Hosted by
The Public Health Agency of Canada (PHAC)’s Data, Surveillance and Foresight Branch (DSFB) and the National Collaborating Centre for Infectious Diseases (NCCID).
Date and time
November 28, 2023
1:00 to 2:00pm Eastern Time / 12:00 to 1:00pm Central Time
Synopsis
Globally, data science is revolutionizing public health surveillance, introducing the potential for a data-driven approach that makes use of advanced analytics, machine learning, and comprehensive data integration. Data science enables rapid analysis of diverse data sources, such as social media, healthcare records, and environmental data, facilitating early detection of potential disease outbreaks with the goal of improving the timeliness of interventions and optimized resource allocation.
While data science presents remarkable opportunities for advancing public health surveillance, it also brings challenges. The complexity and nature of advanced data science techniques may prevent non-experts from interpreting results accurately, emphasizing the need for clear communication. Additionally, the infrastructure and resource requirements for implementing robust data science solutions can be substantial, potentially limiting its adoption, particularly in resource-constrained regions. This webinar will discuss the challenges and opportunities of data science to shape the future of public health surveillance, and to enhance data quality, interpretability, and accessibility.
Resources
Learning Objectives
By the end of this seminar, participants will be able to:
- Understand how AI, data science, and mathematical solutions can be used responsibly to improve public health surveillance and response to emerging and re-emerging infectious disease outbreaks
- Understand the feasibility and benefits of using AI to enable real-time updates of transmission modeling with diverse incoming data streams
- Highlight the accuracy benefits secured by incorporating wastewater data sources among the real datasets used to ground models
- Note the likely strong opportunities from jointly updating models of multiple pathogens sharing common risk factors with cross-pathogen surveillance data
Moderators
- Louis Wong, Acting Manager, DSFB, PHAC
- Luisa Arroyave, Post-doctoral Fellow, NCCID
Speakers
Dr. Jude Kong
- Executive Director, Africa-Canada Artificial Intelligence & Data Innovation Consortium
- Executive Director, Global South AI for Pandemic and Epidemic Preparedness and Response Network (AI4EP)
- Executive Director, Resilience Research Atlantic Alliance on Sustainability, Supporting Recovery and Renewal (REASURE2)
- Assistant Professor, Department of Mathematics & Statistics, Faculty of Science, York University
Dr. Nathaniel Osgood
- Professor, Department of Computer Science, University of Saskatchewan
- Director, Computational Epidemiology & Public Health Informatics Laboratory
- Chief Research Advisor, Saskatchewan Centre for Patient-Oriented Research
- Associate Faculty, Community Health & Epidemiology
- Chief Scientist, Computer Simulation & Advanced Research Technologies (CSART)
- Associate Faculty, Bioengineering Division.
Accreditation statement
Surveillance Advances is a self-approved group learning activity (Section 1) as defined by the Maintenance of Certification Program of the Royal College of Physicians and Surgeons of Canada. Surveillance Advances is also approved by the Council of Professional Experience for professional development hours for members of the Canadian Institute of Public Health Inspectors.
Format
Seminar will be held on Zoom. Speakers have approximately 30 minutes to present their material, followed by approximately 15 minutes for discussion and questions from attendees. Presentation materials in English and French will be distributed through NCCID media channels.
Access instructions
All instructions for the seminar series will be posted on the Eventbrite registration page and will be emailed to all registrants prior to the event.
Following the event, recordings and slides for each seminar will be distributed through NCCID media channels.
