Mathematical Modelling & Big Data

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.

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 to respond to public health priorities such as COVID-19, influenza, sexually transmitted and blood-borne infections, and tuberculosis. We build awareness of the value of modelling research and big data, especially amongst public health professionals.

What’s New

Math Modelling

Mathematical modelling helps public health answer some complex, real-world questions that can support evidence-driven responses to infectious disease threats.

An example of how models are developed, interpreted, and applied is illustrated in this video focused on modelling tuberculosis prepared by the NCCID and the National Collaborating Centre for Indigenous Health. 

mod4PH Network

One of the ways we support knowledge exchange is through mod4PH, a network of public health professionals and math modellers. Members promote math modelling research to support public health decision-making for infectious disease prevention and control. Join the group on LinkedIn to stay updated on new projects. 

The mod4PH podcast showcases new and relevant math modelling concepts and research for public health, featuring infectious disease modelling experts from various fields. Find new episodes of this podcast on the NCCID Webcasts page.  

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?

Big Data

The NCCID brings collaborators together to explore what can be learned from big data to inform public health decision-making for the control of infectious diseases.

Concerns and questions about the ethics of big data for public health purposes are prudent but can also limit knowledge sharing about how these data are or could be useful. NCCID re-frames dialogue on big data with a different question–not ‘What are the risks?’, but ‘What can be learned from big data’?

The Covid-19 pandemic fueled demands for real-time and geo-positioning data to understand emerging risks. Public health authorities looked for ways to understand the influence of travel and mobility factors, as well as the effectiveness of public health measures aimed at limiting mobility, social contact and disease spread. Although the SARS CoV-2 virus is one important area to explore, big data may provide useful insights on other pathogens, drivers and determinants.

NCCID leads several knowledge translation activities on big data for public health, including

Talking About Big Data Use

Public health’s use of big data calls for adherence to privacy laws and ethical standards, but also for our best efforts at open and clear communication with the public about data practices. 

NCCID highlights the following educational initiative for the public from the Public Health Agency of Canada (PHAC).

Visit the Public Health Agency of Canada’s Public Health Data web pages to learn about:

  • how and why PHAC gathers and analyzes a variety of different data
  • how PHAC respects and protects privacy when gathering and using public health data
  • innovative public health data projects  

Journal Papers

Toward standardizing a lexicon of infectious disease modelling terms

Improving public health policy through infection transmission modelling: Guidelines for creating a community of practice

Effect of flight connectivity on the introduction and evolution of the COVID-19 outbreak in Canadian provinces and territories