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Researches and Applications of Artificial Intelligence to Mitigate Pandemics - 1st Edition - ISBN: 9780323909594

Researches and Applications of Artificial Intelligence to Mitigate Pandemics

1st Edition

History, Diagnostic Tools, Epidemiology, Healthcare, and Technology

Editors: Kauser Hameed Surbhi Bhatia
Paperback ISBN: 9780323909594
Imprint: Academic Press
Published Date: 1st May 2021
Page Count: 200
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Description

Researches and Applications of Artificial Intelligence to Mitigate Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and Technology offers readers an interdisciplinary view of state-of-art research related to the COVID-19 outbreak, with a focus on models employed to model the number of cases of COVID-19 (time series modeling), models employed to diagnostics COVID-19 based on images, as well as, the panoramic of COVID-19 since its discovery up to the book publication. This book showcases the algorithms and models available to manage pandemic data. Healthcare delivery requires the collective competence of Artificial Intelligence (AI), Internet of Things (IoT), Epidemiology, Emotional Health and Machine Learning to fight and look ahead the given health emergency. This book reviews the role of AI, IoT, and Mathematical Modelling as a conclusive know-how to analyze, prevent and fight COVID-19; while considering the existing medical, social and pharmaceutical support as a combined approach in bringing the various expertise under a single title.

Methods and protocols are discussed throughout the book, especially in chapter 2 and chapter 3. The first chapter discusses the basics and history of the diseases, highlighting the details on coronavirus thoroughly for giving an insight to the general audience about the disease outbreak. The second chapter aims to investigate the fast diagnosis of the disease with different automated algorithms and artificial intelligence tools and techniques used to demystify the disease. The third chapter discuss the methods of handling epidemiology for mitigating the spread of the disease, artificial intelligence and mathematical modelling techniques will be employed in this chapter. The fourth chapter explains how mental and physical health is affected with the relationship with social media usage. The emotional intelligence and preventive measures will be discussed in the EQ computational model. The next chapter discusses technology and IOT in the context of nCov-17. The last chapter concludes the datasets, survey of ongoing research and the crosscut challenges in the field.

Key Features

  • Explains novel and hybrid high quality artificial intelligence methodologies, techniques, algorithms, architectures, tools and methods to cope with pandemics
  • Covers rapid point-of-care diagnostics, presents details on varied mathematical models developed to control epidemiology, and lists existing measures to disseminate the spread of infection using computational methods
  • Highlights the negative effect of social media and other sources by applying preventive measures to combat the state of depression and anxiety among community

Readership

Biomedical Engineers, Computer Scientists, Researchers in the fields of Statistics, Mathematics, AI, and Epidemiology

Table of Contents

Chapter 1: A Case of 2019-nCoV Novel Coronavirus Outbreak 
1.1 Introduction 
1.1.1 History of Coronaviruses 
1.1.2 2019-nCov 
1.1.3 Infectivity of nCov 
1.1.4 Clinical symptom and its effect 
1.1.5 Summary 
References 
1.2 Coping Strategies 
1.2.1 Necessary Precautions 
1.2.2 Appropriate Masks and its availability 
1.2.3 Role of Disinfectants 
1.2.4 Immunity Boosters 
1.1.2.5 Summary 
References 
1.3 Demystify Covid-19 
1.3.1 Suspicious symptoms 
1.3.2 Approaches for treatment available 
1.3.3 Medical Observations 
1.3.4 Infection Vs Pharmaceutics 
1.3.5 Reinfection after treatment 
1.3.6 Summary 
References 
1.4 Dispelling Rumors 
1.4.1 Young people and nCov 
1.4.2 Summary 

Chapter 2: Diagnostic tools and support systems for COVID-19 
2.1 Serology-based diagnosis 
2.1.1 Types of Serology Assays 
2.1.2 Uses and Benefits 
2.1.2 Conclusion 
2.2 Nucleic Acid Test based Diagnosis 
2.2.1 Real-time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR) 
2.2.2 RT-PCR Assay Procedure 
2.2.3 Uses and Benefits 
2.2.4 Conclusion 
2.2.5 References 
2.3 Radiography based Diagnosis 
2.3.1 Chest X-ray Imaging Modality 
2.3.2 Manual Diagnosis using Chest X-ray 
2.3.3 Computer-Aided Diagnosis (CAD) using Chest X-ray 
2.3.3.1 Public Datasets 
2.3.3.2 Conventional Image Processing 
2.3.3.3 Advanced Deep Learning Techniques 
2.3.3.4 Advantages and Disadvantages 
2.3.4 Chest X-ray based Diagnoses - Manual vs. CAD 
2.3.4.1 Case Study 
2.3.5 Chest CT-scan Imaging Modality 
2.3.6 Manual Diagnosis using Chest CT-scan 
2.3.7 Computer-Aided Diagnosis (CAD) using Chest CT-scan 
2.3.7.1 Public Datasets 
2.3.7.2 Conventional Image Processing 
2.3.7.3 Advanced Deep Learning Techniques 
2.3.7.4 Advantages and Disadvantages 
2.3.8 Chest CT-scan based Diagnoses - Manual vs. CAD 
2.3.8.1 Case Study 
2.3.9 Conclusion 
2.3.10 References 

Chapter 3: Epidemiology 
3.1 Introduction 
3.2 The Beginning of Mathematical Modeling in Epidemiology 
3.3 Mathematical Modeling Methodologies in Epidemiology 
3.4 The Philosophy of Mathematical Modeling 
3.4.1 Model Complexity 
3.4.2 Model Formulation and Hypothesis Testing 
3.5 The Nature of Epidemiological Data 
3.6. Childhood Micro-parasitic Infections 
3.7. A Simple Epidemic Model – Covid Case Studies 
3.7.1 Different Models 
3.7.2 Transmission Process 
3.7.3 Between-Compartment Flux of Individuals 
3.7.4 Deterministic Setup and Dynamics Analysis of Covid 
3.7.5 Statics and the Average Age at Infection 
3.7.6 Data Analysis Vs Covid Cases 
3.8.Summary 

Chapter 4: Emotional Health and Social Media Vs Sentiment Analysis 
4.1 Stigma 
4.2 Mental health 
4.2.1 Mobile health and serious mental illness 
4.2.2. Social media 
4.2.3. Social distancing 
4.2.4. Domestic crimes and EQ Support 
4.3 Preparing children for Pandemics 
4.4 EQ computational Model 
4.5 Conclusion 
4.6 References 

Chapter 5: Technology 
5.1 IOT in the context of nCov-17 
5.2 Smart technologies for fighting pandemics 
5.3 Conclusion 
5.4 References 
5.5 Techno and Human driven approaches in disease mitigation 
5.6 Case Studies - Discussion of various countries 

Chapter 6: Conclusions 
6.1 Datasets and Resources 
6.1.1 Use Case data 
6.1.2 Textual data 
6.1.3 Biomedical data 
6.1.4 Other supportive Datasets 
6.1.5 Competition Datasets 
Survey of Ongoing Research 
6.2.1 Image Data Analysis 
6.2.2 Video Data Analysis 
6.2.3 Audio Data Analysis 
6.2.4 Sensors Data Analysis (Drones) 
6.2.5 Drug Discovery Analysis 
6.3.1 Bibliometric Analysis of COVID-19 Research 
6.3.1 Bibliometric Data Collection 
6.3.2 Distribution of publications 
6.3.3 Research Topics 
6.3.4 Covid Vs earlier epidemics 
6.4 Cross-Cutting Challenges 
6.4.1 Data Limitation 
6.4.2 Results Vs Urgency Care 
6.4.3 Security and Privacy 
6.4.4 Need For Multidisciplinary Collaboration 
6.4.5 Solutions for controlling the Pandemic 
6.5 Summary 

Details

No. of pages:
200
Language:
English
Copyright:
© Academic Press 2021
Published:
1st May 2021
Imprint:
Academic Press
Paperback ISBN:
9780323909594

About the Editors

Kauser Hameed

Kauser Hameed, is a lecturer at King Faisal University –College of Computer Science - Information’s System department. She earned her Bachelor’s degree in Computer's Application from Osmania University, India in 2002 and completed her Master levels in Computer’s Application from the same campus in 2004. In more than a decade of her work experience, she hold promising and vivid work roles in multinational IT sectors such as GE Capitals and Sumtotal, while devoting her services in Academic later to that. Editing books, participating chapters and compiling academic videos is also one of her interesting diversions. In her career stretch at KFU she have successfully completed funded researches at NOOR Research Center, Taibah University and Deanship of Scientific Research, KFU. Currently, her research interests incline towards developing Mobile app, Full stact web develpment AI and NLP.

Affiliations and Expertise

Lecturer, King Faisal University, College of Computer Science, Inforamtion's System Department, Al-Ahsa, Saudi Arabia

Surbhi Bhatia

Dr. Bhatia completed her PhD in Computer Science and Engineering in 2018 from Banasthali Vidyapith University, Vanasthali, India. She is currently Assistant Professor in the Department of Information Systems at King Faisal University, Hofuf, Saudi Arabia. She has authored/edited 3 books, holds 7 patents, and has more than 25 research publications. Her research interests including data mining, machine learning, database management systems, and computer languages: C, C++, Python.

Affiliations and Expertise

Assistant Professor, Department of Information Systems, King Faisal University, Hofuf, Saudi Arabia

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