Epidemic Forecasting with a Hybrid Deep Learning Method Using CNN-LSTM With WOA-GWO Parameter Optimization: Global COVID-19 Case Study
Mousa Alizadeh, Mohammad Hossein Samaei, Azam Seilsepour, Alireza Monavarian, Mohammad TH Beheshti
TL;DR
This study tackles COVID-19 time-series forecasting by proposing a hybrid CNN-LSTM model whose hyperparameters are tuned with a novel RS-GWO-WOA metaheuristic. The approach integrates CNNs for spatial feature extraction with LSTMs for temporal modeling, and uses random-switching between Grey Wolf Optimizer and Whale Optimization Algorithm to optimize architectural and training hyperparameters. Across 24 countries and six continents, the method consistently outperforms ARIMA and several deep-learning baselines, with statistical tests confirming significant improvements. The framework demonstrates strong potential for accurate, scalable epidemic forecasting to inform public health decisions and resource allocation, and suggests future work incorporating additional contextual data such as demographics and interventions.
Abstract
Effective epidemic modeling is essential for managing public health crises, requiring robust methods to predict disease spread and optimize resource allocation. This study introduces a novel deep learning framework that advances time series forecasting for infectious diseases, with its application to COVID 19 data as a critical case study. Our hybrid approach integrates Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) models to capture spatial and temporal dynamics of disease transmission across diverse regions. The CNN extracts spatial features from raw epidemiological data, while the LSTM models temporal patterns, yielding precise and adaptable predictions. To maximize performance, we employ a hybrid optimization strategy combining the Whale Optimization Algorithm (WOA) and Gray Wolf Optimization (GWO) to fine tune hyperparameters, such as learning rates, batch sizes, and training epochs enhancing model efficiency and accuracy. Applied to COVID 19 case data from 24 countries across six continents, our method outperforms established benchmarks, including ARIMA and standalone LSTM models, with statistically significant gains in predictive accuracy (e.g., reduced RMSE). This framework demonstrates its potential as a versatile method for forecasting epidemic trends, offering insights for resource planning and decision making in both historical contexts, like the COVID 19 pandemic, and future outbreaks.