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<p>This study introduces EpiForecaster, a novel deep-learning ensemble method designed to improve the accuracy of epidemic forecasts during rapidly evolving outbreaks. Using weekly case data from the 2022 Mpox outbreak in Brazil, the USA, Mexico, the UK, and France, spanning from July 10 to October 9, we experiment with combinations of four state-of-the-art deep learning models: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Bi-directional LSTMs (Bi-LSTM), and hybrid CNN-LSTM architectures. Through extensive hyperparameter tuning, each model generates individual stacked forecasts, which are subsequently combined using various meta-model strategies. The primary ensemble approach, EpiForecaster, optimizes model weights using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Bounds (L-BFGS-B) algorithm, dynamically assigning weights based on each model’s performance. Comparative analyses included Gradient Boosting and Genetic Algorithm ensembles, with the Equal-Weight ensemble serving as a baseline. Performance evaluation based on Mean Squared Error (MSE) and Mean Absolute Error (MAE) showed that EpiForecaster consistently enhanced forecast accuracy, challenging the traditional view that larger ensembles uniformly yield better forecasts. EpiForecaster presents a scalable and effective solution for managing the unpredictability of non-stationary data, demonstrating its value in building reliable ensemble models for a variety of predictive applications.</p>

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EpiForecaster: a novel deep learning ensemble optimization approach to combining forecasts for emerging epidemic outbreaks

  • 期刊论文

  • 发表日期：

    2024年12月31日

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  • Milton Soto-Ferrari,
  • Alejandro Carrasco-Pena,
  • Diana Prieto

摘要

This study introduces EpiForecaster, a novel deep-learning ensemble method designed to improve the accuracy of epidemic forecasts during rapidly evolving outbreaks. Using weekly case data from the 2022 Mpox outbreak in Brazil, the USA, Mexico, the UK, and France, spanning from July 10 to October 9, we experiment with combinations of four state-of-the-art deep learning models: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Bi-directional LSTMs (Bi-LSTM), and hybrid CNN-LSTM architectures. Through extensive hyperparameter tuning, each model generates individual stacked forecasts, which are subsequently combined using various meta-model strategies. The primary ensemble approach, EpiForecaster, optimizes model weights using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Bounds (L-BFGS-B) algorithm, dynamically assigning weights based on each model’s performance. Comparative analyses included Gradient Boosting and Genetic Algorithm ensembles, with the Equal-Weight ensemble serving as a baseline. Performance evaluation based on Mean Squared Error (MSE) and Mean Absolute Error (MAE) showed that EpiForecaster consistently enhanced forecast accuracy, challenging the traditional view that larger ensembles uniformly yield better forecasts. EpiForecaster presents a scalable and effective solution for managing the unpredictability of non-stationary data, demonstrating its value in building reliable ensemble models for a variety of predictive applications.