Skip to main content
SpringerLink
Log in

A Review of Computational Intelligence Technologies for Tackling Covid-19 Pandemic

  • Chapter
  • First Online:
The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care

Part of the book series: Internet of Things ((ITTCC))

Abstract

In March 2020, the World Health Organization (WHO) declared COVID-19 as a pandemic that covers around 185 countries and territories in the world where the coronavirus infirmity is present. The COVID-19 epidemic is dispersing all over the world in a few months. The traditional health care systems face new challenges associated with the constant increase of patients with this disease. This epidemic has caused a mess worldwide. In India, cases are increased day by day. Due to COVID-19, countries have a huge loss which cannot be estimated both in the economy and life of citizens. To recover this economic loss and save the life, deployment of emerging technologies is used to battle this invisible enemy. During this period, several researchers have written lots of research papers in various fields. The main aim of this chapter is to summarize the existing literature addressing the use of computational intelligence (CI) technologies to battle COVID-19 infection. Nowadays researchers have been analyzed the data related to COVID-19 and draw some conclusions by applying emerging technologies like AI, IoT, deep learning, Blockchain, Neural Network, Fuzzy, and machine learning algorithms. These strategies help policymakers and frontline people to take additional steps by avoiding the unfold of the virus and manage the disease. Researchers also suggest the use of Artificial Intelligence (AI) and the Internet of Things (IoT) to fight this pandemic and do all necessary work by following the guidelines given by the government.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons and Fractals, 139. https://doi.org/10.1016/j.chaos.2020.110059.

  2. Montemurro, N. (2020). The emotional impact of COVID-19: From medical staff to common people. Brain, Behavior, and Immunity, 1591, 1–2. https://doi.org/10.1016/j.bbi.2020.03.032

    Article  Google Scholar 

  3. Haleem, A., Javaid, M., Vaishya, R., & Deshmukh, S. G. (2020). Areas of academic research with the impact of COVID-19. The American Journal of Emergency Medicine, 38, 1524–1526. https://doi.org/10.1016/j.ajem.2020.04.022

    Article  Google Scholar 

  4. Shaw, R., Kim, Y., & Hua, J. (2020). Governance, technology and citizen behavior in pandemic: Lessons from COVID-19 in East Asia. Progress in Disaster Science, 6, 100090. https://doi.org/10.1016/j.pdisas.2020.100090

    Article  Google Scholar 

  5. Manjunath, B. S. (2020, April). Covid-19: 8 ways in which technology helps pandemic management. IT News, ET CIO.

    Google Scholar 

  6. Kumar, A., Gupta, P. K., & Srivastava, A. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research & Reviews, 14(4), 569–573. https://doi.org/10.1016/j.dsx.2020.05.008

    Article  Google Scholar 

  7. Startus Insight. (2020). 8 ways emerging technologies tackle the global coronavirus pandemic.

    Google Scholar 

  8. Ransing, R., Nagendrappa, S., Patil, A., Shoib, S., & Sarkar, D. (2020). Potential role of artificial intelligence to address the COVID-19 outbreak-related mental health issues in India. Psychiatry Research, 290, 113176. https://doi.org/10.1016/j.psychres.2020.113176

    Article  Google Scholar 

  9. Sipior, J. C. (2020). Considerations for development and use of AI in response to COVID-19. International Journal of Information Management, 55, 102170. https://doi.org/10.1016/j.ijinfomgt.2020.102170

    Article  Google Scholar 

  10. Zhang, K., et al. (2020). Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell, 181(6), 1423–1433.e11. https://doi.org/10.1016/j.cell.2020.04.045

    Article  Google Scholar 

  11. Mohanty, S., Harun, M., Rashid, A. I., Mridul, M., Mohanty, C., & Swayamsiddha, S. (2020). Application of artificial intelligence in COVID-19 drug repurposing. Diabetes and Metabolic Syndrome: Clinical Research & Reviews, 14(5), 1027–1031. https://doi.org/10.1016/j.dsx.2020.06.068

    Article  Google Scholar 

  12. Albahri, O. S., et al. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13, 1381–1396. https://doi.org/10.1016/j.jiph.2020.06.028

    Article  Google Scholar 

  13. Park, Y., Casey, D., Joshi, I., Zhu, J., & Cheng, F. (2020). Emergence of new disease: How can artificial intelligence help? Trends in Molecular Medicine, 26(7), 6–8. https://doi.org/10.1016/j.molmed.2020.04.007

    Article  Google Scholar 

  14. Ahuja, A. S., Reddy, V. P., & Marques, O. (2020). Artificial intelligence and COVID-19: A multidisciplinary approach. Integrative Medicine Research, 9(3), 100434. https://doi.org/10.1016/j.imr.2020.100434

    Article  Google Scholar 

  15. Ke, Y. Y., et al. (2020). Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomedical Journal, 43(4), 355–362. https://doi.org/10.1016/j.bj.2020.05.001

    Article  Google Scholar 

  16. Imran, A., et al. (2020). AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked, 20, 100378. https://doi.org/10.1016/j.imu.2020.100378

    Article  Google Scholar 

  17. Wang, B., Yang, Z., Xuan, J., & Jiao, K. (2020). Crises and opportunities in terms of energy and AI technologies during the COVID-19 pandemic. Energy AI, 1, 100013. https://doi.org/10.1016/j.egyai.2020.100013

    Article  Google Scholar 

  18. Shi, F., et al. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 1–13. https://doi.org/10.1109/RBME.2020.2987975

  19. Jamshidi, M., et al. (2020). Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment. IEEE Access, 8, 109581–109595. https://doi.org/10.1109/ACCESS.2020.3001973

    Article  Google Scholar 

  20. Mei, X., et al. (2020). Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature Medicine, 26(8), 1224–1228. https://doi.org/10.1038/s41591-020-0931-3

    Article  Google Scholar 

  21. Salman, F. M., Abu-Naser, S. S., Alajrami, E., Abu-Nasser, B. S., & Ashqar, B. A. M. (2020). COVID-19 detection using artificial intelligence. International Journal of Computer Engineering Research, 4(3), 18–25.

    Google Scholar 

  22. Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337–339. https://doi.org/10.1016/j.dsx.2020.04.012

    Article  Google Scholar 

  23. Maghdid, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., & Rabie, K. (2020). A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: Design study (pp. 1–7).

    Google Scholar 

  24. Nguyen, T. T., Waurn, G., & Campus, P. (2020). Artificial intelligence in the battle against coronavirus ( COVID-19 ): A survey and future research directions. Researchgate.Net (pp. 1–13). https://doi.org/10.13140/RG.2.2.36491.23846.Artificial

  25. Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020). A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. IEEE Access, 8, 90225–90265. https://doi.org/10.1109/ACCESS.2020.2992341

    Article  Google Scholar 

  26. Sohrabi, C., Alsafi, Z., Neill, N. O., Khan, M., & Kerwan, A. (2020, January). Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19. The COVID-19 resource centre is hosted on Elsevier Connect, the company’s public news and information.

    Google Scholar 

  27. Barragán, D., & Manero, J. (2020). How big data and artificial intelligence can help against COVID-19 (pp. 4–11). IE Business School.

    Google Scholar 

  28. Pham, Q. V., Nguyen, D. C., Huynh-The, T., Hwang, W. J., & Pathirana, P. N. (2020). Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: A survey on the state-of-the-arts. IEEE Access, 8, 130820–130839. https://doi.org/10.1109/ACCESS.2020.3009328

    Article  Google Scholar 

  29. Piccialli, F., Di, V., Giampaolo, F., & Cuomo, S. (2021). A survey on deep learning in medicine: Why, how and when? Information Fusion, 66, 111–137.

    Article  Google Scholar 

  30. Wong, K. K. L., Fortino, G., & Abbott, D. (2020). Deep learning-based cardiovascular image diagnosis: A promising challenge. Future Generation Computer Systems, 110, 802–811. https://doi.org/10.1016/j.future.2019.09.047

    Article  Google Scholar 

  31. Amini, A., Chen, W., Fortino, G., Li, Y., Pan, Y., & Wang, M. D. (2020). Editorial: Special issue on ‘AI-driven informatics, sensing, imaging and big data analytics for fighting the COVID-19 pandemic. IEEE Journal of Biomedical and Health Informatics, 24(10), 2731–2732. https://doi.org/10.1109/JBHI.2020.3025594

    Article  Google Scholar 

  32. Singh, R. P., Javaid, M., Haleem, A., & Suman, R. (2020). Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research & Reviews, 14(4), 521–524. https://doi.org/10.1016/j.dsx.2020.04.041

    Article  Google Scholar 

  33. Rahman, A., Hossain, M. S., Alrajeh, N. A., & Alsolami, F. (2020). Adversarial examples – Security threats to COVID-19 deep learning Systems in Medical IoT devices. IEEE Internet of Things Journal, 1–1. https://doi.org/10.1109/jiot.2020.3013710

  34. Oyeniyi, J., Ogundoyin, I., & Oyeniran, O. (2020, June). Application of internet of things (IoT) to enhance the fight against covid-19 application of internet of things (IoT) to enhance the fight against covid-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14, 521–524.

    Article  Google Scholar 

  35. Alam, T. (2020). Coronavirus disease (Covid-19): Reviews, applications, and current status. SSRN Coronavirus & Infectious Disease Research eJournal. https://doi.org/10.2139/ssrn.3660497.

  36. Yang, T., Gentile, M., Shen, C. F., & Cheng, C. M. (2020). Combining point-of-care diagnostics and internet of medical things (IOMT) to combat the Covid-19 pandemic. Diagnostics, 10(4), 4–6. https://doi.org/10.3390/diagnostics10040224

    Article  Google Scholar 

  37. Rahman, M. S., Peeri, N. C., Shrestha, N., Zaki, R., Haque, U., & Hamid, S. H. A. (2020). Defending against the novel coronavirus (COVID-19) outbreak: How can the internet of things (IoT) help to save the world? Health Policy Technology, 9, 136–138. https://doi.org/10.1016/j.hlpt.2020.04.005

    Article  Google Scholar 

  38. Swayamsiddha, S., & Mohanty, C. (2020). Application of cognitive internet of medical things for COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research & Reviews, 14(5), 911–915. https://doi.org/10.1016/j.dsx.2020.06.014

    Article  Google Scholar 

  39. Pratap, R., Javaid, M., Haleem, A., Vaishya, R., & Ali, S. (2020). Internet of medical things ( IoMT ) for orthopaedic in COVID-19 pandemic: Roles , challenges , and applications. Journal of Clinical Orthopaedics and Trauma, 11(4), 713–717. https://doi.org/10.1016/j.jcot.2020.05.011

    Article  Google Scholar 

  40. Kamal, M., Aljohani, A., & Alanazi, E. (2020). IoT meets COVID-19: Status, challenges, and opportunities.

    Google Scholar 

  41. Tsikala Vafea, M., et al. (2020). Emerging technologies for use in the study, diagnosis, and treatment of patients with COVID-19. Cellular and Molecular Bioengineering, 13, 249–257. https://doi.org/10.1007/s12195-020-00629-w

    Article  Google Scholar 

  42. Mohammed, M. N., Syamsudin, H., Al-Zubaidi, S., Sairah, A. K., Ramli, R., & Yusuf, E. (2020). Novel covid-19 detection and diagnosis system using iot-based smart helmet. International Journal of Psychosocial Rehabilitation, 24(7), 2296–2303. https://doi.org/10.37200/IJPR/V24I7/PR270221

    Article  Google Scholar 

  43. Kumar, M. S., Raut, D. R. D., Narwane, D. V. S., & Narkhede, D. B. E. (2020). Applications of industry 4.0 to overcome the COVID-19 operational challenges. Diabetes and Metabolic Syndrome: Clinical Research & Reviews, 14(5), 1283–1289. https://doi.org/10.1016/j.dsx.2020.07.010

    Article  Google Scholar 

  44. Otoom, M., Otoum, N., Alzubaidi, M. A., Etoom, Y., & Banihani, R. (2020). An IoT-based framework for early identification and monitoring of COVID-19 cases. Biomedical Signal Processing and Control, 62, 102149. https://doi.org/10.1016/j.bspc.2020.102149

    Article  Google Scholar 

  45. Nasajpour, M., Pouriyeh, S., Parizi, R. M., Dorodchi, M., Valero, M., & Arabnia, H. R. (2020). Internet of things for current COVID-19 and future pandemics: An exploratory study. Journal of Healthcare Informatics Research, 4, 325–364.

    Article  Google Scholar 

  46. Prabhu, J., et al. (2020). IoT role in prevention of COVID-19 and health care workforces behavioural intention in India - an empirical examination. International Journal of Pervasive Computing and Communications, 16(4), 331–340. https://doi.org/10.1108/IJPCC-06-2020-0056

    Article  Google Scholar 

  47. Haleem, A., Javaid, M., Khan, I. H., & Vaishya, R. (2020). Significant applications of big data in COVID-19 pandemic. The Indian Journal of Orthopaedics, 54(4), 526–528. https://doi.org/10.1007/s43465-020-00129-z

    Article  Google Scholar 

  48. Vaishya, R., Haleem, A., Vaish, A., & Javaid, M. (2020). Emerging technologies to combat the COVID-19 pandemic. Journal of Clinical and Experimental Hepatology, 10(4), 409–411. https://doi.org/10.1016/j.jceh.2020.04.019

    Article  Google Scholar 

  49. Chang, M. C., & Park, D. (2020). How can blockchain help people in the event of pandemics such as the COVID-19? Journal of Medical Systems, 44(5), 102. https://doi.org/10.1007/s10916-020-01577-8

    Article  Google Scholar 

  50. Mashamba-Thompson, T. P., & Crayton, E. D. (2020). Blockchain and artificial intelligence technology for novel coronavirus disease-19 self-testing. Diagnostics, 10(4), 8–11. https://doi.org/10.3390/diagnostics10040198

    Article  Google Scholar 

  51. Kumar, R., et al. (2020). Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging. 14(8), 1–12.

    Google Scholar 

  52. Bansal, A., Garg, C., & Padappayil, R. P. (2020). Optimizing the implementation of COVID-19 ‘immunity certificates’ using Blockchain. Journal of Medical Systems, 44(9), 19–20. https://doi.org/10.1007/s10916-020-01616-4

    Article  Google Scholar 

  53. Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A.. (2020). Blockchain and AI-based solutions to combat coronavirus (COVID-19)-like epidemics: A survey (pp. 1–15).

    Google Scholar 

  54. Song, J., Gu, T., Feng, X., Ge, Y., & Mohapatra, P. (2020). Blockchain meets COVID-19: A framework for contact information sharing and risk notification system.

    Google Scholar 

  55. Kalla, A., Hewa, T., Mishra, R. A., Ylianttila, M., & Liyanage, M. (2020). The role of blockchain to fight against COVID-19. IEEE Engineering Management Review, 48, 85–96. https://doi.org/10.1109/EMR.2020.3014052

    Article  Google Scholar 

  56. Alam, T. (2020). Internet of things and blockchain-based framework for coronavirus (COVID-19) disease. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3660503.

  57. Dai, H.-N., Imran, M., & Haider, N. (2020). Blockchain-enabled internet of medical things to combat COVID-19. IEEE Internet of Things Magazine, 3, 52–57.

    Article  Google Scholar 

  58. Xu, H., Zhang, L., Onireti, O., Fang. Y., Buchanan, W. B., & Imran, M. A. (2020). BeepTrace: Blockchain-enabled privacy-preserving contact tracing for COVID-19 pandemic and beyond (pp. 1–13). https://doi.org/10.13140/RG.2.2.25101.15849/1.

  59. Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419–422. https://doi.org/10.1016/j.dsx.2020.04.032

    Article  Google Scholar 

  60. Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. C. (2020). Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digital Health, 2(8), e435–e440. https://doi.org/10.1016/S2589-7500(20)30142-4

    Article  Google Scholar 

  61. Kumar, P. (2020, June). Literature based study on cloud computing for health and sustainability in view of covid19. Core.Ac.Uk.

  62. Ye, J. (2020). The role of health technology and informatics in global public health emergency: Practices and implications from the COVID-19 pandemic (preprint). JMIR Medical Informatics, 8, e19866. https://doi.org/10.2196/19866

    Article  Google Scholar 

  63. Vidal-Alaball, J., et al. (2020). Telemedicine in the face of the COVID-19 pandemic. Atencion Primaria, 52(6), 418–422. https://doi.org/10.1016/j.aprim.2020.04.003

    Article  Google Scholar 

  64. Iyengar, K., Upadhyaya, G. K., Vaishya, R., & Jain, V. (2020). COVID-19 and applications of smartphone technology in the current pandemic. Diabetes and Metabolic Syndrome: Clinical Research & Reviews, 14(5), 733–737. https://doi.org/10.1016/j.dsx.2020.05.033

    Article  Google Scholar 

  65. Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of Machine Learning and Artificial Intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons Fractals, 139, 110059. https://doi.org/10.1016/j.chaos.2020.110059

    Article  MathSciNet  Google Scholar 

  66. Chowdhury, M. A., Shah, Q. Z., Kashem, M. A., Shahid, A., & Akhtar, N. (2020). Evaluation of the effect of environmental parameters on the spread of COVID-19: A fuzzy logic approach. Advances in Fuzzy Systems, 2020.

    Google Scholar 

  67. Wang, L., Lin, Z. Q., & Wong, A. (2020). COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. Scientific Reports, 1–12.

    Google Scholar 

  68. Khan, A. I., Shah, J. L., & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 196, 105581. https://doi.org/10.1016/j.cmpb.2020.105581

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Maharaja Surajmal Institute, New Delhi, India

    Anamika Rana

  2. Institute of Innovation in Technology and Management, New Delhi, India

    Sushma Malik

Authors
  1. Anamika Rana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sushma Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  2. Vardhaman College of Engineering, Hyderabad, Telangana, India

    M.A. Jabbar

  3. Vardhaman College of Engineering, Hyderabad, Telangana, India

    Rajanikanth Aluvalu

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  5. School of Engineering, Instituto Superior de Engenharia, Porto, Portugal

    Ana Madureira

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rana, A., Malik, S. (2021). A Review of Computational Intelligence Technologies for Tackling Covid-19 Pandemic. In: Siarry, P., Jabbar, M., Aluvalu, R., Abraham, A., Madureira, A. (eds) The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-75220-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-75220-0_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75219-4

  • Online ISBN: 978-3-030-75220-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation