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A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

Tallat Jabeen1,2, Ishrat Jabeen1, Humaira Ashraf2, Nz Jhanjhi3,*, Mamoona Humayun4, Mehedi Masud5, Sultan Aljahdali5

1 Research Center for Modelling and Simulation (RCMS) NUST, Islamabad, 44000, Pakistan
2 Department of Computer Science and Software Engineering, International Islamic University, Islamabad, 44000, Pakistan
3 School of Computer Science and Engineering, SCE Taylor’s University, Subang Jaya, 47500, Malaysia
4 College of Computer Science and Information Technology, Jouf University, Al Jouf, Saudi Arabia
5 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: Nz Jhanjhi. Email: email

Computers, Materials & Continua 2022, 70(2), 2365-2380.


COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019. It affects the whole world through person-to-person communication. This virus spreads by the droplets of coughs and sneezing, which are quickly falling over the surface. Therefore, anyone can get easily affected by breathing in the vicinity of the COVID-19 patient. Currently, vaccine for the disease is under clinical investigation in different pharmaceutical companies. Until now, multiple medical companies have delivered health monitoring kits. However, a wireless body area network (WBAN) is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient. The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure. If COVID-19 affected patient is monitored through WBAN sensors and network, a physician or a doctor can guide the patient at the right time with the correct possible decision. This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein, a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output. Security cipher helps to avoid wireless network issues like packet loss, network attacks, network interference, and routing problems. Monte Carlo based covid-19 detection technique gives 90% better results in terms of time complexity, performance, and efficiency. Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity, performance, and efficiency and thus, is advocated as a significant application for lessening hospital expenses.


Cite This Article

T. Jabeen, I. Jabeen, H. Ashraf, N. Jhanjhi, M. Humayun et al., "A monte carlo based covid-19 detection framework for smart healthcare," Computers, Materials & Continua, vol. 70, no.2, pp. 2365–2380, 2022.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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