1st Edition

Data Science for Infectious Disease Data Analytics An Introduction with R

By Lily Wang Copyright 2023
419 Pages 129 B/W Illustrations
by Chapman & Hall

419 Pages 129 B/W Illustrations
by Chapman & Hall

Data Science for Infectious Disease Data Analytics: An Introduction with R provides an overview of modern data science tools and methods that have been developed specifically to analyze infectious disease data. With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis... Read more

Chapter 1 Introduction

Chapter 2 Data Wrangling

Chapter 3 Data Visualization with R Package “ggplot2”

Chapter 4 Interactive Visualization

Chapter 5 R Shiny

Chapter 6 Interactive Geospatial Visualization

Chapter 7 Epidemic Modeling

Chapter 8 Compartment Models

Chapter 9 Time Series Analysis of Infectious Disease Data

Chapter 10 Regression Methods

Chapter 11 Neural Networks

Chapter 12 Hybrid Models

Appendix A

Appendix B

Appendix C

Biography

Dr. Lily Wang is a tenured professor of statistics at George Mason University. She earned her PhD in statistics from Michigan State University in 2007. Before joining Mason in 2021, she was on the faculty of Iowa State University (2014-2021) and the University of Georgia (2007-2014). Her primary research areas include non/semi-parametric modeling and inference, statistical learning of data objects with complex features, methodologies for functional data, spatiotemporal data, imaging, and general issues related to data science and big data analytics. Dr. Wang is a fellow of both the Institute of Mathematical Statistics and the American Statistical Association and an Elected Member of the International Statistical Institute. She is currently serving on the editorial board of Journal of the Royal Statistical Society, Series B, Journal of Nonparametric Statistics and Statistical Analysis and Data Mining.