Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| स्वरूप: | थीसिस |
| भाषा: | English |
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Brac University
2021
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| ऑनलाइन पहुंच: | http://hdl.handle.net/10361/15400 |
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10361-154002022-01-26T10:13:16Z Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19 Tabassum, Tahia Rahman, Saiham Mahmood, Moosfiqur Hassan Siam, Md. Fahim Mumu, Sadia Anika Alam, Md. Golam Rabiul Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University COVID-19 LSTM Air Pollution K-Means Clustering COVID-19 Mortality Regression COVID-19 Lockdowns COVID-19 (Disease) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-45). Nationwide lockdowns implemented in consequence of the devastating COVID-19 pandemic, caused noticeable improvements in air quality throughout the world. This paper implements a multivariate long-short term memory network to forecast changes in the Air Quality Index and Particulate Matter 2.5 (PM2.5) concentration for 26 cities in India, and 50 cities in Europe, had their lockdown not occurred or been extended. A linear regression model was used to correlate confounderadjusted PM2.5 values with COVID-19 mortality rate in the U.S.A. Heat maps were visualized with K-Means Clustering that signified the correlation between increased air pollution with higher COVID-19 cases and mortality rates. Our results indicate that 76% of the European cities in our dataset underwent at least a 40% improvement in air quality as a result of their lockdowns, whereas 17 out of the 26 Indian cities observed 20%. Adjusted PM2.5 was seen to be a statistically significant contributor to increasing mortality rate, with a single unit increase contributing to 3% more deaths due to COVID-19, at a 95% confidence level. Tahia Tabassum Saiham Rahman Moosfiqur Hassan Mahmood Md. Fahim Siam Sadia Anika Mumu B. Computer Science 2021-10-19T04:41:15Z 2021-10-19T04:41:15Z 2021 2021-01 Thesis ID 17301183 ID 17101116 ID 17101105 ID 20141040 ID 20141032 http://hdl.handle.net/10361/15400 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 52 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
COVID-19 LSTM Air Pollution K-Means Clustering COVID-19 Mortality Regression COVID-19 Lockdowns COVID-19 (Disease) |
| spellingShingle |
COVID-19 LSTM Air Pollution K-Means Clustering COVID-19 Mortality Regression COVID-19 Lockdowns COVID-19 (Disease) Tabassum, Tahia Rahman, Saiham Mahmood, Moosfiqur Hassan Siam, Md. Fahim Mumu, Sadia Anika Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19 |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Tabassum, Tahia Rahman, Saiham Mahmood, Moosfiqur Hassan Siam, Md. Fahim Mumu, Sadia Anika |
| format |
Thesis |
| author |
Tabassum, Tahia Rahman, Saiham Mahmood, Moosfiqur Hassan Siam, Md. Fahim Mumu, Sadia Anika |
| author_sort |
Tabassum, Tahia |
| title |
Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19 |
| title_short |
Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19 |
| title_full |
Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19 |
| title_fullStr |
Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19 |
| title_full_unstemmed |
Correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of COVID-19 |
| title_sort |
correlating lockdowns, mortality rates and air pollution: a deep learning imbued study of covid-19 |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15400 |
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