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Title: From Epidemic to Pandemic Modelling
Authors: Connolly, S
Gilbert, D
Heiner, M
Keywords: SIR model;coloured Petri nets;stochastic Petri nets;continuous Petri nets;ODEs;simulation;geographic spatio-temporal modelling;multiscale models
Issue Date: 14-Jul-2022
Publisher: Frontiers Media SA
Citation: Connolly, S., Gilbert, D., Heiner, M. (2022) 'From Epidemic to Pandemic Modelling', Frontiers in Systems Biology, 2, pp. 1 - 23. doi:10.3389/fsysb.2022.861562.
Abstract: We present a methodology for systematically extending epidemic models to multilevel and multiscale spatio-temporal pandemic ones. Our approach builds on the use of coloured stochastic and continuous Petri nets facilitating the sound component-based extension of basic SIR models to include population stratification and also spatio-geographic information and travel connections, represented as graphs, resulting in robust stratified pandemic metapopulation models. The epidemic components and the spatial and stratification data are combined together in these coloured models and built in to the underlying expanded models. As a consequence this method is inherently easy to use, producing scalable and reusable models with a high degree of clarity and accessibility which can be read either in a deterministic or stochastic paradigm. Our method is supported by a publicly available platform PetriNuts; it enables the visual construction and editing of models; deterministic, stochastic and hybrid simulation as well as structural and behavioural analysis. All models are available as Supplementary Material, ensuring reproducibility. All uncoloured Petri nets can be animated within a web browser at, assisting the comprehension of those models. We aim to enable modellers and planners to construct clear and robust models by themselves.
Description: Data Availability Statement: The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
ISSN: 2674-0702
Appears in Collections:Dept of Computer Science Research Papers

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