Mathematical Modeling of Epidemic Dynamics for clinical research with Machine Learning Simulations

Abstract

Mathematical modeling is important in spreading, controlling, and prediction of infectious diseases in the population. Conventional compartmental epidemic models including susceptible-infected-recovered (SIR) frameworks can give systematic information on the dynamics of transmission and reproduction numbers, as well as intervention effects. Nevertheless, classical models tend to assume some fixed parameter and simplified assumptions that are not necessarily capable of describing the complex, real-world epidemiological trends that can be seen in the clinical data. The framework of machine learning (ML) simulations into epidemic modeling provides better predictive power through the opportunity to estimate parameters using data, detect nonlinear patterns, and adaptively forecast. In this paper, a hybrid model of mathematical epidemic modeling and machine learning simulation is provided to aid clinical research practices. The method combines the compartmental modeling, parameter optimization, and supervised learning algorithms to enhance the prediction of outbreaks, stratification of risks, and intervention planning. Based on simulation tests, it is found that ML-based parameter tuning enhances short-term forecasting and is more capable of capturing nonlinearity transmission patterns in comparison to conventional deterministic models. Although there are these benefits, practical constraints are that it requires high-quality clinical datasets, is computationally complex, black-box models are difficult to interpret, and training data may be biased. The way forward is explanatory AI integration to clinical transparency, integration of real-time streaming health data, federated learning to analyze privacy-preserving epidemiology, and multi-scale modeling models to relate the individual-level clinical data to the population-level epidemic processes.