research-article

Investigating the Spread of Coronavirus Disease via Edge-AI and Air Pollution Correlation

Authors:

K. Janarthanan,

Fadi Al-Turjman,

T. Priya ReshmaAuthors Info & Claims

ACM Transactions on Internet Technology, Volume 21, Issue 4

Article No.: 105, Pages 1 - 10

https://doi.org/10.1145/3424222

Published: 22 July 2021 Publication History

Abstract

Coronavirus Disease 19 (COVID-19) is a highly infectious viral disease affecting millions of people worldwide in 2020. Several studies have shown that COVID-19 results in a severe acute respiratory syndrome and may lead to death. In past research, a greater number of respiratory diseases has been caused by exposure to air pollution for long periods of time. This article investigates the spread of COVID-19 as a result of air pollution by applying linear regression in machine learning method based edge computing. The analysis in this investigation have been based on the death rates caused by COVID-19 as well as the region of death rates based on hazardous air pollution using data retrieved from the Copernicus Sentinel-5P satellite. The results obtained in the investigation prove that the mortality rate due to the spread of COVID-19 is 77% higher in areas with polluted air. This investigation also proves that COVID-19 severely affected 68% of the individuals who had been exposed to polluted air.

References

[1]

T. K. Burki. 2020. Coronavirus in China. Lancet Respiratory Medicine 8, 3 (2020), 238.

[2]

W. B. Yu, G. D. Tang, L. Zhang, and R. T. Corlett. 2020. Decoding the evolution and transmissions of the novel pneumonia coronavirus (SARS-CoV-2) using whole genomic data. Zoological Research 41, 3 (2020), 247–257.

[3]

J. F. W. Chan, S. Yuan, K. H. Kok, K. K. W. To, H. Chu, J. Yang, F. Xing, et al. 2020. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet 395, 10223 (2020), 514–523.

[4]

W. Liu, Z. W. Tao, L. Wang, M. L. Yuan, K. Liu, L. Zhou, S. Wei, et al. 2020. Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. Chinese Medical Journal 133, 9 (2020), 1032–1038.

[5]

M. Goumenou, D. Sarigiannis, A. Tsatsakis, O. Anesti, A. O. Docea, D. Petrakis, D. Tsoukalas, et al. 2020. COVID‑19 in northern Italy: An integrative overview of factors possibly influencing the sharp increase of the outbreak. Molecular Medicine Reports 22, 1 (2020), 20–32.

[6]

P. Mehta, D. F. McAuley, M. Brown, E. Sanchez, R. S. Tattersall, and J. J. Manson. 2020. COVID-19: Consider cytokine storm syndromes and immunosuppression. Lancet 395, 10229 (2020), 1033–1034.

[7]

L. Chen, J. Xiong, L. Bao, and Y. Shi. 2020. Convalescent plasma as a potential therapy for COVID-19. Lancet Infectious Diseases 20, 4 (2020), 398–400.

[8]

P. Colson, P. E. Fournier, S. Amrane, M. Hocquart, M. Mailhe, V. Esteves-Vieira, B. Doudier et al. 2020. Early treatment of 1061 COVID-19 patients with hydroxychloroquine and azithromycin. Travel Medicine and Infectious Disease 35 (2020), 101738.

[9]

R. L. Persinger, M. E. Poynter, K. Ckless, and Y. M. Janssen-Heininger. 2002. Molecular mechanisms of nitrogen dioxide induced epithelial injury in the lung. Molecular and Cellular Biochemistry 234, 1 (2002), 71–80.

[10]

C. A. Pope III, R. T. Burnett, G. D. Thurston, M. J. Thun, E. E. Calle, D. Krewski, and J. J. Godleski. 2004. Cardiovascular mortality and long-term exposure to particulate air pollution: Epidemiological evidence of general pathophysiological pathways of disease. Circulation 109, 1 (2004), 71–77.

[11]

F. Zhou, T. Yu, R. Du, G. Fan, Y. Liu, Z. Liu, J. Xiang, Y. Wang, B. Song, X. Gu, and L. Guan. 2020. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 395, 10229 (2020), 1054–1062.

[12]

W. Alhazzani, M. H. Møller, Y. M. Arabi, M. Loeb, M. N. Gong, E. Fan, S. Oczkowski, et al. 2020. Surviving Sepsis Campaign: Guidelines on the management of critically ill adults with coronavirus disease 2019 (COVID-19). Intensive Care Medicine (2020), 1–34.

[13]

A. Darwish, A. E. Hassanien, M. Elhoseny, A. K. Sangaiah, and K. Muhammad. 2019. The impact of the hybrid platform of Internet of Things and cloud computing on healthcare systems: Opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing 10, 10 (2019), 4151–4166.

[14]

F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, K. He, Y. Shi, and D. Shen. 2020. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering 14 (2020), 4–15.

[15]

C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, et al. 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 10223 (2020), 497–506.

[16]

S. Patiyal, D. Kaur, H. Kaur, N. Sharma, A. Dhall, S. Sahai, P. Agrawal, L. Maryam, C. Arora, and G. P. S. Raghava. 2020. A web-based platform on COVID-19 to maintain predicted diagnostic, drug and vaccine candidates. Monoclonal Antibodies and Immunodiagnosis and Immunotherapy 39, 6 (2020), 204–216.

[17]

S. Tuli, S. Tuli, R. Tuli, and S. S. Gill. 2020. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things 11 (2020), 100222.

[18]

M. S. Hossain and G. Muhammad. 2019. Emotion recognition using secure edge and cloud computing. Information Sciences 504 (2019), 589–601.

Digital Library

[19]

Y. Hao, Y. Miao, L. Hu, M. S. Hossain, G. Muhammad, and S. U. Amin. 2019. Smart-Edge-CoCaCo: AI-enabled smart edge with joint computation, caching, and communication in heterogeneous IoT. IEEE Network 33, 2 (2019), 58–64.

[20]

M. S. Hossain, G. Muhammad, and N. Guizani. 2020. Explainable AI and mass surveillance system-based healthcare framework to combat COVID-I9 like pandemics. IEEE Network 34, 4 (2020), 126–132.

[21]

M. A. Rahman. 2020. Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices. Sustainable Cities and Society 62 (2020), 102372.

[22]

S. Serte, A. Serener, and F. Al-Turjman. 2020. Deep learning in medical imaging: A brief review. Wiley Transactions on Emerging Telecommunications Technologies. E-pub ahead of print, 2020.

[23]

L. Greco, G. Percannella, P. Ritrovato, F. Tortorella, and M. Vento. 2020. Trends in IoT based solutions for health care: Moving AI to the edge. Pattern Recognition Letters 135, 1 (2020), 146–153.

[24]

C. Y. Lin, C. M. Wang, M. L. Chen, and B. F. Hwang. 2019. The effects of exposure to air pollution on the development of uterine fibroids. International Journal of Hygiene and Environmental Health 222, 3 (2019), 549–555.

[25]

M. Ciesielczyk, A. Szwabe, M. Morzy, and P. Misiorek. 2017. Progressive random indexing: Dimensionality reduction preserving local network dependencies. ACM Transactions on Internet Technology 17, 2 (2017), 1–21.

Digital Library

[26]

S. K. Unnithan and L. Gnanappazham. 2020. Estimation of PM2.5 from MODIS aerosol optical depth over the Indian subcontinent. In Applications of Geomatics in Civil Engineering. Lecture Notes in Computer Science, Vol. 33. Springer, 249–262.

[27]

L. Sogacheva, T. Popp, A. M. Sayer, O. Dubovik, M. J. Garay, A. Heckel, N. C. Hsu, et al. 2020. Merging regional and global aerosol optical depth records from major available satellite products. Atmospheric Chemistry and Physics 20, 4 (2020), 2031–2056.

[28]

X. Chen, J. Ding, J. Wang, X. Ge, M. Raxidin, J. Liang, X. Chen, Z. Zhang, X. Cao, and Y. Ding. 2020. Retrieval of fine-resolution aerosol optical depth (AOD) in semiarid urban areas using Landsat data: A case study in Urumqi, NW China. Remote Sensing 12, 3 (2020), 467.

Cited By

Chu LNelen JCrivellari AMasiliūnas DHein CLofi C(2024)Relationships between geo-spatial features and COVID-19 hospitalisations revealed by machine learning models and SHAP valuesInternational Journal of Digital Earth10.1080/17538947.2024.235885117:1Online publication date: 27-May-2024
https://doi.org/10.1080/17538947.2024.2358851
B DV PR MM SB.V BA C(2024)Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networksMultimedia Tools and Applications10.1007/s11042-024-18812-wOnline publication date: 21-Mar-2024
https://doi.org/10.1007/s11042-024-18812-w
CAN AÖZSOY H(2023)A Different Perspective on Air Pollution MeasurementsHava Kirliliği Ölçümlerine Farklı Bir BakışPoliteknik Dergisi10.2339/politeknik.112658026:1(329-344)Online publication date: 27-Mar-2023
https://doi.org/10.2339/politeknik.1126580
Show More Cited By

Index Terms

Investigating the Spread of Coronavirus Disease via Edge-AI and Air Pollution Correlation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Rule learning

Recommendations

Air pollution in the area around the mining complex Trepça in Kosovo
EE'10: Proceedings of the 5th IASME/WSEAS international conference on Energy & environment

In this paper are presented the results of air pollution in Mining Complex Trepça in Kosovo. The region is significantly affected by industrial pollution which is affected by the mining sector. Associated to the region there is effluents by toxic/acidic ...
Evidence of the correlation between a city’s air pollution and human health through soft computing
Abstract
Huge quantities of pollutants are released into the atmosphere of many cities every day. These emissions, due to physicochemical conditions, can interact with each other, resulting in additional pollutants such as ozone. The resulting accumulation ...
Study of the evolution of air pollution in Salamanca (Spain) along a five-year period (1994-1998) using HJ-Biplot simultaneous representation analysis

Five-year data of SO"2, PM10, NO, NO"2, O"3 and CO levels recorded at two air pollution monitoring stations in the city of Salamanca were analysed using a HJ-Biplot Simultaneous Representation analysis method in order to determine the possible ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 4

November 2021

520 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3472282

Editor:
Ling Lu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 July 2021

Accepted: 01 September 2020

Revised: 01 September 2020

Received: 01 July 2020

Published in TOIT Volume 21, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
209
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)2

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chu LNelen JCrivellari AMasiliūnas DHein CLofi C(2024)Relationships between geo-spatial features and COVID-19 hospitalisations revealed by machine learning models and SHAP valuesInternational Journal of Digital Earth10.1080/17538947.2024.235885117:1Online publication date: 27-May-2024
https://doi.org/10.1080/17538947.2024.2358851
B DV PR MM SB.V BA C(2024)Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networksMultimedia Tools and Applications10.1007/s11042-024-18812-wOnline publication date: 21-Mar-2024
https://doi.org/10.1007/s11042-024-18812-w
CAN AÖZSOY H(2023)A Different Perspective on Air Pollution MeasurementsHava Kirliliği Ölçümlerine Farklı Bir BakışPoliteknik Dergisi10.2339/politeknik.112658026:1(329-344)Online publication date: 27-Mar-2023
https://doi.org/10.2339/politeknik.1126580
Dashora RBabu M(2023)A survey on state-of-the-art computing for cyber-physical systems2ND INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMPUTATIONAL TECHNIQUES10.1063/5.0150080(020001)Online publication date: 2023
https://doi.org/10.1063/5.0150080
Gupta SNaidu PKuppan VAlagumeenaakshi MNiruban RSwaminathan J(2022)Analysis of Various Toxic Gas Levels Using 5G ML-IoT for Air Quality Monitoring and ForecastingIOT with Smart Systems10.1007/978-981-19-3575-6_75(789-802)Online publication date: 6-Oct-2022
https://doi.org/10.1007/978-981-19-3575-6_75
Thamizharasan SNaidu PVasuja Devi MCastelo LKovendan ASwaminathan J(2022)Design of Smart Spectacle in 5G-IoT Environment to Detect and Prevent Corona Virus VariantsICT with Intelligent Applications10.1007/978-981-19-3571-8_71(775-783)Online publication date: 1-Oct-2022
https://doi.org/10.1007/978-981-19-3571-8_71
Hasan MHamed TAl-Turjman F(2021)Particle Swarm Optimization for Adaptive Social-distance of Neighborhood in the IoT and COVID-19 Era2021 International Conference on Artificial Intelligence of Things (ICAIoT)10.1109/ICAIoT53762.2021.00009(7-14)Online publication date: Sep-2021
https://doi.org/10.1109/ICAIoT53762.2021.00009
Anser MGodil DKhan MNassani AAskar SZaman KSalamun HSasmoko Indrianti YAbro M(2021)Nonlinearity in the relationship between COVID-19 cases and carbon damages: controlling financial development, green energy, and R&D expenditures for shared prosperityEnvironmental Science and Pollution Research10.1007/s11356-021-15978-w29:4(5648-5660)Online publication date: 23-Aug-2021
https://doi.org/10.1007/s11356-021-15978-w

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Issue’s Table of Contents