We are employing diverse optimization techniques to address the optimal control problem and reduce overall epidemic costs—encompassing both infection-related and intervention expenses. Our approach integrates rigorous theoretical methodologies with data-driven strategies. 
We are conducting infectious disease model simulations using what-if scenarios for COVID-19, Influenza, and RSV at national, state, and city levels to support public health agencies in epidemic preparedness and evaluating the impact of interventions. 
We are training neural networks to augment epidemiological models by improving parameter estimation, identifying abnormal trends, and enhancing the models’ data-driven capabilities.
We are developing a digital twin model that integrates multiple data sources to enhance public health decision-making in Texas. This tool will be applied to the long-term study of respiratory diseases, sexually transmitted diseases, vector-borne diseases, opioid transmission, and chronic diseases.