Mathematical modelling is a research method that can improve public health planning and infectious disease control. Big Data refers to very large and diverse datasets that are analyzed at high velocity to reveal patterns, trends, and associations.
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.
Looking for the PHAC Modelling Group?
NCCID supports an expanding area of knowledge translation and exchange related to mathematical modelling and the use of Big Data for public health.
NCCID brings together modellers, public health practitioners, and decision-makers together to respond to public health priorities such as COVID-19, influenza, sexually transmitted and blood-borne infections, and tuberculosis and now, COVID-19. We build awareness of the value of modelling research and big data, especially amongst public health professionals.
During the Covid-19 pandemic, public health authorities looked at several ways, including big data, to understand the influence of travel, social contact, and mobility factors on the effectiveness of public health measures. Although the SARS CoV-2 virus is one important area to explore, big data can also provide useful insights on other pathogens and public health issues.
NCCID has reframed dialogue on big data to address ethical concerns and questions: What can be learned from big data? What can be lost if big data is not used?
NCCID leads several knowledge translation activities on big data, including:
- infographics to clarify basic concepts related to big data,
- big data research demonstration projects,
- discussions about ethics, methods, and applicability of big data research findings, and
- case studies featuring lessons learned from public health authorities that use big data.
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.
The COVID-19 Public Health Agency of Canada (PHAC) Modelling Group
COVID-19 has spread across the world, and the associated morbidity and mortality, spawning extensive international research to inform both clinical…
PHAC Agent-Based Models on COVID-19
Agent-based models (ABMs) can systematically simulate actions and interactions of independent “agents” that can represent people, places and/or objects within a predefined environment. These models aim to evaluate the success of public health interventions depending upon community structure and population dynamics.
mpox: Perspectives from the US
This webinar will cover the approaches to infection prevention and control of mpox as well as the clinical evaluation and management of mpox infections in the United States.
Advantages and Disadvantages of Modelling with an Equity Lens
This fact sheet summarizes some key opportunities and challenges of using disaggregated data to model infectious disease with an equity lens.
Infectious Disease Modelling with an Equity Lens
This fact sheet describes infectious disease modelling with an equity lens. It provides a definition, offers examples of disaggregation, and highlights the characteristics of infectious diseases transmission that can be influenced by population dynamics.
Waves of Endemicity: Mathematical Models of Disease Persistence in Populations
This webinar examines how mathematical models have improved our understanding of disease persistence in populations and explores how models of endemicity may be applied to evolving COVID-19.
Toward standardizing a lexicon of infectious disease modelling terms
Improving public health policy through infection transmission modelling: Guidelines for creating a community of practice
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
Alison Galvani (@Alison_Galvani)
David Champredon (@DChampredon)
Joanne Langley (@jmllhfx)
Indian Institute of Technology, Roorkee
Indian Council of Medical Research
Dr. Yoav Keynan
NCCID and University of Manitoba
Pan-InfORM – NCCID Workshop (2018)