In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

Yu-Tse Tsan, Endah Kristiani, Po-Yu Liu, Wei-Min Chu and Chao-Tung Yang
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Yu-Tse Tsan: Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan
Endah Kristiani: Department of Computer Science, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 407224, Taiwan
Po-Yu Liu: Division of Infection, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan
Wei-Min Chu: School of Medicine, Chung Shan Medical University, Taichung City 40201, Taiwan
Chao-Tung Yang: Department of Computer Science, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 407224, Taiwan

IJERPH, 2022, vol. 19, issue 11, 1-19

Abstract: The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM 2.5 at 20, NO 2 at 20, and O 3 at 22 error rates.

Keywords: COVID-19; AQI; air pollutant; correlation analysis; deep learning; LSTM; lag times (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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