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; Chao-Tung Yang

doi:10.3390/ijerph19116373

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

Int J Environ Res Public Health. 2022 May 24;19(11):6373. doi: 10.3390/ijerph19116373.

Authors

Yu-Tse Tsan^{1

2

3}, Endah Kristiani^{4

5}, Po-Yu Liu⁶, Wei-Min Chu^{2

3

7

8

9

10}, Chao-Tung Yang^{4

11}

Affiliations

¹ Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan.
² School of Medicine, Chung Shan Medical University, Taichung City 40201, Taiwan.
³ Division of Occupational Medicine, Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan.
⁴ Department of Computer Science, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 407224, Taiwan.
⁵ Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia.
⁶ Division of Infection, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan.
⁷ Department of Family Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan.
⁸ School of Medicine, National Yang Ming Chiao Tung University, Taipei City 11221, Taiwan.
⁹ Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung City 40227, Taiwan.
¹⁰ Institute of Health Policy and Management, National Taiwan University, Taipei City 10617, Taiwan.
¹¹ Research Center for Smart Sustainable Circular Economy, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 407224, Taiwan.

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, NO₂ at 20, and O₃ at 22 error rates.

Keywords: AQI; COVID-19; LSTM; air pollutant; correlation analysis; deep learning; lag times.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
COVID-19* / epidemiology
Deep Learning*
Humans
Pandemics
Particulate Matter / analysis

Substances

Air Pollutants
Particulate Matter

Grants and funding

This work was sponsored by the Ministry of Science and Technology (MOST), Taiwan, under Grant No. MOST 110-2221-E-029-020-MY3, MOST 110-2621-M-029-003, MOST 110-2811-E-029-003, and MOST 111-2622-E-029-003. This work also supported by grants from Taichung Veterans General Hospital (TCVGH), Taiwan under Grant No. TCVGH-T1087804, TCVGH-T1097801, TCVGH-T1107803, TCVGH-1107201C, TCVGH-T1117803, TCVGH-NK1099003, and TCVGH-1103602D.