A web-based geographic framework for detecting and visualizing space-time clusters of infectious diseases: Using COVID-19 data in the United States as an example
Topics: Geographic Information Science and Systems
, Cartography
, Health and Medical
Keywords: COVID-19, space-time scan statistics, geovisualization, WebGIS, space-time cluster detection, tight coupling system
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 11:20 AM (Eastern Time (US & Canada)) - 2/26/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 20
Authors:
Yu Lan, University of North Carolina at Charlotte
Eric Delmelle, University of North Carolina at Charlotte
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Abstract
Robust space-time clustering detection is essential in spatial epidemiology, especially for infectious diseases. Local indicators of spatial association (LISA) and space-time scan tests are two popular methods to detect local space and space-time clusters for infectious diseases. However, not many COVID-19 dashboards or applications incorporate methods of space-time clustering detection. Besides the integration of those methods, another critical issue is the space-time geovisualization of results using those methods. The geovisualization of space-time clusters remains as a challenge because of the requirement of presenting space, time, and variables at the same time. This study proposes a web-based geographic framework for detecting and visualizing space-time true clusters for infectious diseases. To illustrate the framework, a prototype based on this framework using COVID-19 data in U.S. is developed. The specific objectives of this study include: (1) to implement customized space-time clustering detection; (2) to generate interactive geovisualization of space-time clustering results from the first objective; (3) a tight coupling system that incorporated daily data updated and components for the objective one and two. The proposed web-based geographic framework for detecting and visualizing space-time clusters for infectious diseases and the prototype using COVID-19 data in the US are straightforward and accessible for epidemiologists to monitor the transmission and make responded health policies. The tight coupling design of the framework and the prototype help users to focus on the results and discover space-time patterns. It also could be used to introduce space-time closeting detection for educational purposes.
A web-based geographic framework for detecting and visualizing space-time clusters of infectious diseases: Using COVID-19 data in the United States as an example
Category
Virtual Paper Abstract
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