PHAC Models on COVID-19

Canada’s response to Covid-19 is being guided by data and mathematical modelling. 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. At this phase of the epidemic, efforts are in controlling the epidemic and increasing capacity in health care systems.

Mathematical Models:


This compartmental model was developed using Analytica software¹. The model is a dynamic Susceptible-Exposed-Infectious-Removed (SEIR) model that uses differential equations to estimate the change in populations in the various compartments. These compartments are connected between each other and individuals can move from one compartment to another, in a specific order that follows the natural infectious process. The model uses knowledge obtained from studies around the world on the biology of transmission of the virus causing COVID-19 to produce a mathematical representation of how COVID-19 may spread in the Canadian population under different scenarios.

PHAC Agent-Based Model on SARS-CoV2

This study assessed the potential for current and future nonpharmaceutical interventions (NPIs) to prevent SARS-CoV-2 transmission. The modelers developed an age-stratified agent-based simulation model for Canada, under the assumption that community transmission began on February 7, 2020.

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. This model aims to evaluate the success of public health interventions depending upon community structure and population dynamics.

PHAC Gathering Risks Model: On-line Calculator

Gatherings may contribute significantly to the spread of infectious diseases. This risk calculator, based on a probabilistic modelling framework and principles, assess introduction and transmission risk associated with gatherings. The generality of this modelling framework disentangles the factors affecting transmission risk at gatherings. Ideally, this tool can be useful for public health decision-making.