Journal article
A web-based analytical framework for the detection and visualization space-time clusters of COVID-19
Cartography and geographic information science, v 51(2), pp 311-329
03 Mar 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The COVID-19 pandemic has had a profound impact worldwide and continues to spread due to various mutations of the virus. Many governmental and nonprofit agencies at different levels have quickly developed COVID-19 dashboards to disseminate information on the pandemic to the public. However, most of these systems have mainly distributed "plain" information (e.g. cases, death counts, vaccination), and rarely provided insights that can be gained from spatiotemporal analyses, such as the detection of emerging clusters. The results from these analyses hold tremendous potential for health policymakers as they try to identify ways to slow down transmission. We present a web-based geographic framework to detect and visualize space-time clusters of COVID-19. Our tightly coupled framework integrates the prospective space-time scan statistics and local indicators of spatial association (LISA) with novel 2D and 3D interactive visuals in a cyber environment (
http://159.223.164.41/app/
). We illustrate the applicability of our approach using COVID-19 data for the continental US. Our framework is portable to other regions that may experience infectious diseases but is also flexible to handle data of different spatial and temporal granularities. This paper fits within an effort to integrate space-time analytics for the monitoring of infectious diseases in web environment, ultimately improving health surveillance systems.
Metrics
Details
- Title
- A web-based analytical framework for the detection and visualization space-time clusters of COVID-19
- Creators
- Yu Lan - University of North Carolina at CharlotteEric Delmelle - Drexel University, Urban Health Collaborative
- Publication Details
- Cartography and geographic information science, v 51(2), pp 311-329
- Publisher
- Taylor & Francis
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative
- Web of Science ID
- WOS:001088051300001
- Scopus ID
- 2-s2.0-85174302479
- Other Identifier
- 991021874215104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Geography