Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis

✍ Scribed by Subhendu Kumar Pani (editor), Sujata Dash (editor), Wellington P. dos Santos (editor), Syed Ahmad Chan Bukhari (editor), Francesco Flammini (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 416
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

This book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. In the past, it was a common requirement to have domain experts for developing models for biomedical or healthcare. However, recent advances in representation learning algorithms allow us to automatically learn the pattern and representation of the given data for the development of such models. Medical Image Mining, a novel research area (due to its large amount of medical images) are increasingly generated and stored digitally. These images are mainly in the form of: computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new and useful information that can be helpful for scientists and biomedical practitioners.

Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence..

✦ Table of Contents

Preface
Overview
Objective
Organisation
Target Audiences
Acknowledgements
Contents
Contributors
Abbreviations
Editors' Biography
Artificial Intelligence (AI) and Big Data Analytics for the COVID-19 Pandemic
1 Introduction
2 AI for COVID-19 Pandemic
2.1 Screening and Treatment
2.2 Contact Tracing
2.3 Prediction and Forecasting
2.4 Molecular Study and Drug Design
3 Big Data Analytics for COVID-19 Pandemic
3.1 Screening and Treatment
3.2 Contact Tracing
3.3 Prediction and Forecasting
3.4 Molecular Study and Drug Design
4 Existing Challenges and the Way Forward
5 Conclusion
References
COVID-19 TravelCover: Post-Lockdown Smart Transportation Management System
1 Introduction
1.1 Problem Statement
1.2 Related Works
1.3 Scope and Objective
1.4 Novelty
1.5 Scientific Contribution
2 Proposed Methodology
2.1 COVID-19 TravelCover Architecture
2.2 Software Designing
3 Algorithm and Working
3.1 Route Allocation
3.2 Fare Calculation
3.3 Unique Ticket Number Generation
3.4 Ticket Validation Through Mask Detection
3.5 Security Features
3.6 Imposing Guidelines
3.7 Customer-First Approach
3.8 User Interface (UI)
4 Result and Discussion
4.1 Fare Calculation
4.2 Validation of Tickets
4.3 Security Features
4.4 Priority
5 Conclusion and Future Scope
References
Diverse Techniques Applied for Effective Diagnosis of COVID-19
1 Introduction
2 General Overview on COVID-19
3 COVID-19 and Mental Health
4 COVID-19 Diagnosis and Management
5 Different Comprehensive Techniques for Rapid Detection of COVID-19
6 Performance of Several Laboratory Diagnostic Evaluations and Platforms
7 Alternative Methods for the SARS-CoV-2 Detection
8 CRISPR-Based Techniques
9 DNA Endonuclease Targeted CRISPR Trans Reporter (DETECTR)
10 CAS 13-Based Rugged Equitable Scalable Testing (CREST)
10.1 Amplification-Free Assay
10.2 Specific High Enzymatic Reporter Unlocking
10.3 Post Analysis Phase
10.4 RNA Aptamers
10.5 Next-Generation Sequencing (NGS)
11 Molecular Diagnostic Techniques for COVID-19
11.1 Preliminary Phase
11.2 Analysis Phase
11.3 Loop-Mediated Isothermal Amplification (LMIA)
12 Conclusion and Future Perspectives
References
A Review on Detection of COVID-19 Patients Using Deep Learning Techniques
1 Introduction
2 Methodology/Article Selection
3 Review of Literature
4 Discussion
5 Challenges
6 Conclusion
References
Internet of Health Things (IoHT) for COVID-19
1 Introduction
2 Relevant Facts About COVID-19
3 COVID-19 and Mental Health
4 COVID-19 Diagnosis and Management
5 Performance of Several Laboratory Diagnostic Evaluations
6 Healthcare Systems
7 IoT-Based Technologies
8 Utilization of IoT-Based Technologies in Data Acquisition
9 IoT-Based Technologies and Healthcare Systems
10 Specific Authors That Have Worked on the Application of Internet of Things (IoT) in the Management of COVID-19 Diseases
11 Conclusion and Future Perspectives
References
Diagnosis for COVID-19
1 Introduction
1.1 Classification of Coronavirus (CoVs)
1.2 Genomic Organization and Structure of Coronavirus (SARS-CoV-2)
2 Manifestations and Epidemiology of SARS-CoV-2
2.1 Origin of SARS-CoV-2
2.2 Symptoms of SARS-CoV-2
2.3 Incubation Period of SARS-CoV-2
2.4 Transmission of SARS-CoV-2
3 Diagnostic for SARS-CoV-2
3.1 SARS-CoV-2 RNA Detection
3.2 Adequate Specimen Collection
3.3 Simplified Specimen Collection
3.4 Serum Specimens
3.5 Fecal Specimens
3.6 Postmortem Specimens
4 Testing of COVID-19 (SARS-CoV-2)
4.1 Nucleic Acid Amplification Test (NAAT)
4.2 NAAT by Pooling Specimens
4.3 Testing Antibodies
4.4 Antigen Detection (Rapid Diagnostic Tests—RDTs)
5 Treatments for SARS-CoV-2
5.1 Monoclonal Antibodies
5.2 Chloroquine and Hydroxychloroquine
5.3 Plasma Transfusion
5.4 Corticosteroids
5.5 Vaccines
6 Vaccine Development
7 Conclusions
References
IoT in Combating COVID-19 Pandemics: Lessons for Developing Countries
1 Introduction
2 Problem Statement
3 Literature Review
4 Methodology
5 Practical Applications of IoT in Combating COVID-19
5.1 Prediction and Spread Prevention
5.2 Treatment
5.3 Direction and Prospect of IoT
6 IoT—Challenges and Opportunities
7 Conclusion
8 Limitations and Scope of Future Work
References
Machine Learning Approaches for COVID-19 Pandemic
1 Introduction
2 Machine Learning and Artificial Intelligence
3 Machine Learning (ML)/Artificial Intelligence (AL) and COVID-19
4 Computer Communication-Assisted Diagnosis of COVID-19
5 Specific Authors Who Have Applied Machine Learning, Artificial Intelligence, and Smart Sensing for COVID-19
6 Conclusion and Future Perspectives
References
Smart Sensing for COVID-19 Pandemic
1 Introduction
2 Application of Sensors and Biosensors for Monitoring and Detection of COVID-19
3 Application of Drone Technology-Driven Technology and Robotics in Supporting Disinfection Process, Surveillance, and Health System
4 Application of Drone-Driven Technology in Supporting Disinfectant Process, Surveillance, and Health System
5 Application in Data Collection
6 Application in Aerial Disinfection
7 Application in Transportation of Medical Materials
8 Policy Monitoring and Surveillance
9 Dissemination of Information During COVID-19
10 Application of Smart Technology in Medical Assistance, Forecasting Infection Threats, Investigating Diagnosis
11 Application in the Diagnosis and Rehabilitation
12 Collection of COVID-19 Sample
13 Sanitation, Safety, and Management of COVID-19 Situation
14 Application of Robotics in Protecting People During Pandemic Period
15 Measurement of Vital Signs
16 Conclusion and Future Directions
References
eHealth, mHealth, and Telemedicine for COVID-19 Pandemic
1 Introduction
2 eHealth, mHealth, and Telemedicine Applications and COVID-19 outside of Africa
3 eHealth, mHealth, and Telemedicine Applications and COVID-19 within Africa
4 Application of eHealth, mHealth, Telemedicine, and Pandemic
5 Specific Authors That Worked on COVID-19 Pandemic Using eHealth, mHealth, and Telemedicine
6 Role of Telemedicine in Early Detection and Control of COVID-19 Disease
7 High-Speed Telecommunications Systems and COVID-19
8 Application of eHealth, mHealth, Telemedicine, and Clinical Practice
9 Conclusion and Future Perspectives
References
Prediction of Care for Patients in a COVID-19 Pandemic Situation Based on Hematological Parameters
1 Introduction
2 Theoretical Foundation
2.1 COVID-19
2.2 Machine Learning
3 Related Works
4 Methods
5 Results
5.1 Regular Ward Hospitalization
5.2 Semi-Intensive Care Unit Hospitalization
5.3 Intensive Care Unit Hospitalization
6 Discussion
7 Conclusion
References
Bioinformatics in Diagnosis of COVID-19
1 Introduction
2 Detection and Annotation
2.1 UniProt COVID-19 Protein Portal
2.2 Rfam COVID-19 Resources
2.3 Viral Annotation DefineR: SARS-CoV-2 Genome Interpretation and Validation
3 Tracking, Epidemiology, and Evolution
3.1 Covidex
3.2 Covid Simulation Tool (CovidSIM): Epidemiological Models of Viral Spread
4 Drug Design
4.1 VirHostNet SARS-CoV-2 Release
4.2 P-HIPSTer
5 Drug Design for COVID
5.1 CORDITE: CORona Drug InTERactions Database
5.2 CoVex: Coronavirus Explorer
6 Conclusion
References
COVID-19 Detection Using Discrete Particle Swarm Optimization Clustering with Image Processing
1 Introduction
2 Preliminaries
2.1 Overview of Particle Swarm Optimization
2.2 Particle Swarm Optimization Clustering
3 Reviews of Literature
4 Proposed Methodology
4.1 Pre-processing
4.2 Segmentation
4.2.1 Particle Swarm Optimization Clustering
4.2.2 Fitness Measures
4.3 Feature Extraction
5 Experimental Result
6 Conclusion
References
LSTM-CNN Deep Learning–Based Hybrid System for Real-Time COVID-19 Data Analysis and Prediction Using TwitterData
1 Introduction
2 Literature Review
3 Materials and Methods
4 Detailed Architecture of the Proposed Model
4.1 Pre-processing
4.2 Deep Neural Network
4.3 LSTM Models
4.4 Convolutional Neural Networks (CNNs)
4.5 LSTM-CNN Model
4.6 Twitter Dataset
5 Experimental Result Analysis
5.1 Experiment
5.2 Parameter Selection
5.3 Selection of Different ML Models
5.4 Result Analysis
5.5 Evaluation Metrics
6 Discussion of the Work
7 Conclusion, Limitations, and Future Work
References
An Intelligent Tool to Support Diagnosis of Covid-19 by Texture Analysis of Computerized Tomography X-ray Images and Machine Learning
1 Introduction
2 Related Works
3 Methods
3.1 Proposed Method
3.2 Dataset
3.3 Feature Extraction: Haralick and Zernike
3.4 Classification
3.4.1 Multilayer Perceptron
3.4.2 Support Vector Machine
3.4.3 Decision Trees
3.4.4 Bayesian Network and Naive Bayes
3.4.5 Parameters Settings of the Classifiers
3.5 Metrics
4 Results
4.1 Classifiers Experiments Results
4.1.1 Results Using Haralick for Feature Extraction
4.1.2 Results Using Haralick and Zernike for Feature Extraction
5 Discussion
6 Conclusion
Disclosure Statement
Compliance with Ethical Standards
References
Analysis of Blockchain-Backed COVID-19 Data
1 Introduction
2 Related Work
3 Motivation
4 Methodology and Implementation
4.1 Data Collection
4.1.1 ECDC Covid-19 Data
4.1.2 Mipasa ECDC Covid-19 Blockchain Data
4.1.3 The COVID Tracking Project
4.1.4 The-Mipasa's-COVID-Tracking-Project
4.1.5 Mipasa's COVID-19 in Japan
4.1.6 COVID-19-Dataset-in-Japan-From-Kaggle
4.2 Analysis
4.3 Implementation
5 Results
6 Conclusion
References
Intelligent Systems for Dengue, Chikungunya, and Zika Temporal and Spatio-Temporal Forecasting: A Contribution and a Brief Review
1 Introduction
2 Forecasting by Statistical Learning and Compartment Models
3 Forecasting by Machine Learning and Hybrid Models
4 Conclusion
References
Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution
1 Introduction
2 Forecasting by Statistical Learning and Compartment Models
3 Forecasting by Machine Learning and Hybrid Approaches
4 Conclusions
References
Image Reconstruction for COVID-19 Using Multifrequency Electrical Impedance Tomography
1 Introduction
2 Hardwares EIT and MfEIT
3 Types of Images Generated
4 Forward Problem and Inverse Problem in MfEIT
5 Usual Software and Methods for Image Reconstruction
6 EIT Applications with Respect to COVID-19
7 D-Bar Method
8 Future of MfEIT
9 Methods and Materials
10 Discussions and Results
11 Conclusion
References