# Epidemic Forecasting with a Hybrid Deep Learning Method Using CNN-LSTM With WOA-GWO Parameter Optimization: Global COVID-19 Case Study

arXiv: https://arxiv.org/abs/2503.12813 · HTML: https://mlsanity.com/paper/2503.12813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 70 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Epidemic processes in complex networks](https://mlsanity.com/paper/1408.2701/) (2014)
- [A mathematical motivation for complex-valued convolutional networks](https://mlsanity.com/paper/1503.03438/) (2015)
- [Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil](https://mlsanity.com/paper/2007.12261/) (2020)
- [Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting](https://mlsanity.com/paper/2004.01574/) (2020)
- [Integrating Protein Sequence and Expression Level to Analysis Molecular Characterization of Breast Cancer Subtypes](https://mlsanity.com/paper/2410.01755/) (2024)
- [FastGEMF: Scalable High-Speed Simulation of Stochastic Spreading Processes over Complex Multilayer Networks](https://mlsanity.com/paper/2410.16625/) (2024)

## Built on by (most cited first, 1 of 1)

- [Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model](https://mlsanity.com/paper/2503.19048/) (2025)

## Abstract (arXiv)

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.