Xiaoyu Tang

Om Xiaoyu Tang

Background

Xiaoyu Tang is a PhD candidate in Biostatistics at the School of Medical Sciences, Örebro University, working at the intersection of biostatistics, epidemiology, health economics, and data science. With a foundational background in mathematics and statistics, Xiaoyu holds a Master of Science in Statistics from Örebro University, as well as dual bachelor’s degrees in Applied Mathematics and Japanese Language and Literature from China.

Research Focus

Xiaoyu’s research focuses on developing data-driven methodologies to evaluate and optimize public health policies. Combining real-world data with computational and simulation-based approaches, the work primarily utilizes R and Python, with expertise in:

• Statistical modeling and machine learning
• Causal inference and counterfactual analysis
• Health economic evaluation and cost-effectiveness analysis
• Policy efficiency evaluation and microsimulation

Current Project

AI-Powered Pandemic Policy Evaluation & Microsimulation (Funded by the Swedish Research Council / Vetenskapsrådet)

Building on the project "An AI-powered 4-dimension dynamic decision-making framework to boost anti-pandemic strategy: a comprehensive evaluation and microsimulation study", this research integrates biostatistics, machine learning, and health economics to assess pandemic strategies. Ongoing work includes a registry-based microsimulation of pandemic progression and health consequences in Sweden.

Selected Publications & Presentations

Publications: Research outputs have appeared in peer-reviewed journals including Journal of Global Health, BMC Health Services Research, and International Journal of Infectious Diseases, focusing on intervention cost-effectiveness, policy efficiency across European OECD countries, and counterfactual evaluations.

Conferences

Researches were presented internationally, including EuHEA Conference 2024 in Vienna, EuHEA Conference 2026 in Rotterdam and Event for PhD Students in Statistics in Stockholm 2025, all as oral presentations.