GPCE Seminar Series: All Models are Useful: Bayes Ensembling for Robust High Resolution COVID-19 Forecasting

Global Pervasive Computational Epidemiology Seminar Series

Date/time: Thursday, September 9, 11:30am – 12:30pm Eastern (USA)

Topic: All Models are Useful: Bayes Ensembling for Robust High Resolution COVID-19 Forecasting

Speaker: Aniruddha Adiga

Zoom link:


Timely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. In this talk, we discuss our efforts towards forecasting COVID-19 confirmed  cases  which  has  continued to serve local, state and multinational health agencies for over a year. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme and is able to produce forecasts at different resolutions: county, state, and national level. In terms of forecast evaluation, while showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model’s performance with other similar models being  integrated into CDC-initiated COVID-19 Forecast  Hub. In addition, we have developed a public-access interactive dashboard for visualizing and evaluating our model forecasts and will discuss some of its functionalities.


Speaker Bio:
Aniruddha Adiga is a Research Scientist at the NSSAC Division of the Biocomplexity Institute and initiative, UVA. He completed his Ph.D. from the Department of Electrical Engineering, Indian Institute of Science (IISc), Bangalore, India and has held the position of Postdoctoral fellow at IISc, North Carolina State University, and UVA. His research areas include signal processing, machine learning, data mining, forecasting and big data analysis. At NSSAC, his primary focus has been the analysis and development of forecasting systems for epidemiological signals such as influenza-like illness and COVID-19 using auxiliary data sources.