MS08 - MEPI-11 Part 2 of 4

Advances in infectious disease modelling: towards a unifying framework to support the needs of small and large jurisdictions (Part 2)

Friday, July 18 at 10:20am

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Note: this minisymposia has multiple sessions. The other sessions are: Part-1 Part-3 and Part-4.

Amy Hurford (Memorial University), Michael Li, Public Health Agency of Canada

Description:

Homogeneous mixing and the aggregation of diverse population groups into one group are frequent simplifying assumptions that may produce erroneous models and recommendations that exacerbate health inequities. Yet, models that make these simplifying assumptions have well-understood dynamics, and can be quickly solved, facilitating data fitting and uncertainty analysis that can support policy recommendations. Advancing the methodology around these model-building tensions is needed, and the best modelling approach may depend on the application. The motivation for developing these modelling approaches is from the COVID-19 pandemic in Canada. Atlantic Canada, the Canadian territories, and other small Canadian jurisdictions experienced different epidemiology, and needed different types of modelling support, than the larger Canadian provinces. There is a need to advance infectious disease modelling to support jurisdictions at all levels, and this session furthers this goal by including talks that describe: infectious disease spread in structured communities; importations and mobility networks; models that were developed for specific small jurisdictions, methods for calculating the reproduction number, estimating healthcare demand, and describing how the needs of small jurisdictions can be integrated into pandemic preparedness plans.

Sally Otto

University of British Columbia

"Coupled dynamics and the challenge of estimating Rt in small jurisdictions"

During the COVID-19 pandemic, estimates of the reproductive number (Rt) or growth rate (r) from different jurisdictions were often surprisingly similar, given expected variation in contact rates. In this talk, I discuss how signatures of growth can be misleading in areas small jurisdictions and what we can learn from considering coupled dynamics with migration among areas.

Julien Arino

University of Manitoba

"Introduced cases and spread of infection in a community"

The recent COVID-19 pandemic made it clear that governments the world over would not hesitate to take public health measures of consequence to curtail the spread of pathogens. Among the myriad of measures used, travel interruptions, enhanced border control and quarantine, targeted specifically spatio-temporal spread. Sometimes, these travel measures were demonstrably useful, but altogether, the overall benefits remain debated. In order to quantify the effect of these measures, it is important to understand how disease introductions unfold in locations from which they are at that point absent. In particular, gaining some sense of the relative contributions of externally and locally generated cases is critical. To do this, we explore numerically a continuous-time Markov chain derived from a simple deterministic metapopulation model for case introduction.

Jude Kong

University of Toronto

"Human Behavior and Epidemic Dynamics: Adaptive vs. Robust Control Strategies in Shaping Outbreak Outcomes"

Human behavior significantly influences epidemic dynamics through complex interactions driven by risk perception and public health interventions. In this talk, we use a model of epidemic spread to examine how adaptive control strategies, where individuals dynamically adjust behaviors in response to trends like case doubling rates or awareness campaigns, influence disease transmission compared to robust control strategies that enforce fixed reductions in risky activities. We equally explore how behavioral adaptations, such as risk compensation—where perceived lower risks lead to increased risky behaviors—or risk homeostasis, where individuals maintain a constant level of acceptable risk, can undermine these control efforts. Our findings suggest that adaptive control strategies, by leveraging responsive behavioral changes, may offer a more effective approach to mitigating epidemic spread. These insights highlight the critical role of understanding and harnessing human behavioral dynamics in designing effective public health strategies for outbreak management.

Pouria Ramazi

Brock University

"Modeling Behavioral Heterogeneity to Optimize Vaccine Uptake Through Tailored Communication"

This talk explores how heterogeneity in individual decision-making influences vaccine uptake and how understanding this variation can enhance public health strategies. We distinguish between two primary behavioral types: evidence-based learners, who base their decisions on immediate personal payoff, and social-based learners, who are influenced by the observed experiences of others. The relative proportions of these two groups in a population fundamentally shape uptake dynamics. Through a mechanistic modeling framework and identifiability analysis, we demonstrate that these group proportions are not only theoretically identifiable but also practically estimable from vaccine uptake data. Our results show significant variation in these proportions across jurisdictions, suggesting that a one-size-fits-all communication strategy may be suboptimal. Tailoring messages to target specific behavioral profiles can more effectively promote vaccination and improve overall public health outcomes.

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Annual Meeting for the Society for Mathematical Biology, 2025.