  Since the onset of the COVID-19 pandemic, there has been a growing interest
in studying epidemiological models. Traditional mechanistic models
mathematically describe the transmission mechanisms of infectious diseases.
However, they often suffer from limitations of oversimplified or fixed
assumptions, which could cause sub-optimal predictive power and inefficiency in
capturing complex relation information. Consequently, Graph Neural Networks
(GNNs) have emerged as a progressively popular tool in epidemic research. In
this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic
tasks and highlight potential future directions. To accomplish this objective,
we introduce hierarchical taxonomies for both epidemic tasks and methodologies,
offering a trajectory of development within this domain. For epidemic tasks, we
establish a taxonomy akin to those typically employed within the epidemic
domain. For methodology, we categorize existing work into Neural Models and
Hybrid Models. Following this, we perform an exhaustive and systematic
examination of the methodologies, encompassing both the tasks and their
technical details. Furthermore, we discuss the limitations of existing methods
from diverse perspectives and systematically propose future research
directions. This survey aims to bridge literature gaps and promote the
progression of this promising field, with a list of relevant papers at
https://github.com/Emory-Melody/awesome-epidemic-modelingpapers. We hope that
it will facilitate synergies between the communities of GNNs and epidemiology,
and contribute to their collective progress.
