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portfolio

Digital Twin in Public Health

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.
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Machine Learning in Epidemiological Models

We are training neural networks to augment epidemiological models by improving parameter estimation, identifying abnormal trends, and enhancing the models’ data-driven capabilities.alt text

Scenario Modeling Projection for Respiratory Diseases

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. alt text

Cost-effecivness Optimal Control for Infectious Diseases

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. alt text

publications

Risk perception and human behaviors in epidemics

Published in IISE Transactions on Healthcare Systems Engineering, 2018

[Website]

Recommended citation: Zhao, Songnian, Yan Kuang, Chih-Hang Wu, Kaiming Bi, and David Ben-Arieh. "Risk perception and human behaviors in epidemics." IISE Transactions on Healthcare Systems Engineering 8, no. 4 (2018): 315-328.

A Memetic Algorithm for Solving Optimal Control Problems of Zika Virus Epidemic with Equilibriums and Backward Bifurcation Analysis

Published in Communications in Nonlinear Science and Numerical Simulation, 2020

[PDF]

Recommended citation: Bi, Kaiming, Yuyang Chen, Chih-Hang John Wu, and David Ben-Arieh. "A Memetic Algorithm for Solving Optimal Control Problems of Zika Virus Epidemic with Equilibriums and Backward Bifurcation Analysis." Communications in Nonlinear Science and Numerical Simulation (2020): 105176.

Additive manufacturing embraces big data

Published in Progress in Additive Manufacturing, 2021

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Recommended citation: Bi, Kaiming, Dong Lin, Yiliang Liao, Chih-Hang Wu, and Pedram Parandoush. "Additive manufacturing embraces big data." Progress in Additive Manufacturing (2021): 1-17.

Evaluating the Health Economic Impacts of Baloxavir Marboxil and Oseltamivir for the Treatment of Influenza in Adult Outpatients in Hong Kong: A Cost-Effectiveness Analysis

Published in SSRN, 2025

[PDF]

Recommended citation: Chen, Ruohan, Zengyang Shao, Kaiming Bi, Benjamin J. Cowling, and Zhanwei Du. "Evaluating the Health Economic Impacts of Baloxavir Marboxil and Oseltamivir for the Treatment of Influenza in Adult Outpatients in Hong Kong: A Cost-Effectiveness Analysis." Available at SSRN 5085546.

talks

Texas All-Payor Claims Database (TX-APCD)

The TX-APCD includes medical, pharmacy, and dental claims, as well as eligibility and provider files, collected from private and public payors. It will contain administrative claims information on approximately 60% of all covered Texans, representing nearly 100% of medical claims regulated by the state.
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Scenario Modeling Hub (SMH)

The SMH pathogen-specific projections provide real-time modeling evidence aiming to support ongoing public health needs. SMH currently produces projections for COVID-19, seasonal influenza, and Respiratory Syncytial Virus (RSV), each addressing different public health questions and uncertainties.
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teaching