Modelling human behaviour response to public policy and its impact on infectious disease spread - case studies using AI/ML, data science, game theory and optimization

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The Universities of Guelph, Calgary, University of British Columbia, Université de Montréal are partnering with Sanofi, Spectus L'Observatoire Québécois de l'Adaptation aux Changements Climatiques (OQACC) and the upcoming Center of Excellence for Respiratory Pathogens (CERP - hosted at Les Hospices Civils de Lyon University Hospital Network) to improve preparedness for respiratory pathogen epidemics in Canada, in the wake of the COVID-19 pandemic. The proposal has national and international collaborators and expands the Canadian emerging infectious disese modelling to France via the collaboration with the upcoming The pandemic has created perturbations to the normal patterns of respiratory pathogens (such as influenza, RSV, etc.), largely due to non-pharmaceutical interventions (NPIs) adopted for COVID-19 prevention: mask wearing, handwashing, and physical distancing. The arrival and implementation of vaccinating policies related to the pandemic has brought into focus an increased rate of vaccine hesitancy in general. Compliance fatigue with COVID-19 public health policy is currently experienced all over the world and might extend to other vaccination programs, while any remaining NPI measures may become increasingly ignored. This will result in an increase of susceptible individuals and may cause for instance a severe influenza season.Our partnership's goal is to better understand and quantify how populations' behaviour and decision-making mechanisms contribute to the dynamics of pathogen transmission. There is a two-way feedback channel between the epidemiology of a pathogen and its spread, and the population's reaction to its presence. Decision-makers and public health experts inform policy, policy is implemented, and the population reacts. Policy success is directly correlated to its adoption levels in the population, while changes in adoption lead to an increase/decrease of pathogen spread. More specifically, we wish to show how individual-level, group-level and population-level decision-making on policy adoption may influence the control of a pathogen transmission.