Data Science for Infectious Disease Data Analytics: An Introduction with R

Book Description

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, such as the newly discovered coronavirus disease (COVID-19).

The primary emphasis of this book will be the data science procedure 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.

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.

Key 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. Describes 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.

中文：

书名：传染病数据分析的数据科学: R导论

传染病数据分析的数据科学: R简介

概述了专门用于分析传染病数据的现代数据科学工具和方法。本书以R中的流行病学数据可视化和分析的快速入门指南，跨越了学术界和实践之间的鸿沟，提供了许多使用最新数据 (如新发现的冠状病毒病 (新型冠状病毒肺炎)) 的生动、有指导意义的数据分析示例。

本书的主要重点将是流行病学研究中的数据科学程序，包括数据争夺，可视化，解释，预测建模和推断，由于广泛领域的数据日益多样化和非实验数据，这一点非常重要。读者从本书中获得的知识和技能也可以转移到其他领域，例如公共卫生，商业分析，环境研究或一般的时空数据可视化和分析。

本书面向具有数学和统计学本科知识的读者，是流行病学学数据科学发展和实施的理想介绍。

主要特点:

描述了如何收集，整理，可视化并将其馈送到预测模型的整个数据科学过程，从而促进了数据源，科学家和决策者之间的有效沟通。描述传染病数据的实用概念，并提供特定的数据科学观点。传染病数据的独特特征和问题以及它们如何影响流行病建模和预测的概述。介绍各种类型的模型和最先进的学习方法，以分析传染病数据，并就如何将不同的模型和方法联系起来提供有价值的见解。