Skip to main content
SpringerLink
Log in
Book cover

International Summit Smart City 360°

SmartCity360° 2020: Science and Technologies for Smart Cities pp 3–19Cite as

IoT and AI for COVID-19 in Scalable Smart Cities

  • Conference paper
  • First Online:

Abstract

COVID-19 which is also known as the novel coronavirus started from China. Motivated by continuous advancement and employments of the Artificial Intelligence (AI) and IoT in various regions, in this study we focus on their underlining deployment in responding to the virus. In this survey, we sum up the current region of AI applications in clinical associations while battling COVID-19. We moreover survey the component, challenges, and issues identified with these technologies. A review was made in requesting AI and IoT by then recognizing their applications in engaging the COVID-19. In like manner, emphasis has been made on a region that utilizes cloud computing in combating diverse similar diseases and the COVID-19 itself. The investigated procedures set forth drives clinical information examination with an exactness of up to 95%. We further end up with a point by point discussion about how AI utilization can be in an ideal situation in battling diverse diseases. This paper gives masters and specialists new bits of information in which AI and IoT can be utilized in improving the COVID-19 situation, and drive further assessments in ending the flare-up of the infection.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. “Situation update worldwide, as of 9 April 2020,” (2020). https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases

  2. “Coronavirus disease (COVID-19) pandemic,” (2020). https://www.who.int/emergencies/diseases/novel-coronavirus-2019

  3. “Coronavirus (COVID-19),” (2020). https://www.cdc.gov/coronavirus/2019-nCoV/index.html

  4. “White House announces new partnership to unleash U.S. supercomputing resources to fight COVID-19,” (2020). https://www.whitehouse.gov/briefings-statements

  5. “arXiv announces new COVID-19 quick search,” (2020). https://blogs.cornell.edu/arxiv/2020/03/30/new-covid-19-quick-search/

  6. Sohrabi, C., et al.: World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 76, 71–76 (2020)

    Article  Google Scholar 

  7. Roser, M., Ritchie, H., Ortiz-Ospina, E., Hasell, J.: Coronavirus (COVID-19) Cases. (2020). https://ourworldindata.org/covid-cases

  8. Fang, L., Karakiulakis, G., Roth, M.: Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? Lancet. Respir. Med. 8(4), e21 (2020)

    Article  Google Scholar 

  9. Wong, S.H., Lui, R.N., Sung, J.J.: Covid-19 and the digestive system. J. Gastroenterol. Hepatol. 35(5), 744−748 (2020)

    Google Scholar 

  10. Baldwin, R., Tomiura, E.: Thinking ahead about the trade impact of COVID-19. In: Economics in the Time COVID-19, p. 59 (2020)

    Google Scholar 

  11. Surveillances, V.: The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) China, 2020. China CDC Weekly 2(8), 113–122 (2020)

    Article  Google Scholar 

  12. Chen, H., et al.: Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records. Lancet 395(10226), 809–815 (2020)

    Article  Google Scholar 

  13. Wang, D., et al.: Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. J. Amer. Med. Assoc. 323(11), 1061 (2020)

    Article  Google Scholar 

  14. Chen, N., et al.: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223), 507–513 (2020)

    Article  Google Scholar 

  15. Jiang, F., Deng, L., Zhang, L., Cai, Y., Cheung, C., Xia, Z.: Review of the clinical characteristics of coronavirus disease 2019 (COVID-19). J. Gen. Intern. Med. 35(5), 1545–1549 (2020). https://doi.org/10.1007/s11606-020-05762-w

    Article  Google Scholar 

  16. Salehi, S., Abedi, A., Balakrishnan, S., Gholamrezanezhad, A.: Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. Am. J. Roentgenol. 215(1), 87–93 (2020). https://doi.org/10.2214/AJR.20.23034

    Article  Google Scholar 

  17. Singhal, T.: A review of coronavirus disease-2019 (COVID-19). Indian J. Pediatrics 87(4), 281–286 (2020)

    Article  Google Scholar 

  18. World Health Organisation (WHO): Novel coronavirus (2019-nCoV). Situation report-SS. (2020). https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200315-sitrep-55-covid-19.pdf?sfvrsn=33daa5cb_6

  19. Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT press (2016)

    Google Scholar 

  20. Beck, B.R., Shin, B., Choi, Y., Park, S., Kang, K.: Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model. Comput. Struct. Biotechnol. J. 18, 784−790 (2020)

    Google Scholar 

  21. Zhavoronkov, A., et al.: Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches. ChemRxi (2020)

    Google Scholar 

  22. Zheng, C., et al.: Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv (2020)

    Google Scholar 

  23. Hu, Z., Ge, Q., Li, S., Jin, L., Xiong, M.: Artificial intelligence forecasting of COVID-19 in China. arXiv preprint arXiv:2002.07112 (2020)

  24. COVID-19 open research dataset challenge (CORD-19): An AI challenge with AI2, CZI, MSR, Georgetown, NIH & The White House. (2020). www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge

  25. IBM releases novel AI-powered technologies to help health and research community accelerate the discovery of medical insights and treatments for COVID-19. (2020). https://www.ibm.com/blogs/research/2020/04/ai-powered-technologies-accelerate-discovery-covid-19/

  26. Mamoshina, P., Vieira, A., Putin, E., Zhavoronkov, A.: Applications of deep learning in biomedicine. Mol. Pharm. 13(5), 1445–1454 (2016)

    Article  Google Scholar 

  27. Cao, C., et al.: Deep learning and its applications in biomedicine. Genomics Proteomics Bioinform. 16(1), 17–32 (2018)

    Article  Google Scholar 

  28. Ekins, S., et al.: Exploiting machine learning for end-to-end drug discovery and development. Nature Mater. 18(5), 435 (2019)

    Article  Google Scholar 

  29. Hu, F., Jiang, J., Yin, P.: Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model. arXiv preprint arXiv:2003.00728 (2020)

  30. Ge, Y., et al.: A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. BioRxiv (2020)

    Google Scholar 

  31. Savioli, N.: One-shot screening of potential peptide ligands on HR1 domain in COVID-19 glycosylated spike (S) protein with deep Siamese network. arXiv preprint arXiv:2004.02136 (2020)

  32. Ton, A.T., Gentile, F., Hsing, M., Ban, F., Cherkasov, A.: Rapid identification of potential inhibitors of sars-cov-2 main protease by deep docking of 1.3 billion compounds. Molecular Informatics 39(8), 2000028 (2020)

    Google Scholar 

  33. Hofmarcher, M., et al.: Large-scale ligand-based virtual screening for SARS- CoV-2 inhibitors using deep neural networks. SSRN 3561442 (2020)

    Google Scholar 

  34. Ong, E., Wong, M.U., Huffman, A., He, Y.: COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. BioRxiv (2020)

    Google Scholar 

  35. Jumper, J., Tunyasuvunakool, K., Kohli, P., Hassabis, D., Team, A.: Computational predictions of protein structures associated with COVID-19. DeepMind (2020)

    Google Scholar 

  36. Senior, A.W., et al.: Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706−710 (2020)

    Google Scholar 

  37. Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., Kim, P.M.: Fast and flexible design of novel proteins using graph neural networks. BioRxiv (2020)

    Google Scholar 

  38. Chenthamarakshan, V., et al.: Target-specific and selective drug design for COVID-19 using deep generative models. arXiv preprint arXiv:2004.01215 (2020)

  39. Corman, V.M., et al.: novel coronavirus (2019-nCoV) by real-time RTPCR. Eurosurveillance 25(3), 2020 (2019)

    Google Scholar 

  40. Fomsgaard, A.S., Rosenstierne, M.W.: An alternative workflow for molecular detection of SARS-CoV-2-escape from the NA extraction kit-shortage. medRxiv (2020)

    Google Scholar 

  41. Maghded, H.S., Ghafoor, K.Z., Sadiq, A.S., Curran, K., Rabie, K.: A novel AI-enabled framework to diagnose coronavirus COVID-19 using smartphone embedded sensors: design study. arXiv preprint arXiv:2003.07434 (2020)

  42. Rao, A.S.S., Vazquez, J.A.: Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey in the populations when cities/towns are under quarantine. Infect. Control Hosp. Epidemiol. 41(7), 826-830 (2020)

    Google Scholar 

  43. Silva, B.M., Rodrigues, J.J., de la Torre Díez, I., López-Coronado, M., Saleem, K.: Mobile-health: a review of current state in 2015. J. Biomed. Inform. 56, 265–272 (2015)

    Google Scholar 

  44. Pham, Q.-V., et al.: A survey of multi-access edge computing in 5G and beyond: fundamentals, technology integration, and state-of-the-art. CoRR arxiv.org/abs/1906.08452 (2019)

    Google Scholar 

  45. Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K.N., Mohammadi, A.: COVID-CAPS: a capsule network-based framework for identification of COVID-19 cases from X-ray images. Pattern Recogn. Lett. 138, 638−643 (2020)

    Google Scholar 

  46. Chaganti, S., et al.: Quantification of tomographic patterns associated with COVID-19 from chest CT. arXiv preprint arXiv:2004.01279 (2020)

  47. Ganasegeran, K., Abdulrahman, S.A.: Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics, pp. 141–155. Springer International Publishing, Cham (2020)

    Google Scholar 

  48. Liu, D., et al.: A machine learning methodology for real-time forecasting of the 2019–2020 COVID-19 outbreak using internet searches, news alerts, and estimates from mechanistic models. arXiv preprint arXiv:2004.04019 (2020)

  49. Chinazzi, M., et al.: The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368(6489), 395−400 (2020)

    Google Scholar 

  50. Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P., Sikdar, B.: A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 7, 82721–82743 (2019)

    Article  Google Scholar 

  51. Rouse, M.: What is IoMT (Internet of Medical Things) or Healthcare IoT?-Definition From WhatIs.com. IoT Agenda, (2015). https://internetofthingsagenda.techtarget.com/definition/IoMT-Internet-%of-Medical-Things

  52. Deloitte Centre for Health Solutions. Medtech Internet Med. Things (2018). https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshcmedtech-iomt-brochure.pdf

  53. Rodrigues, J.J.P.C.: Enabling technologies for the Internet of health things. IEEE Access 6, 13129–13141 (2018)

    Google Scholar 

  54. AMD Telemedicine. Telemedicine Defined. https://www.amdtelemedicine.com/telemedicineresources/telemedicine-defined.html. Accessed 20 Apr 2020

  55. Hornyak, T.: What America Can Learn From China's Use of Robots and Telemedicine to Combat the Coronavirus. CNBC. (2020). https://www.cnbc.com/2020/03/18/how-china-isusing-robots-and-telemedic%ine-to-combat-the-coronavirus.html

  56. Hinkley, G., Briskin, A., Waives, U.S.: Medicare and HIPAA Rules to Promote Telehealth. Pillsbury Law, (2020). https://www.pillsburylaw.com/en/news-and-insights/uswaives-medicare-an%d-hipaa-rules-to-promote-telehealth.html

  57. Makroo, S.: Technology and Business Order post COVID-19. Observer Research Foundation (ORF), (2020). https://www.orfonline.org/expert-speak/technology-and-business-order-post-covid-19-64471/

  58. Mcneil, D.G.: Can smart thermometers track the spread of the Coronavirus? The New York Times, Mar. (2020). https://www.nytimes.com/2020/03/18/health/coronavirusfever-thermometer%s.html

  59. Yang, G.-Z., et al.: Combating COVID-19-The role of robotics in managing public health and infectious diseases. Sci. Robot., 5(40) (2020) Art. no. eabb5589. https://doi.org/10.1126/scirobotics.abb5589

  60. Watson, J., Builta, J.: IoT Set to Play a Growing Role in the COVID-19 Response- Omdia. OMDIA. (2020). https://technology.informa.com/622426/iot-set-to-play-a-growingrole-in%-the-covid-19-response

  61. D’mello, A.: First IoT Buttons Shipped for Rapid Response to Cleaning Alerts. IoT Now-How to Run an IoT Enabled Business, (2020). https://www.iot-now.com/2020/03/24/101940-rstiot-buttons-shipped-rapid-response-cleaning-alerts/

  62. Burns, C.: Estimote wearables track workers to curb COVID-19 outbreak. SlashGear, (2020). https://www.slashgear.com/estimote-wearables-track-workers-to-curbcovid-19-outbreak-02615366/

  63. Etherington, D.: Estimote launches wearables for workplace-level contact tracing for COVID-19. TechCrunch, (2020). https://techcrunch.com/2020/04/02/estimote-launcheswearables-for-workp%lace-level-contact-tracing-for-covid-19/

  64. Deloitte: Understanding COVID-19’s Impact on the Telecom Sector. Accessed: (2020). https://www2.deloitte.com/global/en/pages/about-deloitte/articles/covid19/understanding-covid-19-impact-on-the-telecom-sector.html

  65. GlobalData: Telecom Sector Will Shine in Post Covid-19 Era, Says GlobalData. (2020). https://www.globaldata.com/telecom-sector-will-shine-in-post-covid-19-e%ra-says-globaldata/

  66. Sohrabi, C., et al.: World health organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int. J. Surgery, 76, 71–76 (2020)

    Google Scholar 

  67. Rouse, M.: What is IoMT (Internet of Medical Things) or Healthcare IoT. (2015). https://internetofthingsagenda.techtarget.com/definition/IoMT-Internet-of-Medical-Things

  68. Garattini, C., Raffle, J., Aisyah, D. N., Sartain, F., Kozlakidis, Z.: Big data analytics, infectious diseases and associated ethical impacts. Philos. Technol. 32(1), 69–85 (2019)

    Google Scholar 

  69. Li, C., et al.: High sensitivity detection of coronavirus SARS-CoV-2 using multiplex PCR and a multiplex-PCR-based metagenomic method. bioRxiv (2020)

    Google Scholar 

  70. Eden, J.-S., et al.: An emergent clade of SARS-CoV-2 linked to returned travellers from Iran. bioRxiv (2020)

    Google Scholar 

  71. Sohrabi, C., et al.: World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 76, 71−76 (2020)

    Google Scholar 

  72. Zhao, X., Liu, X., Li, X.: Tracking the spread of novel coronavirus (2019-ncov) based on big data. medRxiv (2020)

    Google Scholar 

  73. Zhou, C., et al.: COVID-19: challenges to GIS with big data. Geography Sustain. 1(1), 77−87 (2020)

    Google Scholar 

  74. Haleem, A., Vaishya, R., Javaid, M., Khan, I.: Artificial Intelligence (AI) applications in orthopaedics: an innovative technology to embrace. J. Clin. Orthop. Trauma 11, S80–S81 (2020). https://doi.org/10.1016/j.jcot.2019.06.012

    Article  Google Scholar 

  75. Biswas, K., Sen, P.: Space-time dependence of coronavirus (COVID-19) outbreak. arXiv preprint arXiv:2003.03149 (2020)

  76. How DAMO academy’s AI system detects coronavirus cases. (2020). https://www.alizila.com/how-damo-academys-ai-system-detects-coronavirus-cases/

  77. Kalkreuth, R., Kaufmann, P.: COVID-19: a survey on public medical imaging data resources. arXiv preprint arXiv:2004.04569 (2020)

  78. Seoul introduces the COVID-19 AI monitoring call system. (2020). https://english.seoul.go.kr/seoul-introduces-the-covid-19-%E3%80%8Cai-monitoring-call-systemE3808D/

  79. Hussain, A.A., Bouachir, O., Al-Turjman, F., Aloqaily, M.: AI techniques for COVID-19. IEEE Access 8, 128776–128795 (2020). https://doi.org/10.1109/ACCESS.2020.3007939

    Article  Google Scholar 

  80. Jin, J., Sun, W., Al-Turjman, F., Khan, M., Yang, X.: Activity pattern mining for healthcare. IEEE Access 8(1), 56730–56738 (2020)

    Article  Google Scholar 

  81. Ullah, Z., Al-Turjman, F., Mostarda, L., Gagliardi, R.: Applications of artificial intelligence and machine learning in smart cities. Elsevier Comput. Commun. J. 154, 313–323 (2020)

    Article  Google Scholar 

  82. Al-Turjman, F., Baali, I.: Machine learning for wearable iot-based applications: a survey. Wiley Trans. Emerging Telecommun. Technol. (2019). https://doi.org/10.1002/ett.3635

  83. Srivastava, V., et al.: A systematic approach for the COVID-19 prediction and parameters estimation. Personal Ubiquitous Comput. J. (2020). 10.1007_s00779–020–01462–8

    Google Scholar 

  84. Karmore, S., et al.: IoT based humanoid software for identification and diagnosis of Covid-19 suspects. IEEE Sensors J. (2020). https://doi.org/10.1109/JSEN.2020.3030905

  85. Kolhar, M., et al.: A three layered decentralized IoT biometric architecture for city lockdown during COVID-19 outbreak. IEEE Access 8(1), 163608–163617 (2020)

    Article  Google Scholar 

  86. Al-Turjman, F., Deebak, D.: Privacy-aware energy-efficient framework using internet of medical things for COVID-19. IEEE Internet of Things Mag. (2020). https://doi.org/10.1109/IOTM.0001.2000123

  87. Rahman, M., et al.: Data-driven dynamic clustering framework for mitigating the adverse economic impact of covid-19 lockdown practices. Elsevier Sustain. Cities Soc. 62, 102372 (2020)

    Article  Google Scholar 

  88. Waheed, A., et al.: CovidGAN: data augmentation using auxiliary classifier GAN for improved covid-19 detection. IEEE Access 8, 91916–91923 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Near East University, 10, Mersin, Nicosia, Turkey

    Adedoyin A. Hussain & Barakat A. Dawood

  2. Research Centre for AI and IoT, Near East University, 10, Mersin, Nicosia, Turkey

    Adedoyin A. Hussain, Barakat A. Dawood & Fadi Al-Turjman

  3. Department of Artificial Intelligence Engineering, Near East University, 10, Mersin, Nicosia, Turkey

    Fadi Al-Turjman

Authors
  1. Adedoyin A. Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barakat A. Dawood
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fadi Al-Turjman
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Technology and Management, Escola Superior de Tecnologia e Gestão, VIANA DO CASTELO, Portugal

    Sara Paiva

  2. Polytechnic Institute of Viana do Castel, Viana do Castelo, Portugal

    Sérgio Ivan Lopes

  3. SIC Laboratory, INSEEC U, ECE Paris Graduate School of Engineering, PARIS, France

    Rafik Zitouni

  4. Vaagdevi College of Engineering, Telangana, India

    Nishu Gupta

  5. University of Minho, Guimarães, Portugal

    Sérgio F. Lopes

  6. Nagoya University, Nagoya, Japan

    Takuro Yonezawa

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hussain, A.A., Dawood, B.A., Al-Turjman, F. (2021). IoT and AI for COVID-19 in Scalable Smart Cities. In: Paiva, S., Lopes, S.I., Zitouni, R., Gupta, N., Lopes, S.F., Yonezawa, T. (eds) Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-76063-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-76063-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76062-5

  • Online ISBN: 978-3-030-76063-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation