Enabling Healthcare 4.0 for Pandemics A Roadmap Using AI, Machine Learning, IoT and Cognitive Technologies
, by Juneja, Abhinav; Bali, Vikram; Juneja, Sapna; Jain, Vishal; Tyagi, Prashant
- ISBN: 9781119768791 | 1119768799
- Cover: Hardcover
- Copyright: 9/22/2021
ENABLING HEALTHCARE 4.0 for PANDEMICS
The book explores the role and scope of AI, machine learning and other current technologies to handle pandemics.
In this timely book, the editors explore the current state of practice in Healthcare 4.0 and provide a roadmap for harnessing artificial intelligence, machine learning, and Internet of Things, as well as other modern cognitive technologies, to aid in dealing with the various aspects of an emergency pandemic outbreak. There is a need to improvise healthcare systems with the intervention of modern computing and data management platforms to increase the reliability of human processes and life expectancy. There is an urgent need to come up with smart IoT-based systems which can aid in the detection, prevention and cure of these pandemics with more precision. There are a lot of challenges to overcome but this book proposes a new approach to organize the technological warfare for tackling future pandemics.
In this book, the reader will find:
- State-of-the-art technological advancements in pandemic management;
- AI and ML-based identification and forecasting of pandemic spread;
- Smart IoT-based ecosystem for pandemic scenario.
Audience
The book will be used by researchers and practitioners in computer science, artificial intelligence, bioinformatics, data scientists, biomedical statisticians, as well as industry professionals in disaster and pandemic management.
Abhinav Juneja PhD is an associate director & professor at BMIET, Sonepat, India.
Vikram Bali PhD is professor and head of the Computer Science and Engineering Department at JSS Academy of Technical Education, Noida, India.
Sapna Juneja PhD is professor in the Department of Computer Science and Engineering at BMIET, Sonepat, India.
Vishal Jain PhD is an associate professor in the Department of Computer Science and Engineering, Sharda University, Greater Noida, India
Preface xv
Part 1: Machine Learning for Handling COVID-19 1
1 COVID-19 and Machine Learning Approaches to Deal With the Pandemic 3
Sapna Juneja, Abhinav Juneja, Vikram Bali and Vishal Jain
1.1 Introduction 3
1.1.1 COVID-19 and its Various Transmission Stages Depending Upon the Severity of the Problem 4
1.2 COVID-19 Diagnosis in Patients Using Machine Learning 5
1.2.1 Machine Learning to Identify the People who are at More Risk of COVID-19 6
1.2.2 Machine Learning to Speed Up Drug Development 7
1.2.3 Machine Learning for Re-Use of Existing Drugs in Treating COVID-19 8
1.3 AI and Machine Learning as a Support System for Robotic System and Drones 10
1.3.1 AI-Based Location Tracking of COVID-19 Patients 10
1.3.2 Increased Number of Screenings Using AI Approach 11
1.3.3 Artificial Intelligence in Management of Resources During COVID-19 11
1.3.4 Influence of AI on Manufacturing Industry During COVID-19 11
1.3.5 Artificial Intelligence and Mental Health in COVID-19 14
1.3.6 Can AI Replace the Human Brain Intelligence in COVID-19 Crisis? 14
1.3.7 Advantages and Disadvantages of AI in Post COVID Era 15
1.4 Conclusion 17
References 17
2 Healthcare System 4.0 Perspectives on COVID-19 Pandemic 21
Rehab A. Rayan, Imran Zafar and Iryna B. Romash
2.1 Introduction 22
2.2 Key Techniques of HCS 4.0 for COVID-19 24
2.2.1 Artificial Intelligence (AI) 24
2.2.2 The Internet of Things (IoT) 25
2.2.3 Big Data 25
2.2.4 Virtual Reality (VR) 26
2.2.5 Holography 26
2.2.6 Cloud Computing 27
2.2.7 Autonomous Robots 27
2.2.8 3D Scanning 28
2.2.9 3D Printing Technology 28
2.2.10 Biosensors 29
2.3 Real World Applications of HCS 4.0 for COVID-19 29
2.4 Opportunities and Limitations 33
2.5 Future Perspectives 34
2.6 Conclusion 34
References 35
3 Analysis and Prediction on COVID-19 Using Machine Learning Techniques 39
Supriya Raheja and Shaswata Datta
3.1 Introduction 39
3.2 Literature Review 40
3.3 Types of Machine Learning 42
3.4 Machine Learning Algorithms 43
3.4.1 Linear Regression 43
3.4.2 Logistic Regression 45
3.4.3 K-NN or K Nearest Neighbor 46
3.4.4 Decision Tree 47
3.4.5 Random Forest 48
3.5 Analysis and Prediction of COVID-19 Data 48
3.5.1 Methodology Adopted 49
3.6 Analysis Using Machine Learning Models 54
3.6.1 Splitting of Data into Training and Testing Data Set 54
3.6.2 Training of Machine Learning Models 54
3.6.3 Calculating the Score 54
3.7 Conclusion & Future Scope 55
References 55
4 Rapid Forecasting of Pandemic Outbreak Using Machine Learning 59
Sujata Chauhan, Madan Singh and Puneet Garg
4.1 Introduction 60
4.2 Effect of COVID-19 on Different Sections of Society 61
4.2.1 Effect of COVID-19 on Mental Health of Elder People 61
4.2.2 Effect of COVID-19 on our Environment 61
4.2.3 Effect of COVID-19 on International Allies and Healthcare 62
4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19 63
4.2.5 Effect of COVID-19 on Labor Migrants 63
4.2.6 Impact of COVID-19 on our Economy 64
4.3 Definition and Types of Machine Learning 64
4.3.1 Machine Learning & Its Types 65
4.3.2 Applications of Machine Learning 68
4.4 Machine Learning Approaches for COVID-19 69
4.4.1 Enabling Organizations to Regulate and Scale 69
4.4.2 Understanding About COVID-19 Infections 69
4.4.3 Gearing Up Study and Finding Treatments 69
4.4.4 Predicting Treatment and Healing Outcomes 70
4.4.5 Testing Patients and Diagnosing COVID-19 70
References 71
5 Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19 75
Nishant Jha and Deepak Prashar
5.1 Introduction 76
5.2 Related Work 78
5.3 Suggested Methodology 79
5.4 Models in Epidemiology 80
5.4.1 Bayesian Inference Models 81
5.4.1.1 Markov Chain (MCMC) Algorithm 82
5.5 Particle Filtering Algorithm 82
5.6 MCM Model Implementation 83
5.6.1 Reproduction Number 84
5.7 Diagnosis of COVID-19 85
5.7.1 Predicting Outbreaks Through Social Media Analysis 86
5.7.1.1 Risk of New Pandemics 87
5.8 Conclusion 88
References 88
Part 2: Emerging Technologies to Deal with COVID-19 91
6 Emerging Technologies for Handling Pandemic Challenges 93
D. Karthika and K. Kalaiselvi
6.1 Introduction 94
6.2 Technological Strategies to Support Society During the Pandemic 95
6.2.1 Online Shopping and Robot Deliveries 96
6.2.2 Digital and Contactless Payments 96
6.2.3 Remote Work 97
6.2.4 Telehealth 97
6.2.5 Online Entertainment 98
6.2.6 Supply Chain 4.0 98
6.2.7 3D Printing 98
6.2.8 Rapid Detection 99
6.2.9 QRT-PCR 99
6.2.10 Immunodiagnostic Test (Rapid Antibody Test) 99
6.2.11 Work From Home 100
6.2.12 Distance Learning 100
6.2.13 Surveillance 100
6.3 Feasible Prospective Technologies in Controlling the Pandemic 101
6.3.1 Robotics and Drones 101
6.3.2 5G and Information and Communications Technology (ICT) 101
6.3.3 Portable Applications 101
6.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges 102
6.4.1 Remote Healthcare 102
6.4.2 Prevention Measures 103
6.4.3 Diagnostic Solutions 103
6.4.4 Hospital Care 104
6.4.5 Public Safety During Pandemic 104
6.4.6 Industry Adapting to the Lockdown 105
6.4.7 Cities Adapting to the Lockdown 105
6.4.8 Individuals Adapting to the Lockdown 106
6.5 The Golden Age of Drone Delivery 107
6.5.1 The Early Adopters are Winning 107
6.5.2 The Golden Age Will Require Collaboration and Drive 108
6.5.3 Standardization and Data Sharing Through the Smart City Network 108
6.5.4 The Procedure of AI and Non-AI-Based Applications 110
6.6 Technology Helps Pandemic Management 111
6.6.1 Tracking People With Facial Recognition and Big Data 111
6.6.2 Contactless Movement and Deliveries Through Autonomous Vehicles, Drones, and Robots 112
6.6.3 Technology Supported Temperature Monitoring 112
6.6.4 Remote Working Technologies to Support Social Distancing and Maintain Business Continuity 112
6.7 Conclusion 113
References 113
7 Unfolding the Potential of Impactful Emerging Technologies Amid COVID-19 117
Nusrat Rouf, Aatif Kaisar Khan, Majid Bashir Malik, Akib Mohi Ud Din Khanday and Nadia Gul
7.1 Introduction 118
7.2 Review of Technologies Used During the Outbreak of Ebola and SARS 120
7.2.1 Technological Strategies and Tools Used at the Time of SARS 120
7.2.2 Technological Strategies and Tools Used at the Time of Ebola 121
7.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis 124
7.3.1 Artificial Intelligence 124
7.3.1.1 Application of AI in Developed Countries 127
7.3.1.2 Application of AI in Developing Countries 128
7.3.2 IoT & Robotics 129
7.3.2.1 Application of IoT and Robotics in Developed Countries 130
7.3.2.2 Application of IoT and Robotics in Developing Countries 131
7.3.3 Telemedicine 131
7.3.3.1 Application of Telemedicine in Developed Countries 132
7.3.3.2 Application of Telemedicine in Developing Countries 133
7.3.4 Innovative Healthcare 133
7.3.4.1 Application of Innovative Healthcare in Developed Countries 134
7.3.4.2 Application of Innovative Healthcare in Developing Countries 134
7.3.4.3 Application of Innovative Healthcare in the Least Developed Countries 135
7.3.5 Nanotechnology 135
7.4 Conclusion 136
References 137
8 Advances in Technology: Preparedness for Handling Pandemic Challenges 143
Shweta Sinha and Vikas Thada
8.1 Introduction 143
8.2 Issues and Challenges Due to Pandemic 145
8.2.1 Health Effect 146
8.2.2 Economic Impact 147
8.2.3 Social Impact 148
8.3 Digital Technology and Pandemic 149
8.3.1 Digital Healthcare 149
8.3.2 Network and Connectivity 151
8.3.3 Development of Potential Treatment 151
8.3.4 Online Platform for Learning and Interaction 152
8.3.5 Contactless Payment 152
8.3.6 Entertainment 152
8.4 Application of Technology for Handling Pandemic 153
8.4.1 Technology for Preparedness and Response 153
8.4.2 Machine Learning for Pandemic Forecast 155
8.5 Challenges with Digital Healthcare 157
8.6 Conclusion 158
References 159
9 Emerging Technologies for COVID-19 163
Rohit Anand, Nidhi Sindhwani, Avinash Saini and Shubham
9.1 Introduction 163
9.2 Related Work 165
9.3 Technologies to Combat COVID-19 166
9.3.1 Blockchain 167
9.3.1.1 Challenges and Solutions 168
9.3.2 Unmanned Aerial Vehicle (UAV) 169
9.3.2.1 Challenges and Solutions 169
9.3.3 Mobile APK 170
9.3.3.1 Challenges and Solutions 170
9.3.4 Wearable Sensing 171
9.3.4.1 Challenges and Solutions 172
9.3.5 Internet of Healthcare Things 173
9.3.5.1 Challenges and Solutions 175
9.3.6 Artificial Intelligence 175
9.3.6.1 Challenges and Solutions 175
9.3.7 5G 176
9.3.7.1 Challenges and Solutions 176
9.3.8 Virtual Reality 176
9.3.8.1 Challenges and Solutions 177
9.4 Comparison of Various Technologies to Combat COVID-19 177
9.5 Conclusion 185
References 185
10 Emerging Techniques for Handling Pandemic Challenges 189
Ankur Gupta and Puneet Garg
10.1 Introduction to Pandemic 190
10.1.1 How Pandemic Spreads? 190
10.1.2 Background History 191
10.1.3 Corona 192
10.2 Technique Used to Handle Pandemic Challenges 194
10.2.1 Smart Techniques in Cities 194
10.2.2 Smart Technologies in Western Democracies 196
10.2.3 Techno- or Human-Driven Approach 197
10.3 Working Process of Techniques 197
10.4 Data Analysis 201
10.5 Rapid Development Structure 206
10.6 Conclusion & Future Scope 207
References 208
Part 3: Algorithmic Techniques for Handling Pandemic 211
11 A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling 213
Tan Nhat Pham and Son Vu Truong Dao
11.1 Introduction 213
11.2 Methodology 214
11.2.1 Data Collection 214
11.2.2 Mathematical Model Development 215
11.2.3 Proposed Hybrid Adaptive PSO-GWO (APGWO) Algorithm 217
11.2.4 Discrete Version of APGWO 219
11.2.4.1 Population Initialization 219
11.2.4.2 Discrete Search Operator for PSO Main Loop 223
11.2.4.3 Discrete Search Strategy for GWO Nested Loop 224
11.2.4.4 Constraint Handling 230
11.3 Computational Results 230
11.4 Conclusion 232
References 233
12 Multi-Purpose Robotic Sensing Device for Healthcare Services 237
HirakRanjan Das, Dinesh Bhatia, Ajan Patowary and Animesh Mishra
12.1 Introduction 238
12.2 Background and Objectives 238
12.3 The Functioning of Multi-Purpose Robot 239
12.4 Discussion and Conclusions 248
References 249
13 Prevalence of Internet of Things in Pandemic 251
Rishita Khurana and Madhulika Bhatia
13.1 Introduction 252
13.2 What is IoT? 255
13.2.1 History of IoT 255
13.2.2 Background of IoT for COVID-19 Pandemic 256
13.2.3 Operations Involved in IoT for COVID-19 257
13.2.4 How is IoT Helping in Overcoming the Difficult Phase of COVID-19? 257
13.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT 260
13.3.1 Smart Disease Surveillance Based on Internet of Things 261
13.3.1.1 Smart Disease Surveillance 261
13.3.2 IoT PCR for Spread Disease Monitoring and Controlling 263
13.4 Global Technological Developments to Overcome Cases of COVID-19 264
13.4.1 Noteworthy Applications of IoT for COVID-19 Pandemic 265
13.4.2 Key Benefits of Using IoT in COVID-19 269
13.4.3 A Last Word About Industrial Maintenance and IoT 270
13.4.4 Issues Faced While Implementing IoT in COVID-19 Pandemic 270
13.5 Results & Discussions 270
13.6 Conclusion 271
References 272
14 Mathematical Insight of COVID-19 Infection—A Modeling Approach 275
Komal Arora, Pooja Khurana, Deepak Kumar and Bhanu Sharma
14.1 Introduction 275
14.1.1 A Brief on Coronaviruses 276
14.2 Epidemiology and Etiology 277
14.3 Transmission of Infection and Available Treatments 278
14.4 COVID-19 Infection and Immune Responses 279
14.5 Mathematical Modeling 280
14.5.1 Simple Mathematical Models 281
14.5.1.1 Basic Model 281
14.5.1.2 Logistic Model 282
14.5.2 Differential Equations Models 283
14.5.2.1 Temporal Model (Linear Differential Equation Model, Logistic Model) 283
14.5.2.2 SIR Model 284
14.5.2.3 SEIR Model 285
14.5.2.4 Improved SEIR Model 287
14.5.3 Stochastic Models 288
14.5.3.1 Basic Model 288
14.5.3.2 Simple Stochastic SI Model 289
14.5.3.3 SIR Stochastic Differential Equations 290
14.5.3.4 SIR Continuous Time Markov Chain 290
14.5.3.5 Stochastic SIR Model 291
14.5.3.6 Stochastic SIR With Demography 292
14.6 Conclusion 292
References 293
15 Machine Learning: A Tool to Combat COVID-19 299
Shakti Arora, Vijay Anant Athavale and Tanvi Singh
15.1 Introduction 300
15.1.1 Recent Survey and Analysis 301
15.2 Our Contribution 303
15.3 State-Wise Data Set and Analysis 307
15.4 Neural Network 308
15.4.1 M5P Model Tree 309
15.5 Results and Discussion 309
15.6 Conclusion 314
15.7 Future Scope 314
References 314
Index 317
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