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A Review of Graph Neural Networks in Epidemic Modeling

Published 28 Mar 2024 in cs.LG, cs.SI, physics.soc-ph, and q-bio.PE | (2403.19852v4)

Abstract: 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-modeling-papers. We hope that it will facilitate synergies between the communities of GNNs and epidemiology, and contribute to their collective progress.

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Summary

  • The paper reviews the integration of GNNs with epidemic modeling by presenting a structured taxonomy of tasks and data sources.
  • It distinguishes between neural and hybrid methods that capture spatial-temporal dynamics and support intervention analysis.
  • The survey highlights research challenges such as scalability, multi-modal data integration, and the need for explainable predictions.

Comprehensive Review on the Application of Graph Neural Networks in Epidemic Modeling

Introduction to the Survey

The application of Graph Neural Networks (GNNs) in the field of epidemic modeling has witnessed considerable advancement and growth, primarily fueled by the onset of the COVID-19 pandemic. The traditional mechanistic models, though insightful for understanding disease transmission mechanisms, face limitations when adapting to the intricacies and dynamism of real-world scenarios. The emergence of GNNs as a potent tool in capturing complex relational patterns presents a promising avenue for enhancing epidemic research. This survey meticulously reviews the incorporation of GNNs in epidemic tasks, exploring hierarchical taxonomies for both tasks and methodologies and shedding light on future directions.

Epidemiological Tasks Taxonomy

Epidemiological tasks are broadly categorized based on their objectives, encompassing Detection, Surveillance, Prediction, and Projection.

  • Detection tasks focus on the historical tracing of incidents, while Surveillance tasks emphasize real-time monitoring of events.
  • Prediction tasks are aimed at forecasting future incidents over longer time spans, not necessitating real-time processing.
  • Tasks under Projection seek to understand epidemic outcomes, incorporating changes like interventions and evolving initial states.

These categories offer a structured approach to addressing varied goals within epidemic modeling, guiding researchers in their specific domains of interest.

Data Sources and Graph Construction

The review highlights diverse sources of epidemiological data, including demographic records, mobility information, online searches, sensor-based data, and simulated data. The construction of graphs from these data sources plays a crucial role in modeling spatial-temporal dynamics and understanding disease spread. The taxonomy provided classifies graph construction based on the dynamicity of nodes and edges, capturing static and dynamic features, which is pivotal for accurately representing disease transmission networks.

Methodological Distinctions

The methodologies employed in leveraging GNNs for epidemic modeling are categorized into Neural Models and Hybrid Models. Neural Models rely solely on deep learning techniques to uncover patterns, whereas Hybrid Models blend the predictive prowess of neural networks with the foundational principles of mechanistic models. This section explores the specifics of each category, highlighting models' capabilities in spatial dynamics modeling, temporal dynamics modeling, and intervention modeling, thus offering insights into how GNNs are utilized to address different aspects of epidemic modeling.

Challenges and Future Directions

The survey identifies several challenges and potential research directions in applying GNNs to epidemic modeling. These include the need for models that can handle epidemic data at multiple scales, the integration of multi-modal data, the continuous nature of epidemic diffusion processes, the formulation of effective intervention strategies, and the generation of explainable predictions. Addressing these challenges requires innovative approaches and further research to enhance the accuracy, scalability, and interpretability of GNN-based epidemic models.

Conclusion

This survey provides a comprehensive overview of the application of GNNs in epidemic modeling, covering hierarchical taxonomies, methodological distinctions, challenges, and future directions. By bridging literature gaps and promoting the progression of this promising field, the survey aims to inspire and facilitate collaborations between the communities of GNNs and epidemiology, ultimately contributing to advancements in combating infectious diseases.

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