Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations.Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.
Author(s): Joao Alexandre Lobo Marques, Simon James Fong
Publisher: Springer
Year: 2022
Language: English
Pages: 160
City: Cham
Contents
Research and Technology Development Achievements During the COVID-19 Pandemic—An Overview
1 Introduction
2 WHO Guidelines and Support
2.1 Immediate Research Actions
2.2 Research Areas/Knowledge Gaps
3 The Collaboration Network
3.1 A Case of Research Collaboration
4 Technology Development—The New Vaccines
5 Bibliometrics in COVID-19 Research
6 Ethical Considerations for COVID-19 Research
6.1 Quality of Research
7 Impacts and Next Steps
References
Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models
1 Introduction
2 Analysis and Setting of the Initial Parameters
2.1 Variables for Parameter Setting: Total Population
3 Dynamic Analysis of COVID19 Outbreak in Singapore Based on Adaptive SEAIRD Model
3.1 Establishment of Simulation Model
3.2 Simulation of Singapore Pandemic and Forecast Analysis of Turning Point
3.3 Further Improved Model
3.4 Prevention and Control Measures
4 Conclusions
References
The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak
1 Introduction
2 Materials and Methods
2.1 Presenting the GROOMS Method
2.2 The Proposed Solution: PNN + CF
3 A Case Study from Wuhan, China
4 Conclusion
References
Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System
1 Introduction
2 Brief Literature Review
3 Materials and Methods
3.1 Pre-processing Phase (Polynomial Neural Network)
3.2 Data Mining Fuzzy Induction
4 Results and Discussion
4.1 Composite Monte Carlo Simulation—Deterministic Input
4.2 Composite Monte Carlo Simulation—Probabilistic Analysis
4.3 Simulation Results
4.4 Preparation for the Fuzzy Inference System
5 Conclusions and Future Works
References
The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic
1 Introduction
2 Methodology
2.1 Simulation Environment
2.2 Epidemiological Model
2.3 Approach 1—Dynamic Simulation
3 Fuzzy Simulator
3.1 Modeling of Adaptive Fuzzy-PID Compound Control Simulation System
3.2 System Results
4 Predictive System Based on PID Controller with Nonlinear AI
4.1 Wavelet-ANN-PID Model Description
4.2 Analysis of Simulation Results
5 Model Performance Evaluation
6 Conclusion
Appendix
References
A Quantum Field Formulation for a Pandemic Propagation
1 Introduction
2 Mathematical Model for the Virus Inside a Healthy Society
3 Interaction with Other People and Spontaneous Symmetry Breaking
3.1 Flattening the Curve: The Sigmoid-Like Distribution
3.2 The Spontaneous Symmetry Breaking and Vacuum Expectation Values for the Pandemic
4 Family of Parameters for Some Countries
5 Conclusions
Appendix A
References
