Institute of Data Science & Department of Computer Science, School of Computing

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Intelligent Data Science for Contact Tracing and Outbreak Investigation

The recent emergence and re-emergence of coronavirus epidemics has sparked the need for better preparedness for the frequent appearance and spread of infectious diseases. Data science can be an invaluable resource for enhancing our epidemic preparedness and in combating these disease outbreaks.

 

In this seed project, we aim to develop intelligent data science methodologies and applications for (1) contract tracing and (2) outbreak investigation. As a proof of concept, we propose to explore mining large-scale digital traces based on wi-fi access points to infer a weighted contact network based on spatiotemporal colocation of two devices connected to the same AP at the same time. We will collect and use a real-world wi-fi dataset to investigate the potential strengths and limitations of using such data for contact tracing and infer a weighted contact network that can be used for outbreak investigation such as the prediction of disease spread patterns for pre-planning exercises. We will also develop privacy-preserving methods to address potential privacy concerns on the use of such digital traces.

 

 

Investigators and Team Members

 

Principal Investigator: Professor See-Kiong Ng (IDS)

Co-Principal Investigator: Associate Professor Stephane Bressan (Dept of CS)

 

Team Members:

Guilherme Augusto Trevisan De Almeida Cintra Zagatti, Wu Tingfeng, Zhang Ruixi

 

 

 

 

Resources: Github Public Repository

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