Abstract

In this study, we first review contact tracing methods used during the COVID-19 pandemic and provide an analysis of the benefits and drawbacks of different data collection and privacy protection methods. Our findings provide the benefits and drawbacks of using GPS versus Bluetooth for contact tracing, as well as the different data collection and storage methods and their effectiveness in protecting user privacy. We then analyze Twitter data to understand individual mobility behavior and sentiment by using a Natural Language Processing tool on text content of user tweets and Tweet coordinate data. Social media's ability to provide data on location and user sentiment through tweet text content makes it a complementary data source for understanding individual mobility behavior and attitudes during a disaster such as a global pandemic. Analyzing mobility behavior and sentiment during COVID-19 lockdown protocols is crucial in understanding the effectiveness of these pandemic procedures and improving them for future pandemics. We also assess the viability of using Google Maps GPS data for contact tracing during the COVID-19 pandemic. To do this, we generated activity diaries from a participant's Google Maps Location data and GPS ground truth data and compared visited locations and stay durations obtained for each location recording method. This study provides a thorough analysis of the use of passively collected location data to investigate the effect of pandemic restrictions and track individuals' interactions and visited locations for contact tracing purposes. It is our intention to aid health authorities, government officials, and other policy makers in developing innovative responses to these threats so that the most negative effects of future pandemics may be avoided.

Notes

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Graduation Date

2021

Semester

Fall

Advisor

Hasan, Samiul

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering; Smart Cities Track

Format

application/pdf

Identifier

CFE0008834; DP0026113

URL

https://purls.library.ucf.edu/go/DP0026113

Language

English

Release Date

December 2021

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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