Disease Forecasting Working Group

Established in September 2017, this working group aims to advance our capacity to forecast infectious disease dynamics by developing a forecasting architecture written in the programming language R and disseminating stress-tested R packages to CEID members and the greater scientific community. By forecasting a variety of diseases with generic time series models and comparing these to CDC models, the group seeks to provide researchers with modeling frameworks needed to efficiently and effectively forecast infectious diseases, in real time. This working group allows for coordination between CEID teams and forecasting research groups at other institutions through collaborative projects that often take the form of forecasting competitions for infectious disease time series. In the past, the forecasting working group has developed a Flu forecasting architecture and contributes to the CDC’s seasonal Flu Forecasting Challenge. The forecasting working group is currently working on the production version of a forecasting system for COVID cases, hospitalizations, and deaths. The production system will include an automated forecasting pipeline complete with evaluations of forecasting quality and visualizations. The forecasting working group plans to increase the activities to include explanatory modeling of COVID and focus on development of open-source standard software resources for infectious disease forecasting.

For information on joining this working group, or partnering for a specific or envisioned project, please contact us at ceid@uga.edu


Working Group Members:

Avranil Basu

Eamon O’Dea

Eric Marty

John Drake

Savannah Hammerton