Dynamic Network Anomaly Modeling of Cell-Phone Call Detail Records for Infectious Disease Surveillance Open Access

Abstract

Global monitoring of novel diseases and outbreaks is crucial for pandemic prevention. To this end, movement data from cell-phones is already used to augment epidemio- logical models. Recent work has posed individual cell-phone metadata as a universal data source for syndromic surveillance for two key reasons: (1) these records are already collected for billing purposes in virtually every country and (2) they could allow deviations from people’s routine behaviors, both in terms of mobility and social interactions such as during illness, to be detected. In this paper, we develop the necessary models to conduct population-level infectious disease surveillance by using cell-phone metadata individually linked with health outcomes. Specifically, we propose GraphDNA—a model that builds Graph neural networks (GNNs) into Dynamic Network Anomaly detection. Using cell-phone call records (CDR) linked with diagnostic information from Iceland during the H1N1v influenza outbreak, we show that GraphDNA outperforms state-of-the-art baselines on individual Date- of-Disease (DoD) prediction, while tracking the epidemic signal in the overall population. Our results suggest that proper modeling of the universal CDR data could inform public health officials and bolster epidemic preparedness measures.

Table of Contents

Introduction -- P1

Background and Related Work -- P5

The GraphDNA Framework -- P8

Experiments -- P22

Applications -- P30

Conclusion -- P32

Appendix A -- P34

Appendix B -- P36

Bibliography -- P38

About this Honors Thesis

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School	Emory College
Department	Quantitative Science
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Computer Science
Keyword	Graph Mining Network Anomaly Detection Dynamic Network Deep Learning Disease Monitoring
Committee Chair / Thesis Advisor	An, Weihua, Emory University
Committee Members	Vigfusson, Ymir, Emory University Yang, Carl, Emory University

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