Higher-order Interaction Matters: Dynamic Hypergraph Neural Networks for Epidemic Modeling Público

Abstract

The ongoing need for effective epidemic modeling has driven advancements in capturing the complex dynamics of infectious diseases. Traditional models, such as Susceptible-Infected-Recovered, and graph-based approaches often fail to account for higher-order interactions and the nuanced structure pattern inherent in human contact networks. This study introduces a novel Human Contact-Tracing Hypergraph Neural Network framework tailored for epidemic modeling called EpiDHGNN, leveraging the capabilities of hypergraphs to model intricate, higher-order relationships from both location and individual level. Both real-world and synthetic epidemic data are used to train and evaluate the model. Results demonstrate that EpiDHGNN consistently outperforms baseline models across various epidemic modeling tasks, such as source detection and forecast, by effectively capturing the higher-order interactions and preserving the complex structure of human interactions. This work underscores the potential of representing human contact data as hypergraphs and employing hypergraph-based methods to improve epidemic modeling, providing reliable insights for public health decision-making.

Table of Contents

1 Introduction 1

2 Background 5

2.1 Mechanistic Epidemic Modeling . . . . . . . . . . . . . . . . . . . . . 5

2.2 Graphs for Epidemic Modeling . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Hypergraphs for Epidemic Modeling . . . . . . . . . . . . . . . . . . . 8

3 Formulation 9

3.1 Hypergrpah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Dynamic Hypergrpah . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.3 Epidemic Modeling Tasks . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3.1 Source Detection . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3.2 Infection Forecasting . . . . . . . . . . . . . . . . . . . . . . . 11

4 Method 12

4.1 HGNN Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.2 Temporal Convolution Module . . . . . . . . . . . . . . . . . . . . . . 13

4.3 Contact Pattern Awareness Module . . . . . . . . . . . . . . . . . . . 14

4.4 EpiDHGNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5 Experiments 17

5.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

i

5.2 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5.3 RQ1 - Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5.4 RQ2 - Ablation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5.5 RQ3 - Module Effectiveness . . . . . . . . . . . . . . . . . . . . . . . 22

5.6 RQ4 - Generalizability . . . . . . . . . . . . . . . . . . . . . . . . . . 23

6 Conclusion 25

6.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

6.1.1 Hypergraph SIR . . . . . . . . . . . . . . . . . . . . . . . . . . 25

6.1.2 Alternative Model Selection . . . . . . . . . . . . . . . . . . . 26

6.1.3 Human Contact Data Simulation . . . . . . . . . . . . . . . . 26

6.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

A Appendix 28

A.1 Hypergraph SIR Simulation . . . . . . . . . . . . . . . . . . . . . . . 28

Bibliography 31

About this Honors Thesis

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School	Emory College
Department	Computer Science
Degree	B.A.
Submission	Honors Thesis
Language	English
Research Field	Computer Science Health Sciences, Epidemiology
Palabra Clave	Epidemiology Deep Learning Hypergraph Graph Data Mining
Committee Chair / Thesis Advisor	Wei jin, Emory University
Committee Members	Carl Yang, Emory University Jiaying Lu, Emory University

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