Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
Real-time forecasting of non-stationary time series is a challenging problem, especially when the time series evolves rapidly. For such cases, it has been observed that ensemble models consisting of a diverse set of model classes can perform consistently better than individual models. In order to account for the nonstationarity of the data and the lack of availability of training examples, the models are retrained in real-time using the most recent observed data samples. Motivated by the robust performance properties of ensemble models, we developed a Bayesian model averaging ensemble technique consisting of statistical, deep learning, and compartmental models for fore-casting epidemiological signals, specifically, COVID-19 signals. We observed the epidemic dynamics go through several phases (waves). In our ensemble model, we observed that different model classes performed differently during the various phases. Armed with this understanding, in this paper, we propose a modification to the ensembling method to employ this phase information and use different weighting schemes for each phase to produce improved forecasts. However, predicting the phases of such time series is a significant challenge, especially when behavioral and immunological adaptations govern the evolution of the time series. We explore multiple datasets that can serve as leading indicators of trend changes and employ transfer entropy techniques to capture the relevant indicator. We propose a phase prediction algorithm to estimate the phases using the leading indicators. Using the knowledge of the estimated phase, we selectively sample the training data from similar phases. We evaluate our proposed methodology on our currently deployed COVID-19 forecasting model and the COVID-19 ForecastHub models. The overall performance of the proposed model is consistent across the pandemic. More importantly, it is ranked second during two critical rapid growth phases in cases, regimes where the performance of most models from the ForecastHub dropped significantly.
Identifier
85147908313 (Scopus)
ISBN
[9781665480451]
Publication Title
Proceedings 2022 IEEE International Conference on Big Data Big Data 2022
External Full Text Location
https://doi.org/10.1109/BigData55660.2022.10020579
First Page
1594
Last Page
1603
Grant
75D30119C05935
Fund Ref
National Science Foundation
Recommended Citation
Adiga, Aniruddha; Kaur, Gursharn; Hurt, Benjamin; Wang, Lijing; Porebski, Przemyslaw; Venkatramanan, Srinivasan; Lewis, Bryan; and Marathe, Madhav, "Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources" (2022). Faculty Publications. 3399.
https://digitalcommons.njit.edu/fac_pubs/3399