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ORCID
https://orcid.org/0009-0008-1348-1954
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Public Health
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2023
Month Degree Awarded
May
Abstract
Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights.
In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into a hierarchy made up of different temporal scales, produces forecasts at each level of the hierarchy, then reconciles those forecasts using optimized weighted forecast combination. While THieF's unique approach has shown substantial accuracy improvements in a diverse range of applications, such as operations management and emergency room admission predictions, this technique had not previously been applied to outbreak forecasting.
We generated candidate models formulated using the THieF methodology, which differed by their hierarchy schemes and data transformations, and ensembles of the THieF models, computed as a mean of predictive quantiles. The models were evaluated using weighted interval score (WIS) as a measure of forecast skill, and the top-performing subset was compared to several benchmark models. These models included simple ARIMA and seasonal ARIMA models, a naive baseline model, and an ensemble of operational incident hospitalization models from the US COVID-19 Forecast Hub. The THieF models and THieF ensembles demonstrated improvements in WIS and MAE, as well as competitive prediction interval coverage, over many benchmark models for both the validation and testing phases. The best THieF model generally ranked second out of nine total models during the testing evaluation. These accuracy improvements suggest the THieF methodology may serve as a useful addition to the infectious disease forecasting toolkit.
DOI
https://doi.org/10.7275/35077839
First Advisor
Nicholas G. Reich
Second Advisor
Evan L. Ray
Third Advisor
Benjamin W. Rogers
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Shandross, Li, "Forecasting COVID-19 with Temporal Hierarchies and Ensemble Methods" (2023). Masters Theses. 1287.
https://doi.org/10.7275/35077839
https://scholarworks.umass.edu/masters_theses_2/1287
Included in
Biostatistics Commons, Data Science Commons, Longitudinal Data Analysis and Time Series Commons, Public Health Commons, Statistical Models Commons