Mathematical Modeling and Intelligent Control for Combating Pandemics (Springer Optimization and Its Applications, 203)

✍ Scribed by Zakia Hammouch (editor), Mohamed Lahby (editor), Dumitru Baleanu (editor)

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 278
• Category: Library

✦ Synopsis

The contributions in this carefully curated volume, present cutting-edge research in applied mathematical modeling for combating COVID-19 and other potential pandemics. Mathematical modeling and intelligent control have emerged as powerful computational models and have shown significant success in combating any pandemic. These models can be used to understand how COVID-19 or other pandemics can spread, analyze data on the incidence of infectious diseases, and predict possible future scenarios concerning pandemics. This book also discusses new models, practical solutions, and technological advances related to detecting and analyzing COVID-19 and other pandemics based on intelligent control systems that assist decision-makers, managers, professionals, and researchers. Much of the book focuses on preparing the scientific community for the next pandemic, particularly the application of mathematical modeling and intelligent control for combating the Monkeypox virus and Langya Henipavirus.

✦ Table of Contents

Preface
Contents
About the Editors
Part I Mathematical Modelling and Analysis for Covid-19 Pandemic
An Extended Fractional SEIR Model to Predict the Spreading Behavior of COVID-19 Disease using Monte Carlo Back Sampling
1 Introduction
2 Preliminaries
2.1 Compartmental Models
2.2 Fractional Derivatives
3 Extended SEIR Model
3.1 Model Formation
3.2 Numerical Solution
4 Monte Carlo Back Sampling
5 Application of the Fractional Extended SEIR Model
6 Conclusion
References
Dynamics and Optimal Control Methods for the COVID-19 Model
1 Introduction
2 Description of the Model
3 Qualitative Analysis of the Model
3.1 The Solution's Existence and Singularity
3.2 Local Dynamic of the Covid-19 Free Equilibrium
3.3 The Effective Reproduction Number R0
3.4 Global Dynamic of DFE
3.5 Equilibrium Endemic Stability
4 Model Sensibility
5 The Controlled Mathematical Model
5.1 The Optimal Control Problem
5.2 Characterization of the Optimal Control
6 Numerical Simulation and Discussions
7 Conclusion
References
Optimal Strategies to Prevent COVID-19 from Becoming a Pandemic
1 Introduction
1.1 Preliminaries
1.2 Reservoir–People Transmission Biological Network Model
2 Formulation of FOCPs and Its Optimality Systems
2.1 Optimality Systems
3 Numerical Results and Discussion
3.1 Single Control Strategy
3.2 Double Control Strategy
3.2.1 Strategy 1
3.2.2 Strategy 2
3.2.3 Strategy 3
3.3 Triple Control Strategy
3.4 Comparative Analysis
4 Conclusions
References
Modeling and Analysis of COVID-19 Based on a Deterministic Compartmental Model and Bayesian Inference
1 Introduction
2 Modeling Framework
3 Model Calibration
3.1 Curve Fitting Method
3.2 Bayesian Inference Method
3.2.1 Observation Model
3.2.2 Prior Specification
3.2.3 Simulations
3.2.4 Results
4 Vaccination Impacts
5 Conclusion
References
Predicting the Infection Level of COVID-19 Virus Using Normal Distribution-Based Approximation Model and PSO
1 Introduction
2 Related Works
3 Background
4 Problem Statement 1: How to Calculate Infection Level (IL) by Coronavırus in the Human Body
4.1 Proposed Solution
4.2 Example
5 Problem Statement 2: How to Calculate Total Infection (TI) by Coronavırus in the Human Body by Considering Risk Factors
5.1 Total Infection (TI)
6 Infection Level Predictor (ILP) Algorithm
7 Model Validation
8 Background of PSO
9 Experimental Results of PSO
10 Comparisons of ILP and PSO
11 Conclusion and Future Directions
References
An Optimal Vaccination Scenario for COVID-19 Transmission Between Children and Adults
1 Introduction
2 Model Description
3 Problem Formulation with Vaccination Control
3.1 Description of Cost Function
3.2 Existence of Optimal Control
3.3 Characterization of Optimal Control
4 Numerical Simulations and Discussion
5 Concluding Remarks
References
Part II Intelligent Control Techniques and Covid-19 Pandemic
The Role of Artificial Intelligence and Machine Learning for the Fight Against COVID-19
1 How COVID-19 Pandemic Affected Our Lives and It Is Still Doing So
2 Artificial Intelligence and Machine Learning Against COVID-19
3 AI and ML for COVID-19 Diagnosis
4 AI and ML for COVID-19 Drug Discovery and Repurposing
5 AI and ML for COVID-19 Forecasting
6 Conclusion
References
Coronavirus Lung Image Classification with Uncertainty Estimation Using Bayesian Convolutional Neural Networks
1 Introduction
1.1 Problem Statement
2 Related Work
2.1 Nonprobabilistic and Probabilistic Classification
2.2 Bayesian Neural Networks
2.3 Bayesian Learning
2.4 Variational Inference
3 Research Methodology
3.1 Probabilistic Framework
3.1.1 Why Probabilistic Framework
3.2 Dataset Description
3.3 Research Design
3.4 Modeling Process
3.4.1 Loading the COVID-Radiography Image Dataset
3.4.2 Creating Standard CNN Model
3.4.3 Creating Bayesian CNN Model
4 Analysis and Results
4.1 Introduction
4.2 Comparisons of Two Models Output
4.3 Predicting Images from Test Data
5 Conclusion
References
Identify Unfavorable COVID Medicine Reactions from the Three-Dimensional Structure by Employing Convolutional Neural Network
1 Introduction
2 Related Work
3 The Proposed Procedure and Dataset
3.1 Dataset
3.1.1 Medicine
3.1.2 Three-Dimensional Structure of Medicine
3.1.3 Unfavorable COVID Medicine Reactions
3.2 Problem Statement
3.3 The Proposed Architecture
4 Setup and Results of the Experiment
4.1 Performance Measure Definition
4.2 Setup of the Experiment
4.3 Results and Discussions
4.4 ROC-AUC Curve
5 Conclusion
References
Using Reinforcement Learning for Optimizing COVID-19 Vaccine Distribution Strategies
1 Introduction
2 Reinforcement Learning (RL)
2.1 Definition and Key Concepts
2.2 Main Concepts and Methods in Reinforcement Learning Domain
2.3 Applications of RL
2.4 Overview of RL Approaches to Vaccine Distribution
3 Illustrative Example of a RL-Based System for Vaccine Allocation and Distribution
3.1 Generic Architecture
3.2 Classes of the System
3.3 Components of the System
3.4 Typical Operations of the System
3.5 Deployment
3.6 Incorporating Domain-Specific Knowledge and Constraints
4 Evaluation of RL for Vaccine Distribution
4.1 Benefits of RL for Vaccine Distribution
4.2 Challenges and Limitations of RL
4.3 Limitations and Risks of RL for Vaccine Distribution
4.4 Ethical and Social Implications of RL for Vaccine Distribution
5 Answers to Research Questions
5.1 Research Question 1: How Has RL Been Applied to Vaccine Distribution?
5.2 Research Question 2: What Are the Potential Benefits and Limitations of Using RL for Vaccine Distribution?
5.3 Research Question 3: What Is the Methodology for Using RL for Optimizing COVID-19 Vaccine Distribution Strategies, and What Are the Key Steps and Components Involved in This Process?
6 Future Directions for Research and Development
7 Conclusion
References
Incorporating Contextual Information and Feature Fuzzification for Effective Personalized Healthcare Recommender System
1 Introduction
2 Literature Review
3 Proposed Recommendation Framework
3.1 Phase 1 – Patient Profile Formation
3.2 Phase 2 – Similarity Computation and Neighborhood Set Formation
3.3 Phase 3 – Prediction and Recommendations
4 Experiments and Results
4.1 Experimental Settings
4.2 Experimental Results and Discussion
5 Conclusion
References
Prediction of Growth and Review of Factors Influencing the Transmission of COVID-19
1 Introduction
2 Review: Factors Influencing the Transmission of COVID-19
2.1 Effect of Temperature and Humidity
2.2 Effect of Population and Social Distancing
2.3 Effect of Population Density
2.4 Effect of Air Pollution
2.5 Effect of Other Factors
3 Methods Based on Computational Intelligence to Predict COVID-19
3.1 Fuzzy Sets
3.2 Artificial Neural Networks
3.3 Evolutionary Computing
3.4 Swarm Intelligence
4 Method to Predict Exponential Growth of Infected Cases
5 Results and Discussions
6 Findings and Conclusion
References
COVID-19 Combating Strategies and Associated Variables for Its Transmission: An Approach with Multi-Criteria Decision-Making Techniques in the Indian Context
1 Introduction
2 Literature Review
2.1 COVID-19's Vaccinations
2.2 COVID-19's Transmission Variables
2.2.1 Climate-Related Variables
2.2.2 Safety- and Hygiene-Related Variables
2.2.3 Making Decisions with Attentiveness
2.2.4 Social- and Demographic-Variables
2.2.5 Psychological-Related Variables
2.3 Vaccination's Reluctances in India
3 Research Methodology
3.1 The Associated Variables and Sub-variables Identification for the COVID-19 Pandemic Transmission
3.2 The Actions Undertaken in BWM
3.3 The Stages Undertaken in SWARA
4 Results
4.1 Ranking of the Available Vaccine's Preferences in India
4.2 Ranking of the COVID-19 Transmission Variables and Corresponding Sub-variables
4.2.1 Variable's Ranking by BWM
4.2.2 Sub-variable's Ranking by SWARA
5 Discussion
6 Conclusion
References
Crisis Management, Internet, and AI: Information in the Age of COVID-19 and Future Pandemics
1 Introduction
2 Monitoring the Content: The Use of AI Against Internet Misinformation During COVID-19
3 Exploiting the Content: The Use of AI and Social Media to Manage Information in the Case of a Global Crisis
4 Conclusion and Future Research Recommendations
References
Index