SARS‑CoV‑2 vaccination and immunity modelling

Understanding immunity after SARS‑CoV‑2 infection and vaccination is critical to guide vaccination and pandemic recovery efforts.

Experts from York University and the National Research Council of Canada (NRC) are using mechanistic models of immunity development combined with machine learning to help predict outcomes of vaccination against COVID‑19. Their models take into account host immunity outcomes for vaccine single and 2‑dose regimens, intervals between doses, and various vaccine types: viral vector vaccines, protein subunit vaccines, and ribonucleic acid (RNA)‑based vaccines such as messenger ribonucleic acid (mRNA).

The models will be provided to public health organizations such as the Public Health Agency of Canada (PHAC), the National Advisory Committee on Immunization (NACI), the Canadian Immunization Research Network (CIRN), and academic researchers to inform vaccine design and vaccination policy. In the future, the models could be expanded to other types of vaccines, to vaccines against other viruses, and to therapeutics.

Collaborators

• York University, Faculty of Science, Department of Mathematics and Statistics and Center for Disease Modeling
• NRC‑Fields Mathematical Sciences Collaboration Centre

Objectives

• Expand an existing (base) mechanistic model of in‑host SARS‑CoV‑2 virus lifecycle to include pathogen mutations/variants
• Develop a complementary mechanistic model of the host immune system to uncover the complex interactions between interferon signalling pathways and the adaptive immune response to SARS‑CoV‑2 infection and vaccination
• Use machine learning algorithms to inform parameter estimates for the mechanistic models, considering different empirical data sets

Deliverables

• Models that generate synthetic data outputs (predictions, digital twin) that follow the same distributions as empirical biological data, which can be used by public health agencies to guide vaccination policies and predict safety and efficacy of different vaccine types

Project team

• Jane Heffernan, Principal Investigator, York University
• Mohammad Sajjad Ghaemi, Research Officer, NRC‑Fields Collaboration Centre, NRC
• James Ooi, Research Officer, NRC‑Fields Collaboration Centre, NRC