
Building a Platform for Data-Driven Pandemic Prediction
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
The focus is on the integration of statistics and computing tools rather than on an in-depth analysis of all possibilities on each side. Readers can follow different reading paths through the book, depending on their needs. The book is meant as a basis for further investigation of statistical modelling, implementation tools, monitoring aspects, and software functionalities.
Features:
A general but parsimonious class of models to perform statistical prediction for epidemics, using a Bayesian approach
Implementation of automated routines to obtain daily prediction results
How to interactively visualize the model results
Strategies for monitoring the performance of the predictions and identifying potential issues in the results
Discusses the many decisions required to develop and publish online platforms
Supplemented by an R package and its specific functionalities to model epidemic outbreaks
The book is geared towards practitioners with an interest in the development and presentation of results in an online platform of statistical analysis of epidemiological data. The primary audience includes applied statisticians, biostatisticians, computer scientists, epidemiologists, and professionals interested in learning more about epidemic modelling in general, including the COVID-19 pandemic, and platform building.
The authors are professors at the Statistics Department at Universidade Federal de Minas Gerais. Their research records exhibit contributions applied to a number of areas of Science, including Epidemiology. Their research activities include books published with Chapman and Hall/CRC and papers in high quality journals. They have also been involved with academic management of graduate programs in Statistics and one of them is currently the President of the Brazilian Statistical Association.
More details
Other editions
Additional editions


Persons
Marcos Prates obtained his bachelor's in 2006 in the Computational Mathematics program at the Universidade Federal de Minas Gerais (UFMG) and a master's in Statistics in 2008 from the same institution. In 2011 he received his Ph.D. in Statistics from the University of Connecticut and was a Visiting Professor in the same institution from 2019 to 2020. Currently, he is an Associate Professor at UFMG. His main research areas are Bayesian Statistics, Generalised Linear Mixed Models, Machine Learning, and Spatial Statistics. He was Director of the Graduate Program in Statistics in UFMG (2016-2018), was the Secretary for ISBRA, the Brazilian chapter of ISBA (2015-2016), and currently is the President of the Brazilian Statistical Association (2020-2022).
Thais Paiva obtained a bachelor degree in Actuarial Science from the Universidade Federal de Minas Gerais (UFMG) in 2008, and a Masters in Statistics from the same university in 2010. She earned a PhD degree in Statistics at Duke University in 2014. Since 2016, she has been an Assistant Professor in the Statistics Department at UFMG, working actively on the Actuarial Science undergraduate program and on the Statistics graduate program. Her main research interests are Bayesian Statistics, Imputation Methods for Missing Data, Data Confidentiality and Spatial Statistics.
Vinicius Mayrink is an Associate Professor in the Department of Statistics at the Universidade Federal de Minas Gerais (UFMG) in Brazil. He received: his Ph.D. degree in the Department of Statistical Science at Duke University (USA, 2011), B.Sc. degree in Statistics from UFMG (2004), M.Sc. degree in Statistics from the Universidade Federal do Rio de Janeiro (2006) and a second M.Sc. degree in Statistics at Duke University (2009). Vinicius is currently the sub-Director of the Graduate Program in Statistics of the UFMG (2021-2023). He was a member (treasurer, 2015-2016) of the administrative board of ISBRA (the Brazilian chapter of ISBA). His research interests include: Bayesian Inference, Multivariate Analysis, Spatial Statistics, Survival Analysis and Statistical Modeling in Bioinformatics.
Content
1. Overview of the book
2. Pandemic Data
II Modelling
3. Basic Epidemiological Features
4. Data Distributions
5. Modelling Specific Data Features
6. Review of Bayesian Inference
III Further Modelling
7. Modelling Misreported Data
8. Hierarchical Modelling
IV Implementation
9. Data Extraction/ETL
10. Automating Modelling and Inference
11. Building an Interactive App with Shiny
V Monitoring
12. Daily Evaluation of the Updated Data
13. Investigating Inference Results
14. Comparing Predictions
VI Software
15. PandemicLP Package: Basic Functionalities
16. Advanced Settings: The Pandemic Model Funtion
VII Conclusion
17. Future Directions
System requirements
File format: ePUB
Copy protection: Adobe-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
- Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (not Kindle).
The file format ePub works well for novels and non-fiction books – i.e., „flowing” text without complex layout. On an e-reader or smartphone, line and page breaks automatically adjust to fit the small displays.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our ebook Help page.