A standardized scan statistic for detecting spatial clusters with estimated parameters

2012 ◽  
Vol 59 (6) ◽  
pp. 397-410 ◽  
Author(s):  
Lianjie Shu ◽  
Wei Jiang ◽  
Kwok-Leung Tsui
Keyword(s):  
Spatial Clusters ◽  
Scan Statistic ◽  
Estimated Parameters

scholarly journals Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination

2021 ◽  
Vol 18 (11) ◽  
pp. 5553
Author(s):  
Kinley Wangdi ◽  
Kinley Penjor ◽  
Tobgyal ◽  
Saranath Lawpoolsri ◽  
Ric N. Price ◽  
...  
Keyword(s):  
Control Program ◽  
Space Time ◽  
Data Repository ◽  
Spatial Clusters ◽  
Scan Statistic ◽  
Disease Control Program ◽  
Risk Areas ◽  
Vector Borne ◽  
Program Data ◽  
Time Scan

Malaria in Bhutan has fallen significantly over the last decade. As Bhutan attempts to eliminate malaria in 2022, this study aimed to characterize the space–time clustering of malaria from 2010 to 2019. Malaria data were obtained from the Bhutan Vector-Borne Disease Control Program data repository. Spatial and space–time cluster analyses of Plasmodium falciparum and Plasmodium vivax cases were conducted at the sub-district level from 2010 to 2019 using Kulldorff’s space–time scan statistic. A total of 768 confirmed malaria cases, including 454 (59%) P. vivax cases, were reported in Bhutan during the study period. Significant temporal clusters of cases caused by both species were identified between April and September. The most likely spatial clusters were detected in the central part of Bhutan throughout the study period. The most likely space–time cluster was in Sarpang District and neighboring districts between January 2010 to June 2012 for cases of infection with both species. The most likely cluster for P. falciparum infection had a radius of 50.4 km and included 26 sub-districts with a relative risk (RR) of 32.7. The most likely cluster for P. vivax infection had a radius of 33.6 km with 11 sub-districts and RR of 27.7. Three secondary space–time clusters were detected in other parts of Bhutan. Spatial and space–time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Operational research to understand the drivers of residual transmission in hotspot sub-districts will help to overcome the final challenges of malaria elimination in Bhutan.

scholarly journals A Scan Statistic for Binary Outcome Based on Hypergeometric Probability Model, with an Application to Detecting Spatial Clusters of Japanese Encephalitis

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e65419
Author(s):  
Xing Zhao ◽  
Xiao-Hua Zhou ◽  
Zijian Feng ◽  
Pengfei Guo ◽  
Hongyan He ◽  
...  
Keyword(s):  
Japanese Encephalitis ◽  
Probability Model ◽  
Binary Outcome ◽  
Spatial Clusters ◽  
Scan Statistic

scholarly journals Detecting Local Clusters of Under-5 Malnutrition in the Province of Marinduque, Philippines Using Spatial Scan Statistic

2020 ◽  
Vol 13 ◽  
pp. 117863882094067
Author(s):  
Novee Lor C Leyso ◽  
Maylin C Palatino
Keyword(s):  
Z Score ◽  
Spatial Clusters ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Double Burden ◽  
Malnourished Children ◽  
Double Burden Of Malnutrition ◽  
Spatial Scan ◽  
Using Data ◽  
Weight For Age

Underweight and overweight among under-5 children continue to persist in the island Province of Marinduque, Philippines. Local spatial cluster detection provides a spatial perspective in understanding this phenomenon, specifically in which areas the double burden of malnutrition occurs. Using data from a province-wide census conducted in 2014-2016, we aimed to identify spatial clusters of different forms of malnutrition in the province and determine its relative risk. Weight-for-age z score was used to categorize the malnourished children into severely underweight, moderately underweight, and overweight. We used the multinomial model of Kulldorff’s elliptical spatial scan statistic, adjusting for age and socioeconomic status. Four significant clusters across municipalities of Boac, Buenavista, Gasan, and Torrijos were found to have high risk of overweight and underweight simultaneously, indicating existence of double burden of malnutrition within these communities. These clusters should be targeted with tailored plans to respond to malnutrition, at the same time maximizing the resources and benefits.

scholarly journals Searching for space-time clusters: The CutL method compared to Kulldorff’s scan statistic

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Barbara Więckowska ◽  
Ilona Górna ◽  
Maciej Trojanowski ◽  
Agata Pruciak ◽  
Barbara Stawińska-Witoszyńska
Keyword(s):  
Disease Incidence ◽  
Incidence Rates ◽  
Space Time ◽  
Care Planning ◽  
Spatial Clusters ◽  
Health Care Planning ◽  
Scan Statistic ◽  
Software Company ◽  
The One ◽  
Analytical Tools

Both epidemiology and health care planning require analytical tools, especially for cluster detection in cases with unusually high rates of disease incidence. The aim of this work was to extend the application of the CutL method, which is used for detecting spatial clusters of any shape, to detecting space-time clusters, and to show how the method works compared to Kulldorff’s scan statistic. In the CutL method, clusters with disease incidence rates higher than the one entered by the researcher are searched for. The way in which the space-time version of that method works is illustrated with the example of data simulating the distribution of people affected by health problems in Polish counties in the period 2013- 2017. With respect to detection of irregularly shaped space-time clusters, the CutL method turned out to be more effective than Kulldorff’s scan statistic; for cylinder-shaped space-time clusters, the two methods produced similar results. The CutL method has also the important advantage of being widely accessible through the PQScut and PQStat programmes (PQStat Software Company, Poznan, Poland).

scholarly journals Detecting spatial clusters in functional data: New scan statistic approaches

2021 ◽  
pp. 100550
Author(s):  
Camille Frévent ◽  
Mohamed-Salem Ahmed ◽  
Matthieu Marbac ◽  
Michaël Genin
Keyword(s):  
Functional Data ◽  
Spatial Clusters ◽  
Scan Statistic

scholarly journals StarScan: A Novel Scan Statistic for Irregularly-Shaped Spatial Clusters

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Sriram Somanchi ◽  
David Choi ◽  
Daniel B. Neill
Keyword(s):  
Emergency Department ◽  
Training Data ◽  
Ratio Score ◽  
Spatial Clusters ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Total Change ◽  
Emergency Department Data ◽  
Log Likelihood ◽  
Spatial Scan

We propose StarScan, a new star-shaped scan statistic for detecting irregularly-shaped spatial clusters. StarScan generalizes the traditional, circular spatial scan statistic by allowing the radius of the cluster around a center location to vary continuously with the angle, but penalizes the log-likelihood ratio score proportional to the total change in radius. StarScan was compared with circular scan and fast subset scan on simulated respiratory outbreaks and bioterrorist anthrax attacks injected into real-world Emergency Department data. Given a small amount of labeled training data, StarScan learns appropriate penalties for both compact and elongated clusters, resulting in improved detection performance.

The Geography of Mental Health and General Wellness in Galveston Bay After Hurricane Ike: A Spatial Epidemiologic Study With Longitudinal Data

2016 ◽  
Vol 10 (2) ◽  
pp. 261-273 ◽  
Author(s):  
Oliver Gruebner ◽  
Sarah R. Lowe ◽  
Melissa Tracy ◽  
Magdalena Cerdá ◽  
Spruha Joshi ◽  
...  
Keyword(s):  
Mental Health ◽  
Epidemiologic Study ◽  
Spatial Clusters ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Hurricane Ike ◽  
Targeted Interventions ◽  
Geographic Space ◽  
Stress Symptoms ◽  
Geographically Targeted

AbstractObjectivesTo demonstrate a spatial epidemiologic approach that could be used in the aftermath of disasters to (1) detect spatial clusters and (2) explore geographic heterogeneity in predictors for mental health and general wellness.MethodsWe used a cohort study of Hurricane Ike survivors (n=508) to assess the spatial distribution of postdisaster mental health wellness (most likely resilience trajectory for posttraumatic stress symptoms [PTSS] and depression) and general wellness (most likely resilience trajectory for PTSS, depression, functional impairment, and days of poor health) in Galveston, Texas. We applied the spatial scan statistic (SaTScan) and geographically weighted regression.ResultsWe found spatial clusters of high likelihood wellness in areas north of Texas City and spatial concentrations of low likelihood wellness in Galveston Island. Geographic variation was found in predictors of wellness, showing increasing associations with both forms of wellness the closer respondents were located to Galveston City in Galveston Island.ConclusionsPredictors for postdisaster wellness may manifest differently across geographic space with concentrations of lower likelihood wellness and increased associations with predictors in areas of higher exposure. Our approach could be used to inform geographically targeted interventions to promote mental health and general wellness in disaster-affected communities. (Disaster Med Public Health Preparedness. 2016;10:261–273)

Fast Grid-Based Scan Statistic for Detection of Significant Spatial Disease Clusters

2004 ◽  
Author(s):  
Daniel B. Neill ◽  
A. Moore
Keyword(s):  
Scan Statistic ◽  
Disease Clusters ◽  
Grid Based
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