A mathematically-driven framework for pandemic planning and management

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COVID-19 has brought into focus the lack of well-defined and robust tools to manage widespread pandemics, despite the fact that COVID is the fourth major global pandemic in the past 20 years (MERS in 2012-13, H1N1 in 2009-10, SARS in 2002-04). This research program addresses the needs of Canada's public health agencies and healthcare institutions in managing COVID and future pandemics by designing optimal mitigation strategies and vaccine prioritization policies. To obtain these optimal policies, this research leverages our completed COVID agent-based simulation model (ABM) for deep, mathematically-driven analysis in a framework generalizable to any population and disease. Public health agencies predominantly rely on high-level disease spread prediction models, called compartmental models (typically extensions of the traditional susceptible-infectious-recovered (SIR) model), to estimate how the pandemic will spread in terms of daily number of cases, hospitalizations, and deaths. The benefit of these models is that they only require high-level information about the disease, e.g., reproduction number (R0) (the average number of new infections caused by a single case), and population, e.g., size and contact rate; the need for minimal data means these models can be rapidly developed in the early stages of a pandemic. The drawback is that only high-level information goes in, so only high-level information comes out, making these models ill-suited for detailed policy assessments. By including detailed population demographic, medical, and economic information, ABMs, where individuals and their unique characteristics are individually represented, can more precisely simulate disease spread and test and optimize nuanced mitigation strategies. In particular, our incorporation of individual health status (e.g., comorbidities) based on regional prevalence allows for novel investigation of population outcomes, as well as equitably accounts for population diversity. For example, rural and poorer socio-economic areas usually have worse health status and higher prevalence of comorbidities, as well as reduced access to healthcare; these areas will be hit harder by pandemics, and are not represented in compartmental models. Mitigation strategies to respond to an emerging pandemic are generated by public health policymakers based on experience, intuition, limited data analysis (due to novelty of the pandemic), and consultation with modelling experts to answer "what if" questions regarding potential mitigation strategies, which almost certainly will not contain the optimal strategy. This research goes beyond this reactive approach by optimizing policies directly, rather than in an ad hoc what-if fashion, through a combination of optimization and machine learning approaches on the ABM and its contact networks. Additionally, this work provides human-interpretable assessments of the effectiveness of policy "levers" that can be pulled by public health officials.