Speaker
Description
A key challenge in infectious disease modelling is to extend beyond classical epidemiological structures to model the interplay between the spread of a disease and how people respond to an outbreak \cite{lejeune2025formulating}. Recent research highlights the potential of incorporating a human behaviour feedback loop into existing model structures as a promising way to advance disease forecasting, and provide better methods for decreasing the negative impact of disease countermeasures on society \cite{gozzi2025comparative,rahmandad2022enhancing}. This talk presents an age-cohort infectious disease spread model, and builds upon recent contributions for exploring pandemic response strategies in the context of limited resources \cite{andrade2024preparing} in three ways. First, the it incorporates a negative feedback structure to capture human behaviour which moderates disease spread; second, it adds a supply chain structure that determines the availability of countermeasures; and third, it implements allocation policies to allow for prioritisation of specific age cohorts for vaccine distribution. The scenario results are framed based on the overall aNack rate and hospitalisation rate, and the conventional scenario analysis is extended to encompass techniques from the discipline of statistical learning – namely explanatory model analysis \cite{biecek2021explanatory}, which can be deployed to highlight influential model parameters.
Bibliography
@article{andrade2024preparing,
title={{Preparing for pandemic response in the context of limited resources}},
author={Andrade, Jair and Beishuizen, Berend and Stein, Mart and Connolly, M{\'a}ire and Duggan, Jim},
journal={System Dynamics Review},
volume={40},
number={3},
pages={e1775},
year={2024},
publisher={Wiley Online Library}
}
@book{biecek2021explanatory,
title={Explanatory model analysis: explore, explain, and examine predictive models},
author={Biecek, Przemyslaw and Burzykowski, Tomasz},
year={2021},
publisher={Chapman and Hall/CRC}
}
@article{gozzi2025comparative,
title={{Comparative evaluation of behavioral epidemic models using COVID-19 data}},
author={Gozzi, Nicol{`o} and Perra, Nicola and Vespignani, Alessandro},
journal={Proceedings of the National Academy of Sciences},
volume={122},
number={24},
pages={e2421993122},
year={2025},
publisher={National Academy of Sciences}
}
@article{lejeune2025formulating,
title={{Formulating human risk response in epidemic models: Exogenous vs endogenous approaches}},
author={LeJeune, Leah and Ghaffarzadegan, Navid and Childs, Lauren M and Saucedo, Omar},
journal={European Journal of Operational Research},
volume={324},
number={1},
pages={246--258},
year={2025},
publisher={Elsevier}
}
@article{rahmandad2022enhancing,
title={{Enhancing long-term forecasting: Learning from COVID-19 models}},
author={Rahmandad, Hazhir and Xu, Ran and Ghaffarzadegan, Navid},
journal={PLoS computational biology},
volume={18},
number={5},
pages={e1010100},
year={2022},
publisher={Public Library of Science San Francisco, CA USA}
}
