COVID-19 DATA EXPLORATION USING MAPPER ALGORITHM

Carey, Ling Yu Fan (2023) COVID-19 DATA EXPLORATION USING MAPPER ALGORITHM. [Final Year Project Report] (Unpublished)

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Abstract

Topological data analysis (TDA) is a new and rapidly evolving area that offers a variety of novel topological and geometric methods such as persistent homology and mapper algorithm for implying significant elements from potentially complex data. COVID-19 data set that is frequently used in studies is often complex and massive, containing multiple fields such as number of cases and date information that cannot be analysed with traditional data analysis tool which relies on overly simplified assumptions. To investigate and capture the development of the pandemic, mapper algorithm can be used to visualize and analyse the COVID data set provided by the government. Application of mapper algorithm through Kepler Mapper, a python implementation of mapper is done to construct mapper graphs for state wise COVID�19 data in Malaysia along year 2021. The resulting mapper graphs reveal the pandemic’s development progress across time and place during year 2021. Several hot spots and significant growth of COVID-19 cases are discovered in states like Selangor and Sarawak through the graphs. The peak of COVID-19 cases in each state occurred during June to September 2021 as a result from mass festival gathering and new highly transmittable COVID variant. Future analysis could go in a number of different directions include utilizing high dimensional data and persistent homology to study the pandemic.

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