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Real-time forecasting of epidemic trajectories using computational dynamic ensembles.

G Chowell1, R Luo2, K Sun3

  • 1Department of Population Heath Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.

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Summary

This study introduces an ensemble model for forecasting infectious disease spread, combining multiple models to improve accuracy. The ensemble model, integrating the Generalized Logistic Model (GLM) and Generalized-Growth Model (GGM), outperformed individual models and challenge participants in the Ebola Forecasting Challenge.

Keywords:
Ensemble forecastEpidemic forecastingGeneralized-growth modelGeneralized-logistic modelModel ensembleParameter estimationRMSEReproduction numberUncertainty propagationUncertainty quantification

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Mathematical Modeling

Background:

  • Forecasting infectious disease spread is complex due to data and model uncertainties.
  • Existing models often struggle to accurately predict epidemic trajectories.
  • There is a need for robust forecasting methods that quantify uncertainty.

Purpose of the Study:

  • To develop and evaluate an ensemble model for sequential forecasting of social dynamic processes, specifically infectious disease outbreaks.
  • To assess the uncertainty of the ensemble model using a frequentist computational bootstrap approach.
  • To compare the performance of the ensemble model against individual models and established forecasting challenges.

Main Methods:

  • Developed an ensemble model by weighting a set of plausible phenomenological models, including the Generalized-Growth Model (GGM) and Generalized Logistic Model (GLM).
  • Employed a frequentist computational bootstrap approach to evaluate forecast uncertainty.
  • Utilized the root-mean-square error (RMSE) for model calibration and forecasting performance evaluation.
  • Applied the model to outbreak scenarios from the Ebola Forecasting Challenge.

Main Results:

  • The ensemble model, combining GGM and GLM, demonstrated superior overall mean RMSE performance compared to individual models and participants in the Ebola Forecasting Challenge.
  • The ensemble model consistently provided more accurate forecasts than the GGM and GLM individually across various forecasting horizons.
  • Forecast accuracy varied by horizon; the ensemble model excelled at 2-3 week horizons, while GLM performed better at 1 and 4 weeks.

Conclusions:

  • Ensemble modeling offers a promising approach to enhance the accuracy and reliability of infectious disease spread forecasts.
  • Accounting for model uncertainty through methods like bootstrapping is crucial for robust epidemic forecasting.
  • The developed GGM-GLM ensemble model shows potential for improving short-term epidemic outbreak predictions.