Data Science for COVID-19 Volume 1

Computational Perspectives

Utku Kose, Deepak Gupta, Victor Hugo Costa de Albuquerque, Ashish Khanna

• Publisher: Academic Press
• Year: 2021
• Print ISBN: 9780128245361
• eBook ISBN: 9780128245378
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation. - Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and treatment of the COVID-19 virus - Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including both positive and negative research findings - Provides insights into innovative data-oriented modeling and predictive techniques from COVID-19 researchers - Includes real-world feedback and user experiences from physicians and medical staff from around the world on the effectiveness of applied Data Science solutions

Table of contents

• Data Science for COVID-19
• Chapter 1 Predictive models to the COVID-19
• Chapter 2 An artificial intelligence–based decision support and resource management system for COVID-19 pandemic
• Chapter 3 Normalizing images is good to improve computer-assisted COVID-19 diagnosis
• Chapter 4 Detection and screening of COVID-19 through chest computed tomography radiographs using deep neural networks.
• Chapter 5 Differential evolution to estimate the parameters of a SEIAR model with dynamic social distancing
• Chapter 6 Limitations and challenges on the diagnosis of COVID-19 using radiology images and deep learning
• Chapter 7 Deep convolutional neural network–based image classification for COVID-19 diagnosis
• Chapter 8 Statistical machine learning forecasting simulation for discipline prediction and cost estimation of COVID-19 pandemic
• Chapter 9 Application of machine learning for the diagnosis of COVID-19
• Chapter 10 PwCOV in cluster-based web server
• Chapter 11 COVID-19–affected medical image analysis using DenserNet
• Chapter 12 uTakeCare
• Chapter 13 COVID-19 detection from chest X-rays using transfer learning with deep convolutional neural networks
• Chapter 14 Lexicon-based sentiment analysis using Twitter data
• Chapter 15 Real-time social distance alerting and contact tracing using image processing
• Chapter 16 Machine-learning models for predicting survivability in COVID-19 patients
• Chapter 17 Robust and secured telehealth system for COVID-19 patients
• Chapter 18 A novel approach to predict COVID-19 using support vector machine
• Chapter 19 An ensemble predictive analytics of COVID-19 infodemic tweets using bag of words
• Chapter 20 Forecast and prediction of COVID-19 using machine learning
• Chapter 21 Time series analysis of the COVID-19 pandemic in Australia using genetic programming
• Chapter 22 Image analysis and data processing for COVID-19
• Chapter 23 A demystifying convolutional neural networks using Grad-CAM for prediction of coronavirus disease (COVID-19) on X-ray images
• Chapter 24 Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images
• Chapter 25 Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2
• Chapter 26 Received signal strength indication—based COVID-19 mobile application to comply with social distancing using bluetooth signals from smartphones
• Chapter 27 COVID-19 pandemic in India
• Chapter 28 Mathematical recipe for curbing coronavirus (COVID-19) transmition dynamics
• Chapter 29 Sliding window time series forecasting with multilayer perceptron and multiregression of COVID-19 outbreak in Malaysia
• Chapter 30 A two-level deterministic reasoning pattern to curb the spread of COVID-19 in Africa
• Chapter 31 Data-driven approach to COVID-19 infection forecast for Nigeria using negative binomial regression model
• Chapter 32 A novel machine learning–based detection and diagnosis model for coronavirus disease (COVID-19) using discrete wavelet transform with rough neural network
• Chapter 33 Artificial intelligence–based solutions for early identification and classification of COVID-19 and acute respiratory distress syndrome
• Chapter 34 Internet of Medical Things (IoMT) with machine learning–based COVID-19 diagnosis model using chest X-ray images
• Chapter 35 The growth of COVID-19 in Spain. A view based on time-series forecasting methods
• Chapter 36 On privacy enhancement using u-indistinguishability to COVID-19 contact tracing approach in Korea
• Chapter 37 Scheduling shuttle ambulance vehicles for COVID-19 quarantine cases, a multi-objective multiple 0–1 knapsack model with a novel Discrete Binary Gaining-Sharing knowledge-based optimization algorithm
• Index