PIMS- UAlberta Math Biology Seminar: Chris Bauch and Madhur Anand

Decision-makers attempting to restrict the spread of forest pests through infested firewood imported as a result of decisions of the population of park users must contend with population behaviour that both causes pest outbreaks, and responds to them, resulting in a human-environment systems. During the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination in a similarly evolving landscape where infection dynamics and population mitigating behaviour are mutually connected in a feedback loop. In this talk we will describe how modelling human-environment interactions in forest pest control provides a framework for tackling similar types of questions for COVID-19. Our forest pest model demonstrates how decision-makers can prioritise interventions such as park-park closure, firewood subsidies and checkpoints in the context of behavioural dynamics. In the case of COVID-19 we address the question: which age group should be prioritized for COVID-19 vaccination in order to prevent the most deaths? We developed an age-structured human-environment mathematical model for Ontario, Canada, where evolutionary game theory describes how population adherence to non-pharmaceutical interventions (NPIs) responds to case incidence. We find that vaccinating older age groups prevents the most deaths if vaccination begins early in2021. But if it begins later, then vaccinating age groups responsible for the most transmission could actually prevent the most deaths, depending on the parameter regime. We observe that many features of human behaviour are common across different natural systems--such as social learning, responsiveness to natural states, and social norms. As a result, interdisciplinary interactions may inform policies intended to support human-environment sustainability.