If you are interested in joining this session, please email your PIN, title, abstract, and contact information to the following organizers: Moongi Choi (University of Utah; email@example.com), Marynia Kolak (University of Chicago, firstname.lastname@example.org), Michael R. Desjardins (Johns Hopkins University; email@example.com), Alexander Hohl (University of Utah; firstname.lastname@example.org), and Eun-Kyeong Kim (University of Zurich, email@example.com). Please note that we will not accept submissions after the AAG deadline.
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 2021 Geospatial Health Symposium.
Session 1: Environmental Health
Session 2: Accessibility
Session 3: Epidemiology
Session 4: Methods
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: Spatial Analysis and Modeling Specialty Group, Cyberinfrastructure Specialty Group, Health and Medical Geography Specialty Group
|Presenter||Yigong Wang*, University of Chicago, Marynia Kolak, University of Chicago, Qinyun Lin, University of Chicago, Jiaqi Yang, University of Chicago, Uncertainty in COVID Data: The Importance of Distinguishing between Data Sources in COVID Epidemiological Surveillance||15||8:00 AM|
|Presenter||Alexander Hohl*, University of Utah, Michael Desjardins, Johns Hopkins University, Eric Delmelle, University of North Carolina at Charlotte, Yu Lan, University of North Carolina at Charlotte, Simon Brewer, University of Utah, COVID-19 Surveillance||15||8:15 AM|
|Presenter||Ingrid Luffman*, East Tennessee State University, Andrew Joyner, East Tennessee State University, William Tollefson, East Tennessee State University, Abbey Mann, East Tennessee State University, Megan Quinn, East Tennessee State University, Rurality and COVID-19 in Tennessee: Identifying metrics to assess and communicate pandemic spread||15||8:30 AM|
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