Advances in Computational Approaches for Geospatial Health Applications - Epidemiology

Type: Paper
Theme:
Sponsor Groups: Health and Medical Geography Specialty Group, Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group
Organizers: Michael Desjardins, Alexander Hohl, Eun-Kyeong Kim
Chairs: Michael Desjardins

Call for Submissions

If you are interested in joining this session, please email your PIN, title, abstract, and contact information to the following organizers: Michael R. Desjardins (UNC-Charlotte; mdesjar2@uncc.edu), Claudio Owusu (UNC-Charlotte; cowusu@uncc.edu), Alexander Hohl (University of Utah; alexander.hohl@geog.utah.edu), and Eun-Kyeong Kim (University of Zurich, eun-kyeong.kim@geo.uzh.ch). Please note that we will not accept submissions after the AAG deadline.


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.


Themes:

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

Discussants: Michael Widener (University of Toronto); Eric Delmelle (UNC-Charlotte); Fahui Wang (LSU); Xun Shi (Dartmouth); Ying Song (University of Minnesota).


Agenda

Type Details Minutes
Presenter Michael Richard Desjardins*, Johns Hopkins University; Bloomberg School of Public Health; Spatial Science for Public Health Center, Rajib Paul, University of North Carolina at Charlotte, Matthew Eastin, University of North Carolina at Charlotte, Irene Casas, Louisiana Tech University, Eric Delmelle, University of North Carolina at Charlotte, Spatio-temporal modeling of neighborhood level risks for dengue, chikungunya, and Zika in Cali, Colombia 15

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