Harnessing Geospatial Big Data for Infectious Diseases
Type: Virtual Paper
Day: 2/26/2022
Start Time: 9:40 AM
End Time: 11:00 AM
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Sponsor Group(s):
Cyberinfrastructure Specialty Group
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Organizer(s):
Zhenlong Li
, Shengjie Lai
, Bo Huang
, Kathleen Stewart
Chairs(s):
Zhenlong Li, University of South Carolina
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Description:
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 ongoing 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 paper 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:
• 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.
• Innovative methodologies for geospatial big data analytics in the context of infectious diseases, including geocomputation algorithms and geostatistical models. For example, assessing the effectiveness of non-pharmaceutical interventions in preventing the resurgence of COVID-19 using human mobility data.
• 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, for example, the spreading, disparity, morbidity, and mortality of COVID-19.
• 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 such as the COIVD-19 pandemic.
Presentation(s), if applicable
Wenbin Zhang, ; Untangling the changing impact of non-pharmaceutical interventions and vaccination on European Covid-19 trajectories |
Yi-Chen Wang, National University of Singapore; Assessing Spatiotemporal Vulnerability for COVID-19 in Singapore |
Caglar Koylu, ; FlowMapper.org: A web-based computational and visual framework for flow mapping and analysis |
Zhenlong Li, Pennsylvania State University; Measuring Human Mobility Dynamics and Place Connectivity Using Big Social Media Data |
Non-Presenting Participants Agenda
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Harnessing Geospatial Big Data for Infectious Diseases
Description
Virtual Paper
Contact the Primary Organizer
Zhenlong Li - zhenlong@psu.edu