**1. Introduction**

Emerging coronavirus disease (SARS-CoV-2) has posed a significant problem in global public health, and economic crises that we have not experienced before in this century. The disease appeared in December 2019 (COVID-19) which put a large number of individuals around the globe in quarantine, isolation, and lockdown in other to curtail the spreading of the disease [1, 2]. Guidelines have been issued by the Centers for Disease Control and Prevention (CDC) and the World Health Organization to curtail the spreading of the disease and protect the healthy population from contacting the SARS-CoV-2 virus from the infected individuals [3]. Throughout the globe, including the USA do not have the facilities to accommodates such large individuals infected with the SARS-CoV-2 virus while managing quarantine. The authorities all over the globe had built several new facilities (Hospitals) to manage individuals infected with the disease [4]. In this context, it's important to use other alternative models as an adjunct along the current methods using by [3] to curtail how this SARS-CoV-2 virus spreading rapidly like wildfire. Several studies show, how computational intelligence techniques can be applied as an adjunct along with the current guidelines show by [3] to yield timely intervention in faster detection, diagnosis, prediction, contact tracing, drug discovery, treatment, and recovery of infected individuals.

CI is defined by [5] "as a branch of artificial intelligence (AI) which includes the study of versatile components to empower or encourage savvy practices in intricate and evolving situations". CI covers all pieces of AI and underlines the improvement and advancement of real-world applications. However, the virtuous circle of synergy between computational intelligence (CI), life science, and nature, show how CI techniques got their inspiration from natural phenomena that are utilized to solve different field of science complex problem, more specifically medicine [6]. The power of computational intelligence techniques has shown different success stories and always been fruitful since its inception with different novel ideas that are inspired by biology, and nature with powerful computational models. Two "fathers of computer science", Alan M. Turing and John von Neumann, in the year 1940s and 1950s, utilized natural phenomena of pattern formation and self-reproduction to formulate the basis of the computational model known as Cellular Automata [7, 8]. Perceptron was created based on working inner neurons in the brain [9], which is the fundamentals component of artificial neural networks that were advocated during the current "deep learning revolution" [10].

Computational Intelligence (CI) is the hypothesis, design, application, and advancement of "biologically and linguistically" spurred computational standards [11]. Generally, the three primary types of CI have been Neural Networks, Fuzzy Systems, and Evolutionary Computation. Nonetheless, in time numerous naturepropelled processing standards have developed. In this manner, CI is a developing field and at present notwithstanding the three fundamental constituents, it includes figuring ideal models like "ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks" [11]. CI assumes a significant part in creating effective insightful frameworks, including games and cognitive developmental systems. Throughout the most recent couple of years, there has been a blast of research on Deep Learning, specifically deep convolutional neural networks. The best AI framework depends on CI.

The core designing objectives of computational intelligence are to show the methods for the design of intelligence and the central scientific task of computational intelligence is to perceive the philosophies that make intelligent behavior possible, regardless of whether in artificial or natural systems. The center methodologies of computational intelligence-like fuzzy systems, neural computing, and evolutionary computing- have as of late arose as promising devices for the application, development, improvement, and execution of intelligent agents/systems in medical services. Indeed, computational intelligence advancement play important role in bringing reforms to medical services practice.

However, computational intelligence discovered its way into the field of medical science since its inception, this is due to the colossal need for CI techniques in the medical arena. Several applications of computational intelligence techniques exist in the field of the medical arena, for example using neural networks which includes, but not limited to "Cancer prediction [12, 13], Clinical diagnosis of COVID-19 [14], Length of stay prediction [15–17], Speech recognition [18–20], Ophthalmology [21, 22], Radiology (MRI, adaptive medical image

*The Power of Computational Intelligence Methods in the Containment of COVID-19 Pandemic… DOI: http://dx.doi.org/10.5772/intechopen.98931*

visualization, ultrasound images) [23, 24], Neurology (aphasia, electroencephalogram—EEG, and EEG analysis) [25, 26], Image interpretation and analysis [27], Development of drugs [28–30]". However, an investigation by [14] utilized deep learning techniques to identify acoustic signatures of the presence and severity of COVID-19 using a standardized dataset of digital lung auscultations. The researchers estimate that automated translation of lung auscultation could better democratize the accuracy of this basic clinical test beyond the individual capacities of the doctors. Also, they intend to consolidate their algorithm into an autonomous computerized stethoscope (right now a work in progress), that could help decentralize great respiratory examination and observing, and maybe even engage patients to survey themselves, which would lessen nosocomial contaminations happening during a traditional clinical test. Even patients would be able to examine themself at home. This chapter discusses the utility of CI as an adjunct along with the current other methods used in the containment of COVID-19.
