Spatiotemporal disease mapping and analysis
The session recording will be archived on the site until June 25th, 2023
This session was streamed but not recorded
Date: 3/26/2023
Time: 2:40 PM - 4:00 PM
Room: Mineral Hall E, Hyatt Regency, Third Floor
Type: Paper,
Theme:
Curated Track: AAG's GeoEthics Initiative and Related Effort
Sponsor Group(s):
Geographic Information Science and Systems Specialty Group, Health and Medical Geography Specialty Group, Spatial Analysis and Modeling Specialty Group
Organizer(s):
Hui Luan University of Oregon
Michael Desjardins Johns Hopkins University
Eric Delmelle The University of North Carolina at Charlotte
Chair(s):
Hui Luan University of Oregon
Description:
Mapping disease incidence/prevalence and identifying risk factors have been longstanding areas of interest in public health and spatial epidemiology. The emergence of the COVID-19 pandemic has stimulated extensive research in spatiotemporal disease mapping and analysis, including a flood of dashboard visualizing disease spread over space and time via Web and mobile applications. With innovations in data acquisition and dissemination, and with methodological advances in analyzing complex longitudinal data, contemporary disease mapping research has been increasingly focused on understanding how health varies across space, time, socioeconomic, and demographic groups. There are, however, a number of unresolved and challenging methodological issues in mapping and analyzing spatiotemporal health outcomes and behaviours, including but not limited to:
• Sparse data and noise (e.g., zero-inflation)
• Spatiotemporally misaligned data analysis
• Multiscale modeling
• Spatially and temporally varying regression modeling
• Multivariate modeling of more than one health outcome
• Missing data and imputation
• Spatiotemporal cluster detection
• Disease surveillance
• Uncertainty modelling and quantification
• Human mobility and social distancing in disease spreading
• Geoprivacy
• Geovisual analytics of disease patterns
This session is a part of the Geospatial Health Research Symposium, which is organized by the Health and Medical Geography Specialty Group. This yearly symposium aims to bring together national and international scholars, practitioners, and policy makers from different specialties, institutions, sectors, and continents to share ideas, findings, methodologies, and technologies, and to establish and strengthen personal connections, communication channels, and research collaborations. For more information about the symposium, please feel free to contact Paul Delamater at pld@email.unc.edu.
Presentations (if applicable) and Session Agenda:
Kyunghee Rhyu |
An Ecological Model of COVID-19 Space-Time Disparities in Vaccination Rates for Texas |
Aynaz Lotfata, Chicago State University |
Local associations between asthma prevalence and socio-ecological determinants across the United States using spatial machine learning |
Joseph Hoover |
Evaluating environmental and built-environment factors associated with COVID-19 cases on the Navajo Nation |
Hui Luan, University of Texas Southwestern Medical Center |
Food accessibility at the walkable distance scale: a spatial statistical analysis using the Bayesian two-part log-normal model |
Abolfazl Mollalo |
Deep learning in depression rate modeling |
Non-Presenting Participants
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Spatiotemporal disease mapping and analysis
Description
Type: Paper,
Date: 3/26/2023
Time: 2:40 PM - 4:00 PM
Room: Mineral Hall E, Hyatt Regency, Third Floor
Contact the Primary Organizer
Hui Luan University of Oregon
hluan@uoregon.edu
Session Sponsored By
