Название: Data Science for Infectious Disease Data Analytics: An Introduction with R
Автор: Lily Wang
Издательство: CRC Press
Серия: Data Science Series
Год: 2023
Страниц: 420
Язык: английский
Формат: pdf (true)
Размер: 17.2 MB

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 examples using the most up-to-date data.

The primary emphasis of this book is the data science procedures in epidemiological studies, including data wrangling, visualization, interpretation, predictive modeling, and inference, which is of immense importance due to increasingly diverse and nonexperimental data across a wide range of fields. The knowledge and skills readers gain from this book are also transferable to other areas, such as public health, business analytics, environmental studies, or spatio-temporal data visualization and analysis in general.

The book will be an excellent reference for four types of audiences: (1) data scientists with interest in statistical analysis of epidemiological data; (2) undergraduate and graduate students studying biostatistics or epidemiology; (3) epidemiologists learning R to work on data science issues; and (4) practitioners learning epidemic modeling in general and developing data science tools for epidemiological data.

In most chapters, we assume that readers are familiar with introductory statistics. A couple of chapters also require knowledge of calculus, for example, epidemic modeling and neural networks.

Built from the ground up for statistical analysis, R has become one of the favorite programming languages for data scientists. There are many reasons why the R programming language has been so popular in data science. Several important reasons include the open-source data operation packages and utilities, various statistical and graphical capabilities, a wide range of database support, and interactive web-based dashboards. We use the R programming language throughout the book, and we intend for students to learn how to use R for data visualization, modeling, and forecasting, especially in epidemiology. See Appendix A for instructions on installing and using R.

Aimed at readers with an undergraduate knowledge of mathematics and statistics, this book is an ideal introduction to the development and implementation of Data Science in epidemiology.

Features:

Describes the entire Data Science procedure of how the infectious disease data are collected, curated, visualized, and fed to predictive models, which facilitates effective communication between data sources, scientists, and decision-makers.
Explains practical concepts of infectious disease data and provides particular data science perspectives.
Overview of the unique features and issues of infectious disease data and how they impact epidemic modeling and projection.
Introduces various classes of models and state-of-the-art learning methods to analyze infectious diseases data with valuable insights on how different models and methods could be connected.