Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease

Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease.

Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.

The many academic areas covered in this publication include, but are not limited to:

• Data Analysis Using Python
• Data Analytics
• Data Science
• Data Wrangling
• Deep Learning Models for Detection
• Disease Prediction
• Health Informatics
• Machine Learning Using Python
• Mental Health Analytics
• Model Performance Evaluation
• Natural Language Processing
• Neurological Disorders
• Privacy-Preserving Techniques and Frameworks
• Python Data Science
• Statistical Model Development and Deployment
• Text Mining
• Visual Saliency