Intelligent modelling, prediction, and diagnosis from epidemiological data : COVID-19 and beyond / edited by Siddhartha Bhattacharyya.
Intelligent Modeling, Prediction, and Diagnosis from Epidemiological Data: COVID-19 and Beyond is a handy treatise to elicit and elaborate possible intelligent mechanisms for modeling, prediction, diagnosis, and early detection of diseases arising from outbreaks of different epidemics with special reference to COVID-19. Starting with a formal introduction of the human immune systems, this book focuses on the epidemiological aspects with due cognizance to modeling, prevention, and diagnosis of epidemics. In addition, it also deals with evolving decisions on post-pandemic socio-economic structure. The book offers a comprehensive coverage of the most essential topics, including: A general overview of pandemics and their outbreak behavior A detailed overview of CI techniques Intelligent modeling, prediction, and diagnostic measures for pandemics Prognostic models Post-pandemic socio-economic structure The accompanying case studies are based on available real-world data sets. While other books may deal with this COVID-19 pandemic, none features topics covering the human immune system as well as influences on the environmental disorder due to the ongoing pandemic. The book is primarily intended to benefit medical professionals and healthcare workers as well as the virologists who are essentially the frontline fighters of this pandemic. In addition, it also serves as a vital resource for relevant researchers in this interdisciplinary field as well as for tutors and postgraduate and undergraduate students of information sciences.
Gespeichert in:
| Beteiligte Personen | |
|---|---|
| Ausgabe | First edition. |
| Ort, Verlag, Jahr |
Boca Raton, Florida
: CRC Press
, 2022
|
| Umfang | 1 online resource (233 pages) |
| ISBN | 1-00-315868-4 1-000-47470-4 1-000-47473-9 1-003-15868-4 |
| Sprache | Englisch |
| Zusatzinfo | Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Editor -- Contributors -- 1. Human Immune System and Infectious Disease -- 1.1 Introduction -- 1.2 Human Immune System -- 1.2.1 Components of the Immune System -- 1.2.2 Disease and Treatment of the Immune System -- 1.3 Infectious Diseases -- 1.3.1 Occurrences of Pandemic -- 1.4 Vaccines-Strengthening of the Adaptive Immune System: -- 1.4.1 Vaccine Manufacturing -- 1.4.2 Working of Vaccines -- 1.4.3 Vaccine Administration -- 1.4.4 Current Approved Vaccines against COVID-19 -- 1.5 Conclusion -- References -- 2. A Systematic Review of Predictive Models on COVID-19 with a Special Focus on CARD Modeling with SEI Formulation-An Indian Scenario -- 2.1 Introduction -- 2.2 Motivation of the Work -- 2.3 The Pandemics and Epidemics -- 2.4 COVID-19 in India -- 2.5 What Is Epidemiological Data? -- 2.6 Role of Predictive Modeling during COVID-19 -- 2.7 Predictive Models and India -- 2.7.1 Auto-Regressive Integrated Moving Average (ARIMA) -- 2.8 Brief of Prerequisite Concepts-CARD Model and SEI -- 2.8.1 Curve Fitting -- 2.8.2 Flattening the Curve -- 2.8.3 Analytical Hierarchical Process (AHP) -- 2.8.4 Cartogram -- 2.9 About the CARD Model -- 2.10 CARD Model: Pre- and Post Unlock in India -- 2.11 State-wise Evaluation Index (SEI) Model for Governance and Policy Making -- 2.12 Country-Wise Comparison of COVID-19 Scenario -- 2.13 Conclusion -- 2.13.1 From the Cartogram and State-Wise Indexing -- References -- 3. Data Analytics to Assess the Outbreak of the Coronavirus Epidemic: Opportunities and Challenges -- 3.1 Introduction -- 3.2 Background -- 3.2.1 Coronavirus -- 3.2.2 Data Analytics -- 3.3 Challenges and Opportunities -- 3.3.1 Historical Data -- 3.3.2 Biosensors -- 3.3.3 Challenge in the Process of Data -- 3.4 Application for Fighting COVID-19. 3.4.1 Use of Data Analytics for Diagnostics -- 3.4.2 Smartphone Application -- 3.4.3 IoT and Smart Devices -- 3.4.4 Use of Data Analytics for Prediction -- 3.5 Conclusion -- References -- 4. Leveraging Artificial Intelligence (AI) during the Coronavirus Pandemic: Applications and Challenges -- 4.1 Introduction -- 4.2 Datasets and Data Types for COVID-19 -- 4.3 Applications of AI during COVID-19 Crisis -- 4.3.1 Tracking and Warning Disease Outbreaks -- 4.3.2 Early Identification and Risk Prediction of Disease using Medical Imagery -- 4.3.3 Detecting Temperature and Food Delivery by Robot -- 4.3.4 Drug and Vaccine Discovery -- 4.3.5 AI to Detect Noncompliance or Infected Individuals -- 4.3.6 Chatbots to Answer Queries and Spread Awareness -- 4.4 Challenges -- 4.4.1 Data Privacy -- 4.4.2 Lack of Large and Diverse Data -- 4.4.3 Low Quality Data -- 4.5 Conclusion -- References -- 5. Early Prediction of Coronavirus Epidemic Outbreak Using Stacked Long Short-Term Memory Networks -- 5.1 Introduction -- 5.2 Literature Review -- 5.2.1 Motivation -- 5.2.2 Four-Stacked Long Short-Term Memory (LSTM) Networks Architecture for Early Prediction -- 5.2.2.1 Basic Long Short-Term Memory (LSTM) Networks -- 5.2.3 Prediction Model Using Four-Stacked LSTM Networks -- 5.3 Results and Discussion -- 5.3.1 Datasets -- 5.3.2 Experimental Setup -- 5.4 Experimental Results -- 5.5 Conclusion -- 5.5.1 Code and Data Availability -- References -- 6. Use of Satellite Sensors to Diagnose Changes in Air Quality in Africa Before and During the COVID-19 Pandemic -- 6.1 Introduction -- 6.2 COVID-19 and Environment -- 6.3 Materials and Methods -- 6.3.1 Study Area -- 6.3.2 Methodology -- 6.4 Results and Discussion -- 6.4.1 Ozone (O[sub(3)]) -- 6.4.2 Nitrogen Dioxide -- 6.4.3 Aerosol -- 6.4.4 Black Carbon -- 6.5 Conclusion -- References. 7. Public Sentiments Analysis through Tweets on the COVID-19 Pandemic: A Comparative Study and Performance Assessment -- 7.1 Introduction -- 7.2 A brief Literature Survey -- 7.3 Data collection, Preprocessing, and Methodology -- 7.3.1 Raw Data Collection -- 7.3.2 Data Preprocessing -- 7.3.3 Data Visualization -- 7.3.3.1 Monthly Statistics of Tweets -- 7.3.3.2 Sentiments' Polarity of Tweets over Different Times -- 7.3.3.3 Geographical Analytics of Tweets -- 7.3.3.4 Top Hashtags Used -- 7.3.3.5 Textual Analytics -- 7.4 Experimental Results and Discussions -- 7.5 Conclusions -- References -- 8. Exploring Twitter Data to Understand the Impact of COVID-19 Pandemic in India Using NLP and Deep Learning -- 8.1 Introduction: Background and Driving Forces -- 8.2 Literature Review -- 8.3 Methodology -- 8.3.1 Data Collection -- 8.3.2 Data Preprocessing -- 8.3.3 Sentiment Analysis -- 8.3.4 Georeferencing -- 8.4 Experiments and Results -- 8.4.1 Exploratory Data Analysis and Georeferencing -- 8.4.2 Topic Modeling -- 8.4.3 Sentiment Detection -- 8.5 Discussion -- 8.6 Conclusion -- Acknowledgments -- References -- 9. Novel Coronavirus (COVID-19): Tracking, Health Care Precautions, Alerts, and Early Warnings -- 9.1 Introduction -- 9.1.1 Modeling Study -- 9.1.2 Tracking -- 9.1.2.1 Medicinal Perspective and Biological Modeling -- 9.1.2.2 Protein Structure Prediction -- 9.2 Basic Virology -- 9.2.1 Virus Entry -- 9.3 Transmission -- 9.4 Healthcare Precautions, Alerts, and Early Warnings -- 9.4.1 WHO Recommendations -- 9.4.2 CDC Recommendations -- 9.4.3 Disinfection and Sanitization -- 9.4.3.1 Survival of Coronavirus under Different Physical Parameters -- 9.5 Herd Immunity-a Probable/Ultimate Tool against COVID-19 -- 9.6 Conclusion and Future Outlook -- Declarations -- References. 10. Edge Computing-Based Smart Healthcare System for Home Monitoring of Quarantine Patients: Security Threat and Sustainability Aspects -- 10.1 Introduction -- 10.2 Methodology -- 10.3 Literature Review -- 10.3.1 Literature on COVID 19 Aspects -- 10.3.1.1 General Body of Work on COVID-19 -- 10.3.1.2 ICT Application to Tackle Different Aspects of COVID 19 -- 10.3.1.3 Application of Telehealth Consultation during COVID-19 -- 10.3.2 Literature on Sensors -- 10.3.2.1 Wireless Sensors -- 10.3.2.2 Wearable Sensors and Smartphone-Based Devices -- 10.4 Proposed Edge Computing Model -- 10.4.1 Working Algorithm of the Proposed Edge Model -- 10.4.2 Application of the Proposed Model -- 10.5 Discussion and Analysis -- 10.5.1 Privacy and Security Issues -- 10.5.2 Sustainability Analysis -- 10.5.2.1 Environmental Sustainability -- 10.5.2.2 Economic Sustainability -- 10.5.2.3 Social Sustainability -- 10.5.2.4 Demand Sustainability -- 10.6 Conclusion -- Acknowledgment -- Author Contribution -- References -- Index. |
| Serie/Reihe | Chapman and Hall/CRC Computational Intelligence and Its Applications |
| Online-Zugang | ProQuest Ebook Central PDA (EPDA) |
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