Comparative Analysis of Machine Learning Models for Forecasting Infectious Disease Spread

Abstract

Accurate forecasting of infectious disease spread is essential for effective resource planning and strategic decision-making in public health. This study provides a comprehensive evaluation of various machine learning models, from traditional statistical approaches to advanced deep learning techniques, for forecasting disease outbreak dynamics. Focusing on daily positive cases and daily deaths—key indicators despite potential reporting inconsistencies—our analysis aims to identify the most effective models across different algorithm families. By adapting non-time series methods with temporal factors and enriching time series models with exogenous variables, we enhance model suitability for the data’s time-dependent nature. Using India as a case study due to its significant early pandemic spread, we evaluate models through metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Median Squared Error (MEME), and Mean Squared Log Error (MSLE). The models tested include Linear Regression, Elastic Net, Random Forest, XGBoost, and Simple Exponential Smoothing, among others. Results indicate that the Random Forest Regressor outperforms other methods in terms of prediction accuracy across most metrics. Notably, findings suggest that simpler models can sometimes match or even exceed the reliability of more complex approaches. However, limitations include model sensitivity to data quality and the lack of real-time adaptability, which may affect performance in rapidly evolving outbreak situations. These insights have critical implications for public health policy and resource allocation in managing infectious disease outbreaks.