  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. 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 the models are available as
supplementary material, ensuring reproducibility.
