ABSTRACT
The Covid-19 pandemic is an unparalleled threat in today’s environment of quick development, and we face it as a global community. Like climate change, it is challenging our resilience from environmental health, social security, and government to knowledge exchange and economic policy in all sectors of the economy and growth fields. So much as climate change, this too would require everybody to come together and take an appropriate initiative. The coronavirus outbreak has highlighted our strengths and vulnerabilities that it has influenced and enabled us to benefit from each other’s accomplishments and shortcomings. The entire globe might appear small amid this state of disaster and global travel bans. However, it is a period when the concept of teamwork and looking forward were never more relevant. In the wake of Covid-19, all contact-based biometric attendance systems have been rendered practically useless. Thus, a contactless biometric attendance system is the need of the hour to prevent the spreading of Covid-19. The present-day attendance systems are quite difficult to manage and maintain record. The attendance in classes or industries is mostly done manually, and logbooks are used to maintain records. This can be a cumbersome process as sometimes humans can make a mistake which might lead to inconsistency. This chapter proposes a completely automatic attendance system that uses contactless biometric as a health-care major in the Covid-19 pandemic. In a system using facial recognition, there are lots of challenges involved most of the time. These may include low intensity of light or face that is occluded. The You Only Look Once (YOLO) algorithm for facial detection has been used in this chapter to overcome this issue. © The Institution of Engineering and Technology 2022.
ABSTRACT
In this chapter, an application of computer vision and deep learning approach for detecting an outbreak of the influenza virus 2019-nCov caused by the novel coronavirus has been discussed. The deadly and fatal virus that originated in November 2019 has been adversely felt worldwide and declared as a pandemic by the World Health Organization (WHO). COVID-19 is increasing rapidly across the world, and as of November 12, 2020, India has reported more than 86.8 lakh cases, and around the globe, the number has crossed 524 lakh cases in the absence of any effective vaccine for it. Due to the limited number of rapid test kits and the Indian Council of Medical Research (ICMR) labs, more and more people are getting infected by COVID-19 with each passing day. Therefore, the chest X-ray modality has been investigated to detect COVID-19 infected person(s) and understand its impact on a human chest and respiratory system. Further, the convolutional neural network (CNN), a deep learning technique has been used to comprehend the correlation of coronavirus on the human respiratory system using the chest X-ray data of patients. The proposed model has reported a COVID-19 detection accuracy of 99.59% with attention and 99.92% without attention. © The Institution of Engineering and Technology 2022.
ABSTRACT
According to World Health Organization (WHO), Coronaviruses are a large family of viruses which can infect birds and mammals, including humans. At the end of 2019 in Wuhan, China, a novel coronavirus, known as 2019-nCoV, emerged. While COVID-19 and SARS-CoV belong to the same subgroup of beta corona virus, genome-level similarity is only 70 percent, and genetic differences from SARS-CoV have been identified in the novel group. Transfusion of convalescent blood products (CBP), especially convalescent plasma (CP), is useful if the latter induces neutralizing antibodies against emerging infectious agents. CBPs are extracted from a convalescent source by collecting whole blood or plasma apheresis. Passive immunization therapy was successfully implemented back to the 1890s to treat infectious diseases. An individual who is ill with infectious diseases and is recovering has blood drawn and screened for antibodies neutralizing specific microorganisms. After identifying those with high titers of neutralizing antibody, convalescent plasma containing such neutralizing antibodies can be administered to minimize symptoms and mortality in individuals with a specified clinical disease. Convalescent Plasma Transfusion (CPT) has been the subject of increasing attention, especially in the wake of large-scale epidemics.
ABSTRACT
SARI is the most of viral infections that cause generalised clinical symptoms that cannot be easily differentiated from other respiratory infections. Monitoring SARI cases of influenza strains such as H1N1 and Sars-Cov-2, which triggers Covid-19, helps create trends of infection nationally and enhances disease monitoring, which informs response and preparedness to containment. In present article information about genome, Clinical management of severe acute respiratory illness (SARI) in suspect/confirmed novel coronavirus (nCoV) cases by immediate implementation of appropriate IPC measures, early supportive therapy and monitoring, management of hypoxemic respiratory failure, management of septic shock and specific anti-Novel-CoV treatments were given.