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

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 pand...

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Bibliographic Details
Main Authors Pani, Subhendu Kumar, Dash, Sujata, dos Santos, Wellington P, Chan Bukhari, Syed Ahmad, Flammini, Francesco
Format eBook
LanguageEnglish
Published Cham Springer International Publishing AG 2021
Springer International Publishing
Edition1
Subjects
Artificial Intelligence
Computer Science
Cyber-physical systems, IoT
Developmental Biology
Professional Computing
Public Health
Statistics, general
Online AccessGet full text
ISBN303079752X
9783030797522
DOI10.1007/978-3-030-79753-9

Cover

Table of Contents:
  • 4.1.1 Results Using Haralick for Feature Extraction
  • 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
  • Intro -- 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
  • 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
  • 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