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

Author

Listed:
  • Yu-Tse Tsan

    (Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan
    School of Medicine, Chung Shan Medical University, Taichung City 40201, Taiwan
    Division of Occupational Medicine, 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
    Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia)

  • 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
    Division of Occupational Medicine, Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan
    Department of Family Medicine, Taichung Veterans General Hospital, Taichung City 407204, Taiwan
    School of Medicine, National Yang Ming Chiao Tung University, Taipei City 11221, Taiwan)

  • Chao-Tung Yang

    (Department of Computer Science, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 407224, Taiwan
    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, NO 2 at 20, and O 3 at 22 error rates.

Suggested Citation

  • Yu-Tse Tsan & Endah Kristiani & Po-Yu Liu & Wei-Min Chu & Chao-Tung Yang, 2022. "In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning," IJERPH, MDPI, vol. 19(11), pages 1-19, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6373-:d:822755
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    References listed on IDEAS

    as
    1. Ankit Gupta & Hemant Bherwani & Sneha Gautam & Saima Anjum & Kavya Musugu & Narendra Kumar & Avneesh Anshul & Rakesh Kumar, 2021. "Air pollution aggravating COVID-19 lethality? Exploration in Asian cities using statistical models," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 6408-6417, April.
    2. Yu Fu & Shaofu Lin & Zhenkai Xu, 2022. "Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread," IJERPH, MDPI, vol. 19(6), pages 1-13, March.
    3. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
    4. Malki, Zohair & Atlam, El-Sayed & Hassanien, Aboul Ella & Dagnew, Guesh & Elhosseini, Mostafa A. & Gad, Ibrahim, 2020. "Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    5. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(C).
    6. Yu-Tse Tsan & Der-Yuan Chen & Po-Yu Liu & Endah Kristiani & Kieu Lan Phuong Nguyen & Chao-Tung Yang, 2022. "The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA," IJERPH, MDPI, vol. 19(3), pages 1-17, February.
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