FasssTrace: Social Network Analysis Approach in Modeling Contract Tracing for Covid-19 in the Philippines

Date of Award

2021

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Information Systems & Computer Science

First Advisor

Maria Regina E. Estuar., PhD

Abstract

As of date most contact tracing efforts implemented by the local government units to stem the transmission of COVID-19 follow stan- dard manual contract tracing procedures. However, with the constant increase in the speed of the COVID-19 outbreak, traditional contact trac- ing approaches are not sufficient to contain the spread of the disease. This study aims to make contact tracing more efficient by developing a contact tracing model for digital platforms. Using the R platform, this study applied standard social network analysis in the context of contact tracing. Through the computation of micro-level and macro-level net- work measures, this study was able to analyze a contact tracing dataset from a highly urbanized city in Luzon and 1) provide a social network model of confirmed cases with their close contacts 2) identify key nodes in the network and 3) understand its transmission dynamics. The con- structed network took the form of a two-mode network where location nodes are integrated with the person nodes to connect unlinked cases. From the analysis of the key nodes identified in the network, it was re- vealed that 81% of all the COVID-19 links in the city was facilitated by only 5% of the infected population. Moreover, a closer look on the dis- tribution of the micro-level degree scores of all the nodes in the network reveal that in the earlier stages of the pandemic, the situation was more manageable as the distribution portrays a weak likelihood to a power- law distribution, but was overturned when the restrictions loosened.

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