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.
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.
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.
Mathematical modelling can provide timely evidence to guide decision-making. As public health planners strive to prepare for potential outbreaks of COVID-19, this quantification can help identify the type and intensity of control measures required to mitigate infection spread.
Behind the Curtain of Mathematical Modelling : Inside a collaborative modelling project on public health strategies for syphilis management
Case study on the application of mathematical modelling to assess the impact of a newly-designed intervention on the burden of syphilis in Winnipeg, Manitoba.
Their story illustrates how mathematical modelling can provide timely evidence to guide decision-making by public health planners and practitioners throughout the implementation of a new intervention.
The lessons they share may help to demystify modelling and reveal the benefits of collaborations between modellers and public health personnel.
A glossary of terms with their definitions that can be used to conceptualize and parameterize models consistent with those applied in public health, epidemiology, and clinical settings related to infectious diseases.
School closures continue to be considered by public health as a way to manage the spread and severity of influenza. This project provides an up-to-date review and assessment of studies on the effectiveness of closing schools.
A review of terms commonly used in modelling studies of influenza infection spread and control. The objective is to understand the similarities and discrepancies between definitions of the same terms used in different studies.
This document provides details of a proposed logical framework for influenza infection, to enhance the utility and uptake of modelling for public health response.
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)
Margaret Haworth Brockman (PI)
NCCID and University of Manitoba
Indian Council of Medical Research
Indian Institute of Technology, Roorkee
Pan-InfORM – NCCID Workshop (2018)