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Special feature: statistics for COVID-19 pandemic data. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2022; 5:275-277. [PMID: 35669413 PMCID: PMC9161758 DOI: 10.1007/s42081-022-00166-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Detection of space–time clusters using a topological hierarchy for geospatial data on COVID-19 in Japan. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2022; 5:279-301. [PMID: 35578605 PMCID: PMC9097570 DOI: 10.1007/s42081-022-00159-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 03/15/2022] [Accepted: 04/01/2022] [Indexed: 01/04/2023]
Abstract
In this paper, we detected space–time clusters using data on coronavirus disease 2019 (COVID-19) collected daily by each prefecture in Japan. COVID-19 has spread globally since the first confirmed case in China, in December 2019. Several people have to date been infected in Japan since the first confirmed case in January 2020. The outbreak of COVID-19 has had a significant impact on many people’s lives. Studies are being conducted to detect regions, called clusters, which pose a significantly higher risk of infection than their surrounding areas, based on a spatial scan statistics of COVID-19 infections. Among these studies, space–time cluster detection has to date been actively performed to gain knowledge regarding infection status. Based on the spatial scan statistic, the cylindrical scan method is a widely used space–time cluster detection method. This method enables concurrent detection of the location and time of a cluster occurrence. However, this method cannot capture spatial changes in a cluster over time. When applying the existing method to a cluster whose shape changes over time, the number of calculations required becomes extremely large, and the analysis may become difficult. In this study, we focused on the hierarchical structure of the data obtained by conducting an echelon analysis and applied the space–time cluster detection method based on this structure to enable the capture of changes in a cluster’s shape. Furthermore, we visualized the location and period of a cluster’s occurrence and considered the cause of the cluster.
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Benson R, Rigby J, Brunsdon C, Cully G, Too LS, Arensman E. Quantitative Methods to Detect Suicide and Self-Harm Clusters: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095313. [PMID: 35564710 PMCID: PMC9099648 DOI: 10.3390/ijerph19095313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/20/2022] [Accepted: 04/23/2022] [Indexed: 11/16/2022]
Abstract
Suicide and self-harm clusters exist in various forms, including point, mass, and echo clusters. The early identification of clusters is important to mitigate contagion and allocate timely interventions. A systematic review was conducted to synthesize existing evidence of quantitative analyses of suicide and self-harm clusters. Electronic databases including Medline, Embase, Web of Science, and Scopus were searched from date of inception to December 2020 for studies that statistically analyzed the presence of suicide or self-harm clusters. Extracted data were narratively synthesized due to heterogeneity among the statistical methods applied. Of 7268 identified studies, 79 were eligible for narrative synthesis. Most studies quantitatively verified the presence of suicide and self-harm clusters based on the scale of the data and type of cluster. A Poisson-based scan statistical model was found to be effective in accurately detecting point and echo clusters. Mass clusters are typically detected by a time-series regression model, although limitations exist. Recently, the statistical analysis of suicide and self-harm clusters has progressed due to advances in quantitative methods and geospatial analytical techniques, most notably spatial scanning software. The application of such techniques to real-time surveillance data could effectively detect emerging clusters and provide timely intervention.
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Affiliation(s)
- Ruth Benson
- School of Public Health, College of Medicine and Health, University College Cork, Western Gateway Building, T12 XF62 Cork, Ireland; (G.C.); (E.A.)
- National Suicide Research Foundation, University College Cork, 4.28 Western Gateway Building, T12 XF62 Cork, Ireland
- Correspondence:
| | - Jan Rigby
- National Centre for Geocomputation, Maynooth University, W23 F2H6 Maynooth, Ireland; (J.R.); (C.B.)
| | - Christopher Brunsdon
- National Centre for Geocomputation, Maynooth University, W23 F2H6 Maynooth, Ireland; (J.R.); (C.B.)
| | - Grace Cully
- School of Public Health, College of Medicine and Health, University College Cork, Western Gateway Building, T12 XF62 Cork, Ireland; (G.C.); (E.A.)
- National Suicide Research Foundation, University College Cork, 4.28 Western Gateway Building, T12 XF62 Cork, Ireland
| | - Lay San Too
- Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3053, Australia;
| | - Ella Arensman
- School of Public Health, College of Medicine and Health, University College Cork, Western Gateway Building, T12 XF62 Cork, Ireland; (G.C.); (E.A.)
- National Suicide Research Foundation, University College Cork, 4.28 Western Gateway Building, T12 XF62 Cork, Ireland
- Australian Institute for Suicide Research and Prevention, School of Applied Psychology, Griffith University, Brisbane, QLD 4122, Australia
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