A Review of Graph Neural Networks in Epidemic Modeling
CoRR(2024)
摘要
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 fall short when confronted with the growing challenges of
today. 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. We hope that it will facilitate synergies
between the communities of GNNs and epidemiology, and contribute to their
collective progress.
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