Dedication

The editors would like to dedicate this volume to the departed souls during the COVID-19 pandemic.

Preface

The global pandemic COVID-19 operates its devastation for more than 2 years globally. As of October 5, 2022, more than 624 million people were infected with this disease, and more than 6.5 million lost their lives worldwide. This novel coronavirus is not only a threat to life but also diminishing the economy of the world. Different medical institutes, virology, and pharmacology research centers strive to develop vaccines, antidotes, and antibiotics to eliminate the virus and mitigate its effects on patients. Now, different newly invented vaccines are applied through the scheduled vaccination process, which is continuing globally to eliminate this deadly virus.

However, we have experienced different waves of COVID-19 caused by some new strains of COVID-19. Since December 2020, several coronavirus variants have been identified and are under investigation. Each new variant raises questions: Are people more at risk of getting sick? Will the COVID-19 vaccines still work? Are there new or different things we should do now to stay safe? So, an exciting area of research is that the invented vaccines, whether effective to mitigate the new strains or some modifications, must be ensured. Hence, again, the entire COVID-19 research is under scrutiny in every aspect whenever a new strain is identified.

This volume introduces the most recent research and innovative developments regarding the new strains of COVID-19. While the subfields of medical and natural sciences have been working instantly on deriving solutions and trying to protect humankind against such virus types, there is also a great focus on technological developments for improving the mechanism – momentum of science for effective and efficient solutions. At this point, computer intelligence is the most powerful tool for researchers to fight against COVID-19. Thanks to instant data analysis and predictive techniques by computer intelligence, it is possible to get positive results and introduce revolutionary solutions against related medical diseases. By running capabilities – resources for rising computational intelligence, technological fields like artificial intelligence (AI) (with machine learning (ML)/deep learning (DL)), data mining, and applied mathematics are essential components for processing data, recognizing patterns, modeling new techniques, and improving the advantages of computational intelligence. Nowadays, there is a great interest in the application potentials of computer intelligence to be an effective approach for taking humankind one step away, after COVID-19 and before pandemics similar to COVID-19 may appear. The need to guarantee that newly developed vaccinations are successful in preventing the spread of new strains of viruses, is an intriguing field of study. In this context, the book aims to inform the target audience about the latest findings – results regarding a wide variety of computer intelligence applications for fighting against the new strains of COVID-19.

This volume presents the most recent research and innovative developments regarding the new strains of COVID-19. This volume also focuses on the instant data analysis and predictive techniques by computer intelligence, discussion to get positive results, and the introduction of revolutionary solutions against related medical diseases. The volume comprises 15 contributory chapters to report the latest developments in this direction.

The COVID-19 pandemic is one of the biggest catastrophes of this century. Since January 2020, global COVID-19-induced mortality has attained a new record every day and continues its hegemony even in 2022. Nations are fighting wave after wave of the attacks of this pandemic. The humiliating devastation, non-guaranteed preventive option, and zero-guaranteed treatments have left human beings in extreme despair. An inestimable escalation in victimization has already created a mismatch between the current rate of occupancy in clinical establishments, quarantine centers, and skilled care demand. The emergence of such a pandemic is not new but uncommon. History has evidenced the outbreaks of major diseases like cholera, plague, swineflu, AIDS, SARS-Cov, and Mars-Cov. Each pandemic has challenged the very fundamental existence of mankind. The mutual relationship between the causes and effects of infection is heterogeneous and complex. It has been observed that epidemics, endemics, and pandemics are caused by zoonotic pathogens, that is, bacteria, viruses, or parasites, that are transmitted to humans through the environment or direct contact with food, water, or animals. Regardless of the various speculative theories, investigating the proper mechanism of transmission and developing methods to control and prevent incidents are essential. Vaccination is the only path to salvation from such a pandemic. Rather, a fusion of treatment and prevention has the ability to escape such an attack. During any pandemic, survival instincts have driven the human community into a behavioral shift. But, every pandemic leaves the same question, are we ready to face the next one?

Chapter 1 has summarized and fundamentally addressed the historical emergence of different pandemics in the human community of the world. Moreover, a brief outcome of the management of the COVID-19 pandemic has also been observed.

AI is the precise simulation of human intelligence by machines. AI and computer-aided diagnosis are routinely used in medical imaging. In the context of the COVID-19 pandemic, early and accurate diagnosis of COVID-19 cases reduces the spread and mortality caused by SARS-CoV-2, the causative agent of COVID-19. AI has been used to improve the precise detection of COVID-19 cases. The gold standard for the detection of SARS-CoV-2 is RT-PCR. However, RT-PCR is a time-consuming process that can give false-negative results and requires skilled laboratory technicians, which may not be possible in some resource-constrained regions. Contrastingly, AI-based screening and detection of specific changes in X-ray and CT scan images of suspected COVID-19 patients can offer a cost-effective, speedy, and accurate diagnosis of clinical cases. The convolutional neural network (CNN), a DL algorithm, can improve the accuracy and reliability of the diagnosis of COVID-19 from chest X-rays. An enormous amount of training data is required to make accurate predictions of clinical cases via CNN. Several pretrained models such as GoogLeNet, AlexNet, VGG, and open databases such as GitHub can help with the training of DL networks. In Chapter 2, we look at how AI, ML, and DL can be used to improve the specificity, sensitivity, and accuracy of SARS-CoV-2 diagnosis.

Depression, stress, and anxiety are major issues affecting society among many age groups over a period of time. Of late, the COVID-19 pandemic has brought a jump in a phenomenal increase in cases among the younger age group population. In Chapter 3, the authors attempt to explore various DASS-21 variables among millennials and Gen-Z adults due to COVID-19. A strong association between DASS-21 variables and COVID-19 was established among Gen-Z and millennials. Based on the survey, various factors were ranked to understand the COVID-19 effect on DASS-21 variables. It is found that gender and educational qualifications played no role in the effect of COVID-19 on Gen-Z and millennials. Uncertainty in job and business emerges to be a major cause of depression, anxiety, and stress. The results obtained could help psychologists and government bodies to take appropriate steps to improve the mental health conditions of Gen-Z and millennials in society.

COVID-19 has cost 5 million lives worldwide and destabilized the economic and healthcare systems in just two years. An important strategy to combat COVID-19 is to propose a fast and accurate method of screening infected persons. One such efficient technique of screening is doing radiology examinations using chest X-rays. It was found in earlier studies that people affected with COVID-19 display abnormalities in chest radiography images. A large number of works detecting COVID-19 are presently available however; the works do not give a high accuracy of detection. Motivated by this drawback, a deep network-based solution to determine COVID-19 from X-rays of the chest has been proposed in Chapter 4. With the advent of the different variants of the COVID-19 virus determining whether a patient is affected by COVID-19 is not enough. The variant of COVID-19 also needs to be determined. The proposed work also provides an efficient algorithm for variant detection, that is, delta, omicron, or alpha variant. The proposed algorithm is split into two stages. Detection of COVID-19 is undertaken in the first stage, and in the next stage, the variant detection of COVID-19 is done for patients whose test results are COVID-19 positive. The proposed CNN model is inspired by the residual network model ResNet50. The suggested CNN model is inspired by the residual network model ResNet50. The model proposed is efficient and produces a high accuracy of 99.47% for stage 1 of the algorithm, that is, COVID-19 detection, and an accuracy of 98.46% for stage 2, that is, COVID-19 variant detection on the used dataset obtained from images of patients of Eastern India.

The coronavirus disease that caused the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affected a bulk population worldwide. Understanding the evolution and...