  Pandemics, with their profound societal and economic impacts, pose
significant threats to global health, mortality rates, economic stability, and
political landscapes. In response to these challenges, numerous studies have
employed spatio-temporal models to enhance our understanding and management of
these complex phenomena. These spatio-temporal models can be roughly divided
into two main spatial categories: norm-based and graph-based. Norm-based models
are usually more accurate and easier to model but are more computationally
intensive and require more data to fit. On the other hand, graph-based models
are less accurate and harder to model but are less computationally intensive
and require fewer data to fit. As such, ideally, one would like to use a
graph-based model while preserving the representation accuracy obtained by the
norm-based model. In this study, we explore the ability to transform from
norm-based to graph-based spatial representation for these models. We first
show no analytical mapping between the two exists, requiring one to use
approximation numerical methods instead. We introduce a novel framework for
this task together with twelve possible implementations using a wide range of
heuristic optimization approaches. Our findings show that by leveraging
agent-based simulations and heuristic algorithms for the graph node's location
and population's spatial walk dynamics approximation one can use graph-based
spatial representation without losing much of the model's accuracy and
expressiveness. We investigate our framework for three real-world cases,
achieving 94\% accuracy preservation, on average. Moreover, an analysis of
synthetic cases shows the proposed framework is relatively robust for changes
in both spatial and temporal properties.
