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2022 | Book

How COVID-19 is Accelerating the Digital Revolution

Challenges and Opportunities

Editors: Dr. R. Anandan, Dr. G. Suseendran, Pushpita Chatterjee, Noor Zaman Jhanjhi, Uttam Ghosh

Publisher: Springer International Publishing

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About this book

This book explores how digital technologies have proved to be a useful and necessary tool to help ensure that local and regional governments on the frontline of the emergency can continue to provide essential public services during the COVID-19 crisis. Indeed, as the demand for digital technologies grows, local and regional governments are increasingly committed to improving the lives of their citizens under the principles of privacy, freedom of expression and democracy.

The Digital Revolution began between the late 1950s and 1970s and represents the evolution of technology from the mechanical and analog to the digital. The advent of digital technology has also changed how humans communicate – today using computers, smartphones and the internet. Further, the digital revolution has made a tremendous wealth of information accessible to virtually everyone.

In turn, the book focuses on key challenges for local and regional governments concerning digital technologies during this crisis, e.g. the balance between privacy and security, the digital divide, and accessibility. Privacy is a challenge in the mitigation of COVID-19, as governments rely on digital technologies like contact-tracking apps and big data to help trace peoples’ patterns and movements. While these methods are controversial and may infringe on rights to privacy, they also appear to be effective measures for rapidly controlling and limiting the spread of the virus. Next, the book discusses the 10 technology trends that can help build a resilient society, as well as their effects on how we do business, how we work, how we produce goods, how we learn, how we seek medical services and how we entertain ourselves. Lastly, the book addresses a range of diversified technologies, e.g. Online Shopping and Robot Deliveries, Digital and Contactless Payments, Remote Work, Distance Learning, Telehealth, Online Entertainment, Supply Chain 4.0, 3D Printing, Robotics and Drones, 5G, and Information and Communications Technology (ICT).

Table of Contents

Frontmatter
Chapter 1. Impact of Early Termination of Lockdown and Maintaining Social Distancing: COVID-19
Abstract
A novel corona-virus named COVID-19 has spread rapidly and has caused a global outbreak of respiratory illness. It has been confirmed that the bats are the source host of SARS, and camels act as a source for MERS. However, the source host of the COVID-19 remains unknown. All three kinds of pneumonia show human-to-human transmissions. Among which, COVID-19 shows a longer incubation period. The routes for the human to human transmission are common, respiratory droplets, contact, and aerosol. In which, the new form is Aerosol transmission. In which, integration of the air with droplets will occur during transmission that leads to the formation of Droplet Nucleus. It can lead to infection after inhalation. Because of this, the virus has already spread to South Korea, Japan, Iran, Italy, and other countries. The objective of this chapter is to address the impact and list the suggestion to handle COVID-19 safely. The methodology followed in drafting this chapter is to provide answers to the following questions: Q1: The clinical manifestation of COVID-19? Q2: How to prevent the transmission of this disease and protect themselves? Q3: The outcome of COVID-19 pneumonia. Q4: How to diagnose COVID-19? Q5: The effects of COVID-19 pneumonia on pregnancy: Q6: Coronavirus pneumonia in children. Q7: The response strategies against the COVID-19 in China. Q8: Therapeutic Strategy for COVID-19. Q9: Consequences of COVID-19 in Human Daily Life. Q10: How to deal with the novel Coronavirus disease calmly? Q11: The COVID-19 prevention among students. Q12: Plan to return to the campus. Q13: Home-based self-care in climacteric women Q14: Strategies to climacteric women’s psychological problems during COVID-19 pandemic. The outcome of the present research is to provide suggestions to the humankind towards handling the epidemic safely.
Syed Muzamil Basha, J. Janet, S. Balakrishnan, Sajeev Ram, Somula Ramasubbareddy, N. Ch. Sriman Narayana Iyengar
Chapter 2. Health Care Digital Revolution During COVID-19
Abstract
The digital revolution has had both good and bad effects since the 1980s. COVID-19 was identified only a few weeks after the pandemic began. COVID-19’s rapid spread over the world, combined with the virus’s originality, necessitated novel solutions. Professional communication across numerous platforms is increasingly reliant on social media. The constant flow of new knowledge and novel methods of practice has led to the creation of new digital communication strategies. There are majority of health employee remained sick or self-isolation to physically face COVID-19’s patents, clinical groups describe widespread use of messaging apps for communication, to organize service provision or manage staff rotations. The use of digital solutions has risen to previously unheard-of heights as a result of the lockdown, increasing the possibility of scaling up alternative social and economic methods. However, they provide new technological risks and concerns, placing new expectations on policymakers. Growth in COVID-19 is the digital and technological revolution that has shaped our world over the last century. As healthcare systems around the country prepare for an influx of COVID-19 patients, immediate action is needed to modernize healthcare delivery and scale up our systems by leveraging digital technologies. The COVID-19 pandemic has brought to light the limitations of our current healthcare system’s ability to serve all the world during a crisis. The main goal of this chapter is to concentrate on digital healthcare issues and challenges, as well as to provide limitations and recommendations.
Imdad Ali Shah, N. Z. Jhanjhi, Mamoona Humayun, Uttam Ghosh
Chapter 3. Impact of Internet of Health Things (IoHT) on COVID-19 Disease Detection and Its Treatment Using Single Hidden Layer Feed Forward Neural Networks (SIFN)
Abstract
COVID-19 endemic has made the entire world face an extraordinary challenging situation which has made life in this world a fearsome halt and demanding numerous lives. As it has spread across 212 nations and territories and the infected cases and deaths are increased to 5,212,172 and 334,915 (as of May 22 2020). Still, it is a real hazard to human health. Severe Acute Respiratory Syndrome cause vast negative impacts economy and health populations. Professionals involved in COVID test can commit mistakes when testing for identifying the disease. Evaluating and diagnosing the disease by medical experts are the significant key factor. Technologies like machine learning and data mining helps substantially to increase the accuracy of identifying COVID. Artificial Neural Networks (ANN) has been extensively used for diagnosis. Proposed Single Hidden Layer Feedforward Neural Networks (SLFN)-COVID approach is used to detect COVID-19 for disease detection on creating the social impacts and its used for treatment. The experimental results of the proposed method outperforms well when compared to existing methods which achieves 83% of accuracy, 73% of precision, 68% of Recall, 82% of F1-Score.
S. Murugan, K. Vijayakumar, V. Sivakumar, R. Manikandan, Ambeshwar Kumar, K. Saikumar
Chapter 4. Intelligent Approach to Combat COVID-19: An Insight Analysis
Abstract
COVID-19 – the utmost global crisis and the major global pandemic is literally changing our life. Every person is observing at the everyday rise of the death toll and the fast, exponential growth of this novel and dynamic strain of the virus. To find the effective treatment, the virus source prediction, infection classifications are important issues to be addressed. As we are waiting to get rid of this situation and waiting to know the peak and down fall timing of this pandemic, forecasting of epidemic development is also important issue to be addressed. In this present chapter we have used some mathematical modeling and Artificial Intelligence based or more specifically Machine learning based approaches to combat this pandemic.
Pranati Rakshit, Soumen Kumar, Moumita Kumar Roy
Chapter 5. Impact of COVID-19 on Higher and Post-secondary Education Systems
Abstract
Education has a critical role in societal progress. A strong educational system produces good citizens. Countries that scrimp on education fall behind in the drive for progress. The advancement of ICT has resulted in major and positive advancements in the educational system, allowing knowledge to be accessed from anywhere. Numerous of people are unable to be present traditional the advantages of classes in the e-learning system, which has been embraced by most modern civilizations. However, technology cannot be utilized to replace face-to-face communication learning. The combination of traditional education and eLearning is extremely beneficial, and many industrialized civilizations are reaping the benefits. COVID-19’s rapid global expansion has recently brought the old educational system to a halt. Lockdown and seclusion have kept billions of children out of school. The majority of countries have declared school closures. Distance learning is the only way to keep the educational system moving in such a situation. The current eLearning infrastructure, on the other hand, was not prepared for such a quick change from traditional education to eLearning. The object of this chapter is to give focus to the higher and post-secondary education systems and to compare the results to determine which system is more effective. Further, every day, lots of new problems are reported, such as internet access, conducting evaluations, etc.
Imdad Ali Shah, N. Z. Jhanjhi, Mamoona Humayun, Uttam Ghosh
Chapter 6. Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector
Abstract
Coronavirus 2019 (COVID-19) medical images detection and classification are used in artificial intelligence (AI) techniques. Few months back, from the observation it is witnessed that there is a rapid increase in using AI techniques for diagnosing COVID-19 with chest computed tomography (CT) images. AI more accurately detects COVID-19; moreover efficiently differentiates this from other lung infection and pneumonia. AI is very useful and has been broadly accepted in medical applications as its accuracy and prediction rates are high. This paper is developed and aims to fight against corona through AI using computational intelligence in detecting and classifying COVID-19 using Densnet-121 architecture on chest CT images from a global diverse multi-institution dataset. Furthermore, data from clinics and images from medical applications improve the performance of the proposed approach and provide better response with practical applications. Classification performance was evaluated by confusion matrices followed by overall accuracy, precision, recall and specificity for precisely classifying COVID-19 against any condition.
Manikandan Ramachandran, Rajkumar Kulandaivel, Hariharan Kalyanaraman, Murugan Subramanian, Ambeshwar Kumar
Chapter 7. Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm
Abstract
As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets.
Manikandan Ramachandran, Hariharan Kalyanaraman, Prassanna Jayachandran, Ambeshwar Kumar, Murugan Subramanian
Chapter 8. Computational Intelligence Using Big Data for Fight Against Covid-19 Pandemic in Healthcare Environment
Abstract
In world, COVID-19 disease spread over 214 countries and areas which efficiently affects every aspect of our daily lives. In various areas, motivated by recent applications and advances of big data and computational intelligence (CI), this research aims at increasing their significance in COVID-19 response like prevention of severe effects and outbreaks. To improve diagnosis efforts, assess risk factors from blood tests and deliver medical supplies, CI is used during COVID-19. To forecast future COVID-19 cases, CI is used. To check goodness as high accuracy prediction method, the proposed method is checked with real-world data which focus on CI and big data, method which are used in current pandemic. In upcoming days, to enact necessary protection plans, it is very difficult to detect as well as diagnose. For computational methods with help of big data, this research provides prediction and detection of COVID-19. For predicting and detecting cases of COVID-19, performances of proposed models are used as criteria. To improve detection accuracy of COVID-19 cases, proposed method increases combination of big data analytics and CI models with nature-inspired techniques.
Ashok Kumar Munnangi, Ramesh Sekaran, Arun Prasath Raveendran, Manikandan Ramachandran
Chapter 9. Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study
Abstract
This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease.
Amit Kumar Mishra, Ramakant Bhardwaj, K. Sruthila Gopalakrishnan
Chapter 10. Treatment of Novel Coronavirus (2019-nCoV) Using Hinokitiol (β-thujaplicin) Copper Chelate
Abstract
Human Coronavirus (HCoV) or Novel Coronavirus (2019-nCoV) is probably a brand new version of coronavirus that belongs to Betacoronaviruses kind Human Coronaviruses, similar to the Severe Acute Respiratory Syndrome (SARS) coronavirus and Middle-East Respiratory Syndrome (MERS) coronavirus. China recorded the number one case of this virus in December 2019 at Wuhan, the capital town of Hubei province. By 27 March 2020, 10:00 CET, nearly 23,335 humans died out of 509,164 showed instances recorded throughout the world. By the give up of January 2020, China showed that the Novel Coronavirus (2019-nCoV) transmitted from one human to another. This studies pursuits to research a completely specific medicament called “Hinokitiol Copper Chelate” towards the large quantity 2019-nCoV Spike Glycoprotein with a unmarried receptor binding domain. This take a look at gives a super version for Hinokitiol Copper Chelate to be examined in silico towards 2019-nCoV Main Protease.
R. Anandan, Noor Zaman Jhanjhi, B. S. Deepak
Chapter 11. Effects of Economic Liberalization on Poverty and Inequality in India – A Case Study of Pre-COVID-19 Period
Abstract
The purpose of this research is to study the effects of neoclassical trade liberalization policies enacted in India in 1991 to determine the effect on levels of poverty and income inequality. This research predicts that poverty and economic inequality will be reduced due to implementation of economic liberalization policies. The research uses empirical data from the National Sample Survey Organization (NSSO), in India and develops a regression model to determine the effects of economic liberalization on income inequality and absolute poverty. The results of the regression model suggest that income inequality and poverty decreased during the year liberalization policies were enacted, but is not statistically proven with enough confidence that liberalization is strongly correlated with a reduction in inequality and poverty. There is a weak statistical correlation that suggests inequality increased in the Indian urban sector, and decreased in the rural sector due to liberalization. In conjunction with a literature review where more robust data and econometric models are applied, the empirical analysis by complimented with the fact that in general income inequality decreased due to economic liberalization policies alone, holding all exogenous factors that affect income inequality constant. The literature review also confirms that poverty levels decreased with economic liberalization, holding all other exogenous factors that affect poverty constant. The implication of this research is that liberalization polices have been successful for overall development in India, and suggests that implementation of liberalization policies may be desirable in nations under similar circumstances as India in the era before its liberalization.
Rohit Narayan, Satyendra Narayan
Chapter 12. Analyzing the Effect of Choice and Availability in Healthcare on Health Outcomes in Canada – A Pre-COVID-19 Environment
Abstract
This research hypothesizes that greater availability of healthcare services, and greater choice in healthcare facilities results in better health when controlling for a variety of socio-economic factors within the Canadian context. This research will model access to healthcare services using density of general and specialist physicians relative to population size, and the geographic density of healthcare facilities. Choice in healthcare is modeled by the number of healthcare facilities in each health region, when normalized by the population in that health region.
Various health outcomes will be used as benchmarks to test this hypothesis, including self- reported general health, self-reported mental health, influenza immunization rates, body mass index (BMI), and incidence of diabetes, cardiovascular disease and hypertension.
From the empirical results, choice in the healthcare system does not have an impact on the selected health outcomes. Increased availability of healthcare generally improves health outcomes, but this is dependent on the health outcome in question, and the provincial region being analyzed.
Rohit Narayan, Satyendra Narayan
Metadata
Title
How COVID-19 is Accelerating the Digital Revolution
Editors
Dr. R. Anandan
Dr. G. Suseendran
Pushpita Chatterjee
Noor Zaman Jhanjhi
Uttam Ghosh
Copyright Year
2022
Electronic ISBN
978-3-030-98167-9
Print ISBN
978-3-030-98166-2
DOI
https://doi.org/10.1007/978-3-030-98167-9

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