Research
Theme 4: Intervention and Control
As evident in the COVID-19 pandemic, a substantial challenge in managing epidemics is the duration of the application of non-pharmaceutical interventions (NPIs), especially those that disrupt “normal” societal organization and function such as social distancing. These measures quickly become infeasible as pandemic fatigue builds. As pharmaceutical interventions (PIs) such as vaccines, antiviral, or antimicrobial therapies become available, their gradual deployment, concurrently with a potential weakening of the NPIs, raises organizational challenges ; uncertainty about vaccine or antimicrobial efficacy or potential for vaccine waning further complicates the issue, as do potential viral evolutions in response to the use of therapeutic tools. COVID-19 has highlighted the need for novel approaches using a One Health (OH) and the following projects aim to do just that.
Advancing agent-based simulation scalability
Co-Project Investigators: Christian Muise (Queen’s University), Gias Uddin (University of Calgary), Manos Papagelis (York University) and Morgan Craig (Université de Montréal)
High-fidelity simulation models provide the most accurate representation of the spread of COVID and related diseases but are subject to computational limitations. Agent-based modelling, which treats each individual as a unique agent with objectives/properties/etc., is an example of such a high-fidelity model. The project’s objective is to realize a vastly improved scalability of this type of analysis, using modern techniques in the field of deep learning and AI — specifically in representation learning of dynamical processes.
Evolution of the virus: variants, transmission bottlenecks, and fitness
Co-Project Investigators: Jane Heffernan, Jude Dzevela Kong, Iain Moyles, Hanna Jankowski (York University), Jacques Bélair (Université de Montréal), James Watmough (University of New Brunswick), Matthew Betti (Mount Allison University)
Viral evolution can undermine the positive benefits of public health, therapeutic and vaccine interventions during a pandemic/epidemic. This project aims to develop models of virus evolution in-host, and extend our mathematical analysis to virus transmission bottlenecks, to determine probabilities of mutant virus transmission. We will quantify probabilities of virus evolution and transmission in-host and between hosts, given host heterogeneities in infection and immune system characteristics, and different characteristics of virus mutants that increase virus ‘fitness’.
Mathematical modelling of human response behaviour, opinion dynamics, and social influence during pandemics
Co-Project Investigators: Iain Moyles (York University) and Rebecca Tyson (University of British Columbia)
COVID-19 showed that understanding human response to intervention is essential in mitigating disease spread and forming policy. We are particularly interested in understanding how opinion influence affects vaccine and NPI hesitancy. This project aims to incorporate a broader understanding of intervention and control, which embodies the entire theme.
Vaccination and antimicrobials, from the individual to the population: Real-time modelling and data analysis to project therapeutic intervention
Co-Project Investigators: Morgan Craig (Université de Montréal), Stéphanie Portet, Julien Arino and Kang Ling Liao (University of Manitoba)
Vaccination and antimicrobials are a critical piece of the pharmaceutical arsenal in the fight against (R-) emerging infectious disease (EID). The COVID-19 pandemic has shown that deploying treatment and vaccination in the middle of a crisis poses specific complications, particularly when the efficacy and protection of different interventions against the pathogen’s evolutionary landscape are not fully understood. Rapid and accurate forecasting of drug and vaccine dosing needs is paramount to successful public health planning. Grasping the population-level consequences of individual-level properties of therapeutic tools is crucial. The project aims to develop within-host mathematical models of pathogen evolution and pharmaceutical interventions and link them to population-level public health intervention models. To support this work, we will incorporate new data analysis and visualization tools and population genetics.
The implementation of mobility restrictions, in combination with vaccination and non-pharmaceutical interventions, to meet the needs of small communities during a pandemic
Co-Project Investigators: Amy Hurford (Memorial University of Newfoundland), James Watmough (University of New Brunswick), Matthew Betti (Mount Allison University) and Monica Cojocaru (University of Guelph)
During the pandemic, no clear criteria for the formation or dissolution of the Atlantic bubble was established, and while there may have been other communities in Canada, particularly remote communities, including First Nations, and border communities, where quarantine-free travel zones were warranted, the conditions for the formation of such zones, including vaccination levels, and the best approaches to NPI implementation within the quarantine-free zones has not been resolved. This research will serve the needs of Atlantic Canada and other small communities, including First Nations.
Identifying the distribution of human thresholds in responding to (non-) pharmaceutical policies for emerging zoonotic infectious diseases
Co-Project Investigators: Huaiping Zhu (York University) and Pouria Ramazi (Brock University)
This project aims to analyze the role of human adherence to (non-)pharmaceutical mitigating policies in the spread of zoonotic diseases. Using a susceptible infected removed (SIR) model coupled with the linear threshold model. The project team will estimate the distribution of individuals’ thresholds for policies such as vaccination and social distancing. By incorporating this distribution into the SIR plus linear threshold dynamics, the project aims to forecast the future infected cases for a given zoonotic disease. This research seeks to understand better human behaviour’s impact on the spread of zoonotic diseases and develop effective strategies for mitigating their impact.
Modelling NPIs to evaluate past policy strategies for effectiveness
Co-Project Investigators: Jane Heffernan (York University), Jacques Bélair (Université de Montréal), James Watmough (University of New Brunswick), Jude Kong (York University), Iain Moyles (York University), Matthew Betti (Mount Allison University), Hanna Jankowski (York University)
Viral evolution can undermine the positive benefits of public health, therapeutic and vaccine interventions during a pandemic/epidemic. In this project we will develop models of virus evolution in-host, and extend our mathematical analysis to virus transmission bottlenecks, to determine probabilities of mutant virus transmission. The work will be used to determine (1) initial probabilities of zoonotic transmission, with differences in fitness between host species (i.e., bats and humans for COVID-19), and (2) the development and transmission of new viral variants in humans. This work aligns with Themes 2, 3, 4 in various ways. Specifically, we will quantify probabilities of virus evolution and transmission in-host and between hosts, given host heterogeneities in infection and immune system characteristics, and different characteristics of virus mutants that increase virus ‘fitness’ (i.e., virus production in-host: immune escape, virus budding, increased infectivity).