Decision Sciences for COVID-19: Learning Through Case Studies

Preface
Acknowledgment
Introduction
Organization of the Book
Contents
About the Editors
Part I: Artificial Intelligence
Chapter 1: Application of Artificial Intelligence and Big Data for Fighting COVID-19 Pandemic
1.1 Introduction
1.2 Applications of Artificial Intelligence in Combat COVID-19 Pandemic
1.2.1 The Diagnosis and Prediction of COVID-19 Outbreak Using Artificial Intelligence
1.2.2 Artificial Intelligence in COVID-19 Outbreak Epidemiology
1.2.3 Artificial Intelligence in the Vaccine Development for COVID-19
1.3 Applications of Big Data in Fighting COVID-19 Pandemic
1.4 Application Challenges of Artificial Intelligence and Big Data for Fight COVID-19 Pandemic
1.5 Conclusion and Future Research Directions
References
Chapter 2: An IOT-Based COVID-19 Detector Using K-Nearest Neighbor
2.1 Introduction
2.2 Related Work
2.3 The Proposed Model Using k-Nearest Neighbor
2.4 The k-Nearest Neighbor (k-NN)
2.5 Classification of the COVID-19 Symptoms
2.6 Discussion and Result
2.7 Conclusion
References
Chapter 3: Predictive Analytics for Early Detection of COVID-19 by Fuzzy Logic
3.1 Introduction
3.2 Related Work
3.3 Need for Early Detection of Covid-19
3.3.1 More Frequent Signs
3.3.2 Common Signs
3.3.3 Severe Signs
3.4 About Fuzzy Logic
3.4.1 The Fuzzy Framework Contains
3.4.2 Membership Function
3.4.3 Fuzzifier
3.4.4 Fuzzy Control
3.4.5 Fuzzy Set
3.4.6 Defuzzification
3.5 Methodology
3.5.1 Identification of Parameters
3.5.2 Fuzzification (Membership Function)
3.5.3 Fuzzy Inference Method and Rules
3.5.4 Classification and Regression Trees (CART)
3.6 Implementation
3.7 Conclusion
3.8 Future Work
References
Chapter 4: Role of Artificial Intelligence in Diagnosis of Covid-19 Using CT-Scan
4.1 Introduction
4.2 Literature Review
4.2.1 What Is Covid-19 Disease?
4.2.2 New Techniques Used to Diagnose This Disease
4.2.3 How the Machine Learning and Deep Learning Are Used to Examine the Disease?
4.2.4 Chest CT-Image Detection
4.3 Methodology
4.4 Results
4.5 Conclusion and Future Work
References
Chapter 5: Predicting the Pandemic Effect of COVID 19 on the Nigeria Economic, Crude Oil as a Measure Parameter Using Machine ...
5.1 Introduction
5.2 Related Works
5.3 Methodology
5.3.1 Dataset
5.3.2 Machine Learning Models
5.3.2.1 Random Tree Model
5.3.2.2 Decision Table
5.3.2.3 Random Forest
5.3.2.4 M5P
5.3.2.5 Gaussian Processes
5.3.2.6 SMOreg
5.3.3 Proposed Model Used
5.3.4 Production, Crude Oil Price and Export from Central Bank of Nigeria
5.3.5 Covid 19 Nigeria Dashboard
5.4 Results and Discussion
5.4.1 Prediction Results
5.5 Conclusively
5.5.1 Limitation
References
Part II: Forecasting Techniques
Chapter 6: Time Series Analysis and Forecast of COVID-19 Pandemic
6.1 Introduction
6.2 Materials and Methods
6.2.1 Study Area
6.2.2 Data Collection
6.2.3 Methods
6.3 Results
6.4 Discussion
6.5 Conclusions
References
Chapter 7: A Multi-Step Predictive Model for COVID-19 Cases in Nigeria Using Machine Learning
7.1 Introduction
7.2 Related Work
7.3 Materials and Methods
7.3.1 Dataset
7.3.2 Data Preprocessing
7.3.3 Time Series
7.3.3.1 Time Series to Machine Learning Format
7.3.4 Regression Model
7.3.4.1 Linear Regression
7.3.4.2 Multilayer Perceptron
7.3.4.3 Support Vector Regression
7.3.4.4 k-Nearest Neighbor Regression
7.3.4.5 Random Forest Regressor
7.3.5 Metrics
7.4 Experimental Result and Discussion
7.4.1 Model Evaluations
7.4.2 Future Forecasts for 14-Step Ahead
7.4.3 Analysis on Lockdown Dates
7.4.4 Weekday and Monthly Analysis of COVID-19 Cases
7.4.5 Discussion
7.5 Conclusion and Future Research Directions
References
Chapter 8: Nigerian COVID-19 Incidence Modeling and Forecasting with Univariate Time Series Model
8.1 Introduction
8.2 Materials and Methods
8.2.1 Data Collection
8.2.2 Univariate Time Series Model
8.2.3 Autoregressive Integrated Moving Average (ARIMA) Model
8.2.3.1 Model Identification
8.2.3.2 Parameter Estimation
8.2.3.3 Diagnostic Checking
8.2.3.4 Forecasting
8.3 Result and Discussion
8.4 Conclusion
References
Chapter 9: Predicting the Spread of COVID-19 in Africa Using Facebook Prophet and Polynomial Regression
9.1 Introduction
9.2 Related Works
9.3 Research Methodology
9.3.1 Data Collection
9.3.2 Dataset Used
9.3.3 Polynomial Regressions Analysis
9.3.4 Facebook Prophet
9.4 Results and Discussion
9.5 Performance Metrics Measure
9.6 Conclusion
References
Chapter 10: Comparing Predictive Accuracy of COVID-19 Prediction Models: A Case Study
10.1 Introduction
10.2 Prognostic Models of the Epidemic Curves
10.2.1 Prognostic Models for COVID-19 Outbreak
10.3 Nonparametric Tests for Prognostic Models Estimation
10.3.1 Nonparametric Tests for Samples Homogeneity
10.3.2 Original Version of the Klyushin-Petunin Test
10.3.3 Simplified Version of the Klyushin-Petunin Test
10.4 Case Study
10.5 Conclusion and Scope for the Future Work
References
Part III: Social Sciences
Chapter 11: AIC Algorithm for Entrepreneurial Intention in Covid19 Pandemic
11.1 Introduction
11.2 Literature Review
11.2.1 Entrepreneurial Intention (EI)
11.2.2 Factors Affecting Entrepreneurial Intention
11.3 Methods
11.3.1 Research Approach
11.3.2 Blinding
11.3.3 AIC in Model Selection
11.4 Results
11.4.1 Akaike Information Criterion Selection
11.4.2 Variance Inflation Factor
11.4.3 Heteroskesdaticity
11.4.4 Autocorrelation
11.4.5 Model Evaluation
11.4.6 Discussion
11.5 Conclusion
References
Chapter 12: Sentiment Analysis for COVID Vaccinations Using Twitter: Text Clustering of Positive and Negative Sentiments
12.1 Introduction
12.2 Literature Study
12.3 Data Source
12.4 Results
12.4.1 Data Cleanup Steps
12.4.2 Sentiment Analysis
12.4.3 Text Clustering
12.4.3.1 Text Clustering of Negative Sentiments
12.4.3.2 Text Clustering of Positive Sentiments
12.5 Discussion
12.6 Conclusion
References
Chapter 13: Participation and Active Contribution of Private Universities in the Prevention of the Covid-19 Pandemic Transmiss...
13.1 Introduction
13.2 Methodology
13.2.1 Research Approach
13.2.2 Research Setting
13.2.3 Research Participant
13.2.4 Data Collection
13.2.5 Data Analysis
13.3 Results and Discussion
13.4 Conclusion
References
Chapter 14: Effects on Mental Health by the Coronavirus Disease 2019 (COVID-19) Pandemic Outbreak
14.1 Introduction
14.1.1 History and Indications of Coronavirus
14.1.2 COVID-19: Protection and Transferring Behavior
14.1.3 COVID-19 and Mental Health
14.2 Effects on Mental Health of Civilizations by Coronavirus Pandemic
14.2.1 Annotation, Readiness, and Initiative for Controlling the Infection
14.2.2 Mental Health Influence on Survivors, Fitness Maintenance Workforce, Rule Implementation Representatives, Children and ...
14.2.3 Mental Health Impact on Elderly
14.2.3.1 Suggested Proposals to Decrease the Jeopardy of Mental Health Influence and Thoughtful Illnesses in the Senior
14.2.4 Effects of Quarantine and Social Distancing on Mental Health Impact
14.2.4.1 Suggested Proposals to Decrease the Effects of Quarantine and Social Distancing
14.2.5 Mental Health Impact on Students
14.2.5.1 Suggested Proposals to Decrease the Effects of Mental Health Impact on Students
14.2.6 Mental Pressure of Productivity and Stigma
14.2.6.1 Suggested Proposals to Decrease the Effects of Mental Pressure of Productivity and Stigma
14.2.7 Mental Health Impact on Vulnerable Population
14.2.7.1 Suggested Proposals to Decrease the Effects of Mental Health Impact on Vulnerable Population
14.3 Management of the COVID-19 Infodemic: Endorsing Healthy Performances and Moderating the Harm from Misinformation and Disi...
14.3.1 Preserving Resilience, Coping, Mindfulness, and Welfare
14.3.2 Prevention of (Mis) Infodemic, Disinformation, and Misinformation
14.3.3 PCI (Psychological Crisis Intervention) and PFA (Psychological First Aid)
14.3.4 Prospective Policies
14.4 Problem Analysis
14.5 Conclusions
References
Chapter 15: Multimodal Analysis of Cognitive and Social Psychology Effects of COVID-19 Victims
15.1 Introduction
15.2 Related Work
15.3 About Psychological and Cognitive Feelings of Patients
15.3.1 The Coronavirus Disease Psychological Effects
15.3.2 COVID´s Susceptible Psychological Consequences
15.3.3 COVID Virus Time Series Consequences
15.3.4 Patient Privacy Aspects
15.3.4.1 Resilientness
15.3.4.2 Community Support
15.3.5 Precautionary Approaches
15.3.6 COVID-19 Patients´ Significant Psychological Effects
15.3.7 COVID-19 Impacts on Neurological and Wellness
15.4 Methodology
15.5 Experiment Analysis
15.6 Discussion
15.7 Conclusion
15.8 Future Enhancement
References
Chapter 16: Public Transport Passenger´s Density Estimation Tool for Supporting Policy Responses for COVID-19
16.1 Introduction
16.2 Understanding the Context
16.2.1 Traffic Data Available for the Aburr Valley Region
16.2.2 The Relationship Between Contagion and Public Transport Passenger´s Density
16.3 Software Tool Description
16.3.1 Description of the Algorithm Input Data
16.3.1.1 Origin-Destination Matrix
16.3.1.2 Population Data
16.3.1.3 Public Transport Systems Features
16.3.2 Software Architecture
16.4 Main Results
16.5 General Conclusions
References
Part IV: Optimization Techniques
Chapter 17: A Generalized Model for Scheduling Multi-Objective Multiple Shuttle Ambulance Vehicles to Evacuate COVID-19 Quaran...
17.1 Introduction
17.2 Scheduling Shuttle Ambulance Problem
17.3 Multi-Objective Multiple Knapsack Problem: A Literature Review
17.4 Mathematical Model for the Problem
17.5 An Illustrated Case Study
17.6 Conclusions and Points for Future Researches
References
Chapter 18: Hyperparameters Optimization of Deep Convolutional Neural Network for Detecting COVID-19 Using Differential Evolut...
18.1 Introduction
18.2 Related Work
18.3 Theory and Methods
18.3.1 Differential Evolution Algorithm
18.3.1.1 Initialization
18.3.1.2 Mutation
18.3.1.3 Crossover
18.3.1.4 Selection
18.3.2 Convolutional Neural Network (CNN)
18.4 The Proposed Framework
18.5 Experimentation
18.5.1 CNN Using DE
18.5.2 CNN without Using DE
18.5.3 Comparison and Analysis
18.6 Conclusion
References
Part V: Data Science
Chapter 19: Quality Design for the COVID-19 Pandemic: Use of a Web Scraping Technique on Text Comments and Quality Ratings fro...
19.1 Introduction
19.2 Literature Review
19.3 Methods
19.3.1 Data Collection
19.4 Results
19.4.1 Passenger Reviews
19.4.2 Text Analysis
19.4.3 Text Analysis for Quality Differentiation: Post-Pandemic Results
19.5 Conclusion
References
Chapter 20: Data Science Models for Short-Term Forecast of COVID-19 Spread in Nigeria
20.1 Introduction
20.2 Related Work
20.3 Methodology
20.3.1 Data Collection
20.3.2 Explorative Analysis of the Dataset (Table 20.1)
20.3.3 Machine Learning Models
20.3.3.1 Regression Model
20.3.3.2 Support Vector Machine
20.4 Implementation and Results
20.4.1 Analysis COVID-19 in Nigeria
20.4.2 Comparative Analysis COVID-19 Nigeria and Selected African Countries
20.4.3 Results of the Models
20.4.4 Discussion
20.5 Conclusion
References
Part VI: COVID-19 Detection
Chapter 21: Attention-Based Residual Learning Network for COVID-19 Detection Using Chest CT Images
21.1 Introduction
21.2 Related Works
21.2.1 Research Gaps and Motivation
21.2.2 Research Contributions
21.3 Materials and Methods
21.3.1 Data Acquisition
21.3.2 Data Augmentation
21.3.3 Analysis of the Architecture of Deep Learning Models
21.3.3.1 AlexNet
21.3.3.2 DenseNet
21.3.3.3 GoogLeNet
21.3.3.4 InceptionV4
21.3.3.5 ResNet
21.3.3.6 ShuffleNet
21.3.3.7 SqueezeNet
21.3.3.8 VGGNet
21.3.4 Proposed System
21.4 Results and Discussion
21.4.1 Experimental Setup
21.4.2 Analysis of Backbone Architecture
21.4.3 Cross-Validation Performance for Proposed Network
21.5 Conclusion
References
Chapter 22: COVID-19 Face Mask Detection Using CNN and Transfer Learning
22.1 Introduction
22.2 Literature Review
22.3 Dataset Used
22.4 Proposed Methodology
22.4.1 Augmentation of Images
22.4.2 Convolutional Neural Network
22.4.2.1 Transfer Learning
22.5 Results
22.6 Using Learning Rate
22.7 Output
22.8 Result Interpretation
22.9 Conclusion
References
Chapter 23: LASSO-DT Based Classification Technique for Discovery of COVID-19 Disease Using Chest X-Ray Images
23.1 Introduction
23.2 Literature Review
23.3 Material and Method
23.3.1 Dataset
23.3.2 Proposed System
23.3.3 Feature Selection
23.3.4 Classification Technique
23.3.4.1 Decision Tree
23.3.5 Performance Analysis
23.4 Result and Discussion
23.5 Conclusion
References
Part VII: Economy
Chapter 24: Economic Policies for the COVID-19 Pandemic: Lessons from the Great Recession
24.1 Introduction
24.2 Relevance of the Study and Previous Literature
24.3 What Lessons Can Be Learned from the Lending Behavior before the Great Recession? A Theoretical Approach
24.4 Methodology
24.5 Econometric Model and Data
24.6 Results
24.7 Mechanisms and Discussion
24.8 Results from the COVID Times and Proposed Economic Policies
24.9 Concluding Remarks
Appendix I
Appendix II
References
Chapter 25: QOL Barometer for the Well-being of Citizens: Leverages during Critical Emergencies and Pandemic Disasters
25.1 Introduction
25.1.1 Idea of Quality of Life
25.1.2 QOL as a Part of Holistic Development
25.1.3 Socioeconomic Changing Roles and New Dimensions of Governance: Good Governance and E-Governance
25.1.4 Revolution in E-Governance Ecosystem across the World
25.1.5 Recent Experience of E-Governance Protocol for Mitigating COVID Pandemic
25.2 Literature Review
25.2.1 Measuring Quality of Life
25.2.2 Governance and E-Governance
25.2.3 Changing Scenarios of E-Governance and Digital Divide in COVID Pandemic
25.3 Objectives
25.4 Research Methodology
25.4.1 Analysis I
25.4.2 Analysis II
25.5 Conclusion
References