COVID-19 DATA EXPLORATION USING MAPPER ALGORITHM
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 stu...
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| Main Author: | |
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| Format: | Final Year Project Report |
| Language: | English |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2023
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/44131/1/Carey%20Ling%20%20ft.pdf http://ir.unimas.my/id/eprint/44131/ |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | 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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