Abstract
The fatal outbreak of COVID-19 has placed its fear around the globe since it was first reported in Wuhan, China, in November 2019. COVID-19 has placed all countries and governments across the world in an unstable position. Most countries underwent partial or full lock down due to the dearth of resources to fight the COVID-19 outbreak, primarily due to the challenges of overloaded healthcare systems. The tally of confirmed COVID-19 cases via laboratory continues to increase around the globe, with reportedly 60.5 million confirmed cases as of November 2020. Evidently, innovation has an imperative function to empower the omnipresent health technologies in order to counter the impacts of COVID-19 in a post-pandemic period. More specifically, the Fifth Generation (5G) cellular network and 5G-empowered e-health and Artificial Intelligence (AI) based arrangements are of on the spotlight. This research explores the use of AI and 5G technologies to help alleviate the effects of COVID-19 spread. The novel approach is based on the premises that the COVID-19 vaccine may take years to rollout effectively, whereas the AI and 5G technologies offer effective solutions to reduce the Covid-19 spread within weeks. Currently, the approaches such as contact tracing and virus testing are not secure and reliable; and the cost of testing is high for end users. The proposed solution offers a self-diagnostic mechanism without any security risk of the users’ data with very low cost using cloud-based data analytics using mobile handsets.
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References
Cascella, M., Rajnik, M., Cuomo, A., Dulebohn, S.C., Di Napoli, R.: Features, Evaluation, and Treatment of Coronavirus. StatPearls Publishing, Statpearls (2020)
WHO: Coronavirus disease (COVID-19) pandemic (2021). https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 25 July 2021
Bakshi, S.K., Ho, A.C., Chodosh, J., Fung, A.T., Chan, R.V.P., Ting, D.S.W.: Training in the year of the eye: The impact of the COVID-19 pandemic on ophthalmic education. Br. J. Ophthalmol. 104, 1181–1183 (2020). https://doi.org/10.1136/bjophthalmol-2020-316991
Zhong, L., Mu, L., Li, J., Wang, J., Yin, Z., Liu, D.: Early prediction of the 2019 novel coronavirus outbreak in the Mainland China based on simple mathematical model. IEEE Access 8, 51761–51769 (2020). https://doi.org/10.1109/ACCESS.2020.2979599
Obeidat, S.: How artificial intelligence is helping fight the COVID-19 pandemic. Entrep. Middle East 19–21 (2020)
Naudé, W.: Artificial intelligence against COVID-19: an early review. IZA Discuss. Pap. (2020)
Qualcomm: Everything you need to know about 5G. Q : What is 5G ? (2021). https://www.qualcomm.com/5g/what-is-5g. Accessed 25 July 2021
Ting, D.S.W., Carin, L., Dzau, V., Wong, T.Y.: Digital technology and COVID-19. Nat. Med. 26, 459–461 (2020). https://doi.org/10.1038/s41591-020-0824-5
Chunming, Z., He, G.: 5G applications help China fight against COVID-19. China Acad. Inf. Commun. Technol. 1–4 (2020)
Deloitte Consulting: Combating COVID-19 with 5G opportunities to improve public health systems (2020)
Tortermvasana, K.: AIS uses 5G, robots in pandemic war (2020)
Charles, A., Ruan, S.: In China, robot delivery vehicles deployed to help with COVID-19 emergency—UNIDO (2020)
Happich, J.: 5G edge patrol robots deployed in China to detect Covid-19 cases (2020)
Li, D.: 5G and intelligence medicine—how the next generation of wireless technology will reconstruct healthcare? Precis. Clin. Med. 2, 205–208 (2019). https://doi.org/10.1093/pcmedi/pbz020
Vaishya, R., Haleem, A., Vaish, A., Javaid, M.: Emerging technologies to combat the COVID-19 pandemic. J. Clin. Exp. Hepatol. 10, 409–411 (2020). https://doi.org/10.1016/j.jceh.2020.04.019
He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., Ma, J.: A framekwork for cardiac arrythmia detection from IoT-based ECGs. World Wide Web 23(5), 2835–2850 (2020)
Khalil, F., Wang, H., Li, J.: Integrating Markov model with clustering for predicting web page accesses. In: Proceeding of the 13th Australasian World Wide Web Conference (AusWeb 2007), pp. 63–74 (2007)
Vimalachandran, P., Liu, H., Lin, Y., Ji, K., Wang, H., Zhang, Y.: Improving accessibility of the Australian my health records while preserving privacy and security of the system. Health Inf. Sci. Syst. 8(1), 1–9 (2020)
Supriya, S., Siuly, S., Wang, H., Zhang, Y.: Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf. Sci. Syst. 8(1), 1–15 (2020). https://doi.org/10.1007/s13755-020-00129-1
Sarki, R., Ahmed, K., Wang, H., Zhang, Y.: Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf. Sci. Syst. 8(1), 1–9 (2020). https://doi.org/10.1007/s13755-020-00125-5
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Ahmed, S., Shrestha, A., Yong, J. (2021). Towards a User-Level Self-management of COVID-19 Using Mobile Devices Supported by Artificial Intelligence, 5G and the Cloud. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_4
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