Nowcasting and Forecasting Infectious Disease Dynamics
Meeting Times:
- Wednesday July 16, 1:30 PM – 5:00 PM
- Thursday July 17, 9:00 AM – 5:00 PM
- Friday July 18, 9:00 AM – 5:00 PM
Classroom: TBA
Module Summary:
This course covers essential topics such as delay distributions and their estimation, discrete convolutions, Rt estimation, and the generation interval. It delves into nowcasting techniques, particularly correcting for right truncation in infectious disease count data, and explores various forecasting methods and their evaluation. Additionally, the course introduces ensemble methods, highlighting their benefits and implementation. Through a combination of theoretical knowledge and practical applications, students will gain a comprehensive understanding of these critical concepts in infectious disease modeling.
Prerequisites:
It is expected that course participants have basic knowledge of statistics and mathematics and rudimentary knowledge of infectious disease epidemiology. It is also expected that participants have basic computer software knowledge and preferably are familiar with the software R.
Module Content:
- Delay distributions and how to estimate them; discrete convolutions
- Rt estimation and the generation interval
- Nowcasting (i.e. correcting for right truncation in infectious disease count data)
- Forecasting and evaluation, ensemble methods
Instructors
Nick Reich, PhD
Nicholas Reich, Professor of Biostatistics, University of Massachusetts Amherst, Director, COVID-19 Forecast Hub, Director, Influenza Forecasting Center of Excellence
Dr. Reich's primary research interests are in developing models for complex and dynamic disease systems, developing statistical methods that can draw accurate inferences from disease surveillance data, and optimizing design and analysis strategies for cluster-randomized studies. As a teacher and a collaborator, he focuses on creating reproducible research and on communicating statistical results and concepts clearly and intuitively.
Sam Abbott, PhD
Sam Abbott, Assistant Professor, London School of Hygiene & Tropical Medicine
Dr. Abbott is an infectious disease researcher interested in real-time analysis, forecasting, semi-mechanistic modelling, and open-source tool development. His main research interest lies in developing, evaluating, and applying methods for improving our understanding of infectious disease dynamics in real-time. His current main areas of work are developing and evaluating methods for nowcasting right truncated data, developing and evaluating methods to forecast and understand variant dynamics, reconstructing unobserved infections from a range of data sources (such as count data and prevalence measures), and developing methods for the estimation of the effective reproduction number, the growth rate, and generation interval distribution as well as use cases for these estimates and understanding their interactions.
Required Software:
- R Studio
