Building a Platform for Data-Driven Pandemic Prediction: From Data Modelling to Visualisation - The CovidLP Project

This book provides an overview of probabilistic prediction for pandemic modeling and guidance on building platforms with currently available technology using tools such as R, Shiny, and interactive plotting programs. It is geared towards practitioners with an interest in developing and presenting results in an online platform of statistical analysis of epidemiological data.

Format: Paperback / softback
Length: 364 pages
Publication date: 14 September 2021
Publisher: Taylor & Francis Ltd

This comprehensive book delves into the realm of building platforms for pandemic prediction, offering a comprehensive overview of probabilistic prediction for pandemic modeling through a data-driven approach. It provides valuable guidance on utilizing cutting-edge technology, such as R, Shiny, and interactive plotting programs, to construct platforms capable of handling pandemic data.

Rather than delving into an exhaustive analysis of every possible scenario, the book emphasizes the integration of statistics and computing tools to facilitate effective prediction. It offers a flexible reading structure, allowing readers to tailor their journey based on their specific needs and interests. The book serves as a solid foundation for further exploration of statistical modeling, implementation tools, monitoring aspects, and software functionalities.

Key Features:

A General but Parsimonious Class of Models: The book presents a versatile set of models that employ a Bayesian approach to perform statistical prediction for epidemics. These models encompass a broad range of scenarios and can be customized to suit specific pandemic contexts.

Automated Routines for Daily Prediction Results: The book incorporates automated routines that enable the generation of daily prediction results with ease. This streamlined process saves time and effort, allowing practitioners to focus on analyzing and interpreting the data.

Interactive Visualization of Model Results: The authors provide comprehensive guidance on how to interactively visualize the model results, enabling practitioners to gain a deeper understanding of the dynamics and patterns underlying the pandemic.

Strategies for Monitoring the Performance of Predictions: The book discusses the importance of monitoring the performance of predictions and identifying potential issues or biases in the results. This proactive approach ensures the accuracy and reliability of the pandemic modeling platforms.

Decisions Required for Developing and Publishing Online Platforms: The book delves into the various decisions involved in developing and publishing online platforms for statistical analysis of epidemiological data. It covers topics such as data selection, model fitting, visualization, and user interface design.

Supplemented by an R Package: The book is accompanied by an R package, which offers specific functionalities and tools tailored for epidemic modeling. This package enhances the practicality and accessibility of the content, making it valuable for practitioners and researchers alike.

Target Audience:

The primary audience for this book includes applied statisticians, biostatisticians, computer scientists, epidemiologists, and professionals with a keen interest in epidemic modeling, particularly in the context of the COVID-19 pandemic, and platform building. The authors, professors at the Statistics Department at Universidade Federal de Minas Gerais, bring extensive expertise and experience to the table, ensuring that the content is both comprehensive and authoritative.

In conclusion, this book is a valuable resource for practitioners, researchers, and anyone seeking to develop robust pandemic prediction platforms. Its comprehensive coverage of probabilistic prediction, technology integration, and monitoring aspects provides a solid foundation for understanding and addressing the challenges posed by pandemics. By leveraging the power of statistics and computing, this book empowers individuals and organizations to make informed decisions and contribute to the global effort in combating pandemics.

Weight: 592g
Dimension: 156 x 235 x 37 (mm)
ISBN-13: 9780367709976