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

Song, Hongwen (Spring 2022)

Permanent URL: https://etd.library.emory.edu/concern/etds/9k41zf910?locale=en


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

Rights statement
  • Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.
  • English
Research Field
Committee Chair / Thesis Advisor
Committee Members
Last modified

Primary PDF

Supplemental Files