Research & Projects

Disease Management in Greater Yellowstone Area Elk

Analyze infectious disease transmission and optimize bioeconomic management strategies, bridging ecological dynamics with policy-relevant decision-making.
Investigate disease model with multiple state and control variables in R (numerically) and Mathematica (analytically).
Implement a novel multi-variable optimization method, developing an R-based high-dimensional perturbation algorithm to compute optimal control in a nonlinear, multivariable bioeconomic system: an approach not covered in existing textbooks or prior literature, and can be extended to other areas.

In our first paper, we analyze the dynamics of elk populations facing chronic wasting disease (CWD) transmitted through both direct contact and environmental prion accumulation. Our findings highlight the importance of early intervention: management actions are most effective when prion levels remain relatively low. The model shows that increasing hunting and reducing supplemental feeding not only helps suppress CWD prevalence but also improves long-term economic outcomes by balancing ecological health with tourism and hunting benefits.
In our second paper, we extend the framework to study the interaction between two pathogens — brucellosis and CWD. We demonstrate that these diseases are ecological substitutes but economic complements, meaning that shifts in one disease affect the optimal management of the other. Notably, an increase in brucellosis prevalence can indirectly contribute to controlling CWD, since the two pathogens compete for hosts. This analysis underscores the importance of integrated management strategies when multiple infectious diseases coexist in wildlife populations.

Addressed the challenge of agricultural nonpoint source (NPS) pollution, which is diffuse and stochastic due to climate-driven variability (e.g., rainfall). Current policy instruments focus only on mean pollutant loads, and the U.S. EPA lacks a precise mathematical definition of Total Maximum Daily Loads (TMDLs).
Our paper developed a new framework that (1) provides a formal mathematical definition of TMDLs consistent with safety-first constraints (via Cantelli’s inequality), and (2) introduces dual instruments targeting both the mean and variance of pollutant loads.
We prove that jointly taxing mean and variance yields greater social net benefits than mean-only policies, by reducing the risk of extreme, high-cost pollution events while reliably achieving TMDL targets.

Published in Energy Economics (2025): Showed that efforts to reduce SO₂ emissions in China produced unintended side effects, increasing particulate matter (PM) levels and infant mortality. (link)
Working paper: Investigate how China’s West–East Gas Pipeline (WEPP) spurred the growth of nearby clean industries, highlighting the spatial spillovers of energy infrastructure.

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