Mathematical modelling is a research method that can inform public health planning and infectious disease control. Through complex simulations of real-world possibilities, mathematical modelling provides a cost-effective and efficient method to assess optimal public health interventions.
COVID-19 Models from the Public Health Agency of Canada
The Public Health Agency of Canada (PHAC) has shared information with Canadians from their COVID-19 modelling work. The results from the data indicate that it is critical and essential to physically distance, detect and isolate cases of COVID-19, identify and quarantine close contacts, and prevent international infection from entering the country.
NCCID supports an expanding area of knowledge translation and exchange related to mathematical modelling for public health.
This has included bringing modellers, public health practitioners, and decision-makers together to respond to public health priorities such as influenza, sexually transmitted infections, tuberculosis and now, COVID-19. We build awareness for the value of modelling research for infectious disease public health to introduce more public health professionals to modelling research. By making modelling terms and research more accessible, we bridge knowledge silos. Using case studies, we demonstrate how public health and modeller partnerships bring valuable and different roles and knowledge.
New work will help foster more modelling research and community partnerships to address research questions to reduce the inequitable burden of infectious diseases, particularly for rural and remote regions and communities.
mod4PH
One of the ways we support knowledge exchange is through mod4PH, a discussion forum and virtual meeting place for public health and mathematical modellers. Members promote the use of modelling research in public health decision-making for infectious disease prevention and control. Learn more about mod4PH and join the group on LinkedIn.
What’s New
PHAC Agent-Based Model on COVID-19
Canadian Medical Association Journal (CMAJ) Article CMAJ Article – Projected effects of nonpharmaceutical public health interventions to prevent resurgence of…
PHAC SEIR Model on COVID-19
Canada Communicable Disease Report (CCDR) Publication Summary CCDR Article – Modelling Scenarios of the Epidemic of COVID-19 in Canada The article describes predictive…
COVID-19 Vaccine Outlook & Opportunities for Modelling
Immunization by vaccine is one of the most successful public health interventions for the prevention and control of both endemic infectious diseases and emerging pathogens.
Join us and Dr. Joanne Langley who will present an overview of current vaccine development for COVID-19.
Epidemiology of COVID-19 – Evidence from China
Emerging infectious diseases can spread dramatically and lead to large-scale outbreaks, as seen in pandemics of influenza H1N1, MERS, SARS-CoV-1 and, most recently, SARS-CoV-2 (COVID-19).
Join us and Dr. Benjamin Cowling who will overview the natural history of COVID-19 and shares recent findings from his epidemiological research.
Mathematical Modelling in Public Health Planning: Flu Vaccine
In public health, mathematical modelling helps us answer difficult, real-world questions and understand complex relationships between biological, demographic, and environmental…
Mathematical Modelling in Public Health: Tuberculosis
In public health, mathematical modelling helps us answer difficult, real-world questions and understand complex relationships between biological, demographic, and environmental…
Journal Papers
Toward standardizing a lexicon of infectious disease modelling terms
Modelling COVID-19 Outbreaks
An international team of experts, led by Dr. Seyed Moghadas at York University in Canada, received a grant from the Canadian Institutes for Health Research for a data-driven modelling approach to describe COVID-19 outbreaks and assess the effectiveness of responses for populations in Canada, the US and India. NCCID is pleased to be a partner in this initiative and lead the knowledge translation and exchange aspects of this grant.
Key research outputs include the degree of surge capacity necessary to maintain safe and effective delivery of healthcare systems, estimates of clinical attack rates and likely inpatient flow, effectiveness and cost-effectiveness of intervention strategies and the expected reduction of disease outcomes, and the effect of social policies on reducing community transmission.
Collectively, the investigators bring expertise in disease dynamics, modelling and simulations, data analysis and statistical inference, public health and vaccination, knowledge translation, and infectious disease epidemiology.
The team of Investigators include:
Seyed Moghadas (PI)
York University
Margaret Haworth Brockman (PI)
NCCID and University of Manitoba
Alison Galvani (@Alison_Galvani)
Yale University
David Champredon (@DChampredon)
Western University
Joanne Langley (@jmllhfx)
Dalhousie University
Sandip Banerjee
Indian Institute of Technology, Roorkee
Sandip Mandal
Indian Council of Medical Research
Dr. Yoav Keynan
NCCID and University of Manitoba
Events
Past Events
Pan-InfORM – NCCID Workshop (2018)