Intelligent Systems and Methods to Combat Covid-19

Globally, there is massive uptake and explosion of data, and the challenge is to address issues like scale, pace, velocity, variety, volume, and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 toward epidemiological triad and the study of state of the art. The main aim of this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms, and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time.

Today, with the rapid spread of COVID-19, many governments and start-ups are coming forward to develop smartphone apps that trace where we all are, whom we met and for how long, with a goal of interrupting new chains by informing potentially exposed people. These new platforms make use of anonymous use of Bluetooth technology and GPS, enabled either on smartphones or armbands in order to prepare maps corresponding to quarantine monitoring, contact tracing, movement tracking, social distancing and density reports. With different apps for different countries, one thing most of the apps facilitate is tracking. To save lives during an extraordinary crisis, many governments are willing to overlook privacy implications. Keeping in view that the sensitive data being collected is not exclusive to public health organizations and governments, this chapter explores different apps that were developed aiming to combat COVID-19, and the related personal data privacy concerns that arise in the post-coronavirus era.

Coronavirus disease-19 (COVID-19) is the name given by World Health Organization (WHO) and the cause of this pandemic is severe acute respiratory syndrome-related coronavirus (SARS-CoV-2). This pandemic was started in China and later got spread throughout the world. As of now, it has affected around 213 countries, areas or territories and the world has eyes on it. This chapter provides the insight on mathematical perception about the coronavirus outbreak and is analyzed from prediction and optimization point of view. Constraints, objectives and measures are identified and their association and dependencies are analyzed from mathematical point of view. Four levels and weighting factors are used for the identified constraints. Presented mathematical formulation can evaluate the count of positive cases, mortality, recovery cases, transmission rate and prediction of peak time period. It is observed that the coronavirus outbreak is the constrained multi-objective prediction and optimization problem and also it is time variant.

The first case of COVID-19 was detected in Wuhan the city of China and now it has been spread over more than 100 countries. Due to this epidemic situation, the World Health Organization declares emergency in various cities and countries which causes an outbreak for COVID-19 that leads to control this virus spread. AI plays a significant role to control the COVID-19 a pandemic disease. This chapter explains an automated Artificial Intelligence Enabled (AIE) framework which is designed to control the further spread of the novel coronavirus. Artificial intelligence (AI) is used to address about the current disease coronavirus if applied in an innovative way. The main motive of this chapter suggested that AI is merged with the latest technologies like machine learning (ML) and Global Positioning System (GPS) which are used to design an innovative automation system which is used to control the further spreading of coronavirus. For experimental results, the concept of AIE framework is designed by considering the scenario of urban and rural regions of the state of Haryana(India). The comparative analysis of AIE with traditional frameworks is shown in Table 1 which defines the novelty and efficiency of designed framework. Finally on the basis of this innovative AIE-designed automation system, the 98 and 97% accuracy is achieved in urban and rural regions respectively. Finally, we can conclude that this automated system is fully utilized and works efficiently to control and prevent the further spreading of this novel epidemic coronavirus.

Artificial intelligence (AI) can help to address coronavirus if applied creatively. Artificial intelligence training models to deal with COVID-19 are having challenges as historical data is still not available. Drones and robots equipped with IoT devices provide raw data that needs computing analysis to make that data meaningful and actionable without human involvement. The power of AI and edge computing lies in its ability to process a massive amount of data at breakneck speed and improving efficiency. It enables big data analytics and deployment of algorithm and transmission of data across edge and cloud. This chapter presents how current artificial intelligence enables robotic drone applications and network connectivity is used to improve their performance and increase efficiency in various situations to fight COVID-19. Further, it provides an in-depth review and analysis of literature on related work on COVID-19 outbreak. It also gives the necessary background for future research in edge intelligence, AI-enabled robotic drone and intelligent networks.

The 2019 coronavirus disease (COVID-19) with its origin in China has spread rapidly to other nations and infected millions of people. In this context, this paper proposes the development of algorithm that enhances the details of images and assists the doctors in knowing the exact location of affected area. The proposed technique improvises the most popular image enhancement algorithm, namely, multiscale retinex and adjusts the parameters to intensify the details of chest X-ray/CT images of COVID-19 patients. Multiscale retinex (MSR) is human perception-related enhancement algorithm which improves intensity, contrast, and sharpness in medical image through dynamic range compression. The proposed scheme improves the details of images and validates the resulting images using novel metric called wavelet energy. The proposed study is evaluated on images of COVID-19 patients have been obtained from the open-source GitHub repository. Considering the experimental result presented and performance metric, the proposed algorithm has provided important details to doctors in making right decision.

During the Chinese Spring Festival travel rush in 2020, a new type of pneumonia disease, named COVID-19 subsequently broke out in Wuhan, Hubei province, China. The COVID-19 was quickly spreading in China and emerged nearly all over the world. In this chapter, our motivation is to adopt the deep learning techniques to help clinic doctors to diagnose the patients of COVID-19 and predict the trend of COVID-19. To realize our motivation, we on the one hand adopt deep learning techniques to analyse CT images of patients. The transfer learning and data augmentation techniques are adopted for the lacking of samples in our obtained CT image data set. We build a model by designing and training a new deep network to help clinic doctors to make an appropriate diagnose decision. On the other hand, according to the spreading characteristics of COVID-19 and the controlling measures adopted by Chinese government, we propose to modify the classic SEIR (susceptible-exposed-infectious-recovered) model and establish a new SEIR dynamics model with considering the infectiousness of the people in the latent period and the quarantine period. The appropriate parameters of our modified SEIR model are learned by using deep learning techniques. Our proposed deep learning-based diagnosis for COVID-19 can help medicine doctors to make an appropriate diagnostic decision. Our modified SEIR model can effectively predict the transmission trend of COVID-19 and can be used for short-term trend prediction of the epidemic.

The sudden outburst of the COVID-19 has hit the world badly. Avoid, control, and monitor (ACM) is the need of time! With limited expert manpower in COVID-19, the technologies like artificial intelligence (AI), robotics (R), and IOT (I), i.e., (ARI) would help in avoidance of further spread and control of disease transmission. Prediction and analysis tools are effectively implemented only when sufficient data is available. Though an early stage of any pandemic has to deal with the scarcity of data, an early detection and prediction are equally important steps to fight COVID-19. But a complete solution to the pandemic is impossible because of the loose ends like availability of data and “dependent” development of ARI technology. Since December 2019, there has been a continuous up-scaling in analysis and prediction algorithms because of more data getting available in terms of features and more number of the cases across the world. This chapter will discuss the role of ARI and loose ends in their implementation. It is focused on three major aspects: AI algorithms in analysis and prediction, the use of robotics in control and prevention of the pandemic and the role of IOT for the patient monitoring system (PMS). This discussion will provide an evolutionary path of the algorithms. The accuracy rate for diagnosis and prediction has been increased because of the various novel approaches by researchers. They are trying to overcome the loopholes and are tying the loose ends with the advent of more and more data!

It is a well-established fact that ‘the human beings are the social beings.’ But many a times in a specific situations or circumstances, human beings are forced to adopt and practice social distancing. With the recent outbreak of the pandemic caused by novel coronavirus (COVID-19), people around the world have been advised by the authorities to practice social distancing. This chapter is a modest attempt to study the impact of artificial intelligence on social distancing. AI has contributed and has helped people in different ways in order to maintain social distance. The current situation as well as the advancement of AI in dealing with it has sociological, economic, political, cultural and environmental consequences on the lives of the people all over the world. The present chapter attempts to study the impact of artificial intelligence on maintaining social distancing. The data for understanding the impact on the lives of people shall be collected from the secondary sources as collection of primary data is not possible in this time of coronavirus. The analysis has been done through different case studies related to various technologies which with the help of AI aid in facilitating social distancing and in turn curbing the menace of coronavirus.

This paper highlights the way companies can apply artificial intelligence (AI) in the post-COVID-19 period. We show that how the AI can be advantageous to develop an inclusive model and apply to the businesses of various sizes. The recommendation can be beneficial for academic researchers to identify several ways to overcome the obstacles that companies may face in post-COVID-19 period. The paper also addresses few major global issues, which can assist the policy makers to consider developing a business model to bounce back the world economy after this crisis is over. Overall, this paper enhances the understanding of stakeholders of business about the importance of application of the AI in businesses in a volatile market in post-COVID-19 period.