is important for surveillance. Previous studies have indicated that socioeconomic status has an effect on disease, and that this can be measured with variables such as night-time lights (NTLs) and industrial density (ID). However, there is no understanding of how NTLs and ID correlate with CRDs. We compared spatial heterogeneity obtained by using local cluster detection methods for CRDs and by correlating NTLs and ID with CRDs. Methods: We applied the spatial scan statistic in SaTScan, as well as local indices of spatial association (LISA), Getis and Ord’s local Gi*(d) statistic, and Pearson correlation. In our analysis, data were collected on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previously diagnosed CRDs by a physician from the National Socioeconomic Survey, which is a cross-sectional study conducted by the National Statistical Office of Thailand in 2010. Results: According to our findings, the spatial scan statistic, LISA, and the local Gi*(d) statistic revealed similar results for areas with the highest clustering of CRDs. However, the hotspots for the spatial scan statistic covered a wider area than LISA and the local Gi*(d) statistic. In addition, there were persistent hotspots in Bangkok and the perimeter provinces. NTLs and ID have a positive correlation with CRDs. Conclusions: This study demonstrates that all the statistical methods used could detect spatial heterogeneity of CRDs. NTLs and ID can serve as new parameters for determining disease hotspots by representing the population and industrial boom that typically contributes to epidemics.

scholarly journals Optimizing the maximum reported cluster size in the spatial scan statistic for survival data

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Sujee Lee ◽  
Jisu Moon ◽  
Inkyung Jung
Keyword(s):  
Survival Data ◽  
Cluster Size ◽  
Total Population ◽  
Window Size ◽  
Cluster Detection ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Detection Analysis ◽  
Scanning Window ◽  
Spatial Scan

Abstract Background The 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. Results We 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. Conclusions Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.

scholarly journals A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1819 ◽  
Author(s):  
Wongsa Laohasiriwong ◽  
Nattapong Puttanapong ◽  
Amornrat Luenam
Keyword(s):  
Spatial Heterogeneity ◽  
Respiratory Diseases ◽  
Pearson Correlation ◽  
Cluster Detection ◽  
Detection Methods ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Local Cluster ◽  
Chronic Respiratory Diseases ◽  
Spatial Scan

Background: The Centers for Disease Control and Prevention reported that deaths from chronic respiratory diseases (CRDs) in Thailand increased by almost 13% in 2010, along with an increased burden related to the disease. Evaluating the geographical heterogeneity of CRDs is important for surveillance. Previous studies have indicated that socioeconomic status has an effect on disease, and that this can be measured with variables such as night-time lights (NTLs) and industrial density (ID). However, there is no understanding of how NTLs and ID correlate with CRDs. We compared spatial heterogeneity obtained by using local cluster detection methods for CRDs and by correlating NTLs and ID with CRDs. Methods: We applied the spatial scan statistic in SaTScan, as well as local indices of spatial association (LISA), Getis and Ord’s local Gi*(d) statistic, and Pearson correlation. In our analysis, data were collected on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previously diagnosed CRDs by a physician from the National Socioeconomic Survey, which is a cross-sectional study conducted by the National Statistical Office of Thailand in 2010. Results: According to our findings, the spatial scan statistic, LISA, and the local Gi*(d) statistic revealed similar results for areas with the highest clustering of CRDs. However, the hotspots for the spatial scan statistic covered a wider area than LISA and the local Gi*(d) statistic. In addition, there were persistent hotspots in Bangkok and the perimeter provinces. NTLs and ID have a positive correlation with CRDs. Conclusions: This study demonstrates that all the statistical methods used could detect spatial heterogeneity of CRDs. NTLs and ID can serve as new parameters for determining disease hotspots by representing the population and industrial boom that typically contributes to epidemics.

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