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Adaptive epidemic interventions on geometric graphs

Werf, Luuk van der (2025) Adaptive epidemic interventions on geometric graphs.

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Abstract:The COVID-19 pandemic demonstrated the need for effective intervention strategies to mitigate the spread of infectious diseases, while keeping the societal impact to a minimum. Traditional epidemic research often assumes either a homogeneous social network or fixed interventions. The assumption of homogeneous social networks ignores any geometric influence, while the assumption of fixed interventions neglects the dynamic relationship between disease prevalence and adherence to interventions. This thesis introduces a novel framework to combine adaptive intervention strategies with geometric random graphs. By introducing adaptive interventions that respond to local and global infection levels, we investigate the impact of different social distancing approaches, including distance-based, weight-based, and infection probability-based interventions. Through extensive simulations on randomly generated as well as real-life networks, we find that both targetting high-degree nodes and targetting long distance edges are effective in reducing peak and total infections. Furthermore, our results show that local information might be beneficial, by focussing interventions on the infected areas. However, the adaptive methods do not consistently outperform the threshold methods, which implies that the feedback relation between adherence to the measures and the disease prevalence does not always have a positive impact.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics
Programme:Applied Mathematics MSc (60348)
Link to this item:https://purl.utwente.nl/essays/106086
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