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An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.
Chowell, Gerardo; Dahal, Sushma; Tariq, Amna; Roosa, Kimberlyn; Hyman, James M; Luo, Ruiyan.
  • Chowell G; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Dahal S; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
  • Tariq A; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Roosa K; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Hyman JM; National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee, Knoxville, Tennessee, United States of America.
  • Luo R; Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, Louisiana, United States of America.
PLoS Comput Biol ; 18(10): e1010602, 2022 10.
Article in English | MEDLINE | ID: covidwho-2054252
ABSTRACT
We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In our 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model, 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework can be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Journal.pcbi.1010602

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Journal.pcbi.1010602