Recording Plan: 

Curated Track: 

Zhenlong Li, Pennsylvania State University

Call For Participation
We invite presentations to this session that has been organized for four years. To present a paper in this session, please submit your abstract online by October 31, 2024, at (https://www.aag.org/events/aag2025/), and email your abstract code, paper title, and abstract to one of following organizers by December 2, 2024.

Description:

Aims and Scope:

Public health is inextricably linked to geospatial context. Where, when, and how people interact with natural, social, built, economic and cultural environments directly influence human health outcomes, policy making, planning and implementation, especially for infectious diseases such as COVID-19, HIV, and influenza. Geospatial data has long been used in health studies, dating back to John Snows’ groundbreaking mapping of cholera outbreaks in London, and continuing today in a wide range of scientific inquiries, e.g., examining the effects of environmental, neighborhood, and demographic factors on health outcomes, understanding accessibility and utilization of health services, modeling the spread of infectious diseases, assessing the effectiveness of disease interventions, and developing better healthcare strategies to improve health outcomes and equity.

Emerging sources of geospatial big data, such as data collected from social sensing, remote sensing, and health sensing (health wearables) contain rich information about the environmental, social, population, and individual factors for health that are not available in traditional health data and population statistics. Along with innovative spatial and computing methodologies in GIScience, geospatial big data provides unprecedented opportunities for advancing the infectious disease research. The COVID-19 pandemic further highlights the demand on and the power of big data and spatial analysis in modeling, simulating, mapping, and predicting the spread of infectious diseases and their intervention across the world.

Along these lines, this session aims to capture recent advancements in leveraging geospatial big data and spatial analysis in infectious disease-related research, such as disease mapping and cluster detection, early detection and warning of disease outbreaks, and spatial analysis and modeling of disease spread and control.

Potential topics include (but are not limited to) the following:
1- Collection, processing, and integration of geospatial big data (e.g., satellite images, floor plans, 3D models, social media and mobile phone data) with health big data (e.g., electronic medical records) to extract geospatial context at various spatiotemporal scales (e.g., environmental risks, socioeconomic factors, and population mobility) to address infectious disease questions.

2- Innovative methodologies for geospatial big data analytics in the context of infectious diseases, including geocomputation algorithms and geostatistical models.

3- Combining geospatial big data with advanced computing technologies such as machine learning (ML) and geospatial artificial intelligence (GeoAI) to uncover hidden patterns and new information in infectious diseases related to the spreading, disparity, morbidity, and mortality.

4- Developing accessible and reusable geovisualization and mapping methods, sharable data products, and online tools that help foster multidisciplinary collaborations, engage community and facilitate public understanding and decision-making during disease outbreaks.

We invite presentations to this session that has been organized for four years. To present a paper in this session, please submit your abstract online by October 31, 2024, at (https://www.aag.org/events/aag2025/), and email your abstract code, paper title, and abstract to one of following organizers by December 2, 2024.

Organizers:
1- Zhenlong Li, The Pennsylvania State University, US, zhenlong@psu.edu
2- M. Naser Lessani, The Pennsylvania State University, US, mzl6134@psu.edu
3- Shengjie Lai, University of Southampton, UK, shengjie.lai@soton.ac.uk
4- Bo Huang, Chinese University of Hong Kong, China, bohuang@cuhk.edu.hk
5- Kathleen Stewart, University of Maryland, US, stewartk@umd.edu

Non-Presenting Participants