Link to Session Platform

Chairs(s):

Michael Desjardins, Johns Hopkins Bloomberg School of Public Health

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Description:

Advanced computational capabilities that emerged through continuous technological and methodological developments have transformed many scientific disciplines, including the domain of health and medical geography. Today, scientists tackle computational challenges that used to be virtually impossible to solve, because 1) our ability to collect and store health-related data has improved substantially, and 2) analytical methods for solving scientific problems can now be applied on a massive scale. Therefore, the spatiotemporal analysis and modelling of health-related issues has experienced and driven fundamental changes. The goal of this series of sessions is to create a platform for presenting and stimulate discussion of the accomplishments and remaining challenges of applying and developing computational methods that improve our understanding of health and medical geography. The sessions are also part of the 2022 Geospatial Health Symposium.

Topics may include, but are not limited to: -Disease mapping: spatial and/or spatiotemporal analysis and visualization; -Disparities in health care accessibility; -Stochastic methods for assessing the significance of observed disease patterns; -Spatially explicit and space-time disease modeling (retrospective or prospective); -The use of high-performance computing to analyze complex and massive spatiotemporal data for the discovery of relationships and patterns between health and environment; -Data science approaches, including statistics, data mining, and machine learning, to address issues within health and medical geography.

Sponsor Groups: Health and Medical Geography Specialty Group, Spatial Analysis and Modeling Specialty Group, Cyberinfrastructure Specialty Group

Non-Presenting Participants Agenda