My Research
My research primarily focuses on analysis of computational models of disease transmission to assist public health professionals in crafting interventions to reduce human risk of disease. This has been the focus of my two primary research projects on Chagas Disease transmission in Southern Louisiana and on Lyme Disease transmission in the northeast United States. In addition to these projects I have also worked on using machine learning algorithms to predict optimal generator settings in power grids.
Chagas Disease (Master's Thesis)
Title: Sylvatic and Domestic T. cruzi Transmission Cycles and Chagas Disease Risk in New Orleans, Louisiana
Summary: My research on Chagas Disease focuses on building an ordinary differential equation host-vector model of the transmission of T. cruzi, the caussative agent of Chagas disease, the New Orleans area. I seek to use it first to take the limited data sampling for the New Olreans area to construct an estimation of the number of infected vectors in homes, which is where human infections almost exculsively originate. I will also use the model to identify optimal steps of the transmission pathway for intervention so that we can develop new ways for reducing human risk. I have also conducted literature reviews of human case data to estimate the risk for human infection in Louisiana
Advisors: Dr. James Mac Hyman (Math, Tulane), Dr. Claudia Herrera (Tropical Medicine, Tulane), Dr. Eric Dumonteil (Tropical Medicine, Tulane), Dr. Zhuolin Qu (Math, Tulane)
Lyme Disease (In Review)
Title: Cost Benefit Analysis of Vaccination in Tick-Mouse Transmission of Lyme Disease [PDF]
Abstract: Lyme disease is one of the most prevalent and fastest growing vector-borne bacterial illnesses in the United States, with over 25,000 new confirmed cases every year. Humans contract the bacterium Borrelia burgdorferi through the bite of the tick Ixodes scapularis. The tick can receive the bacterium from a variety of small mammal and bird species, but the white-footed mouse Peromyscus leucopusis is the primary reservoir in the northeastern United States, especially near human settlement. The tick’s life cycle and behavior depend greatly on the season, with different stages of tick biting at different times. Reducing the infection in the tick-mouse cycle may greatly lower human Lyme incidence in some areas. However, research on the effects of various mouse-targeted interventions is limited. One particularly promising method involves administering vaccine pellets to white-footed mice through special bait boxes. In this study, we develop and analyze a mathematical model consisting of a system of nonlinear difference equations to understand the complex transmission dynamics and vector demographics in both tick and mice populations. We evaluate to what extent vaccination of white-footed mice can affect Lyme incidence in I. scapularis, and under which conditions this method is cost-effective in preventing Lyme disease. We find that, in areas with high human risk, vaccination can eliminate mouse-tick transmission of B. burgdorferi while saving money.
Collaborating Authors: Daniel Carrera-Pineyro, Adam Litzler, Andrea McCormack, Josean Velazquez-Molina, Anuj Mubayi, Karen Rıos-Soto, Christopher Kribs
Power Grids
Title: A Machine Learning Approach for Solving AC Optimal Power Flows [PDF]
Summary: My research group partnered with Pacific Northwest National Labs (PNNL) to use machine learning algorithms to solve for optimal generator settings on small-scale power grids. This problem, otherwise known as optimal power flow (OPF), is usually solved with numerical algorithms, but their long computation time limits operators ability to update generator settings in real time. We generated data sets by perturbing IEEE's predefined 30-bus and 300-bus power flow system and trained neural networks and decision tree regression algorithms (XGBoost). We were able to train accurate and quick algorithms for the 30-bus system but were unable to train either algorithm on 300-bus systems.
Collaborating Authors: Deepjyoti Ghosh, Derek Hanely, Kapila Kottegoda, Jenna McDanold, Phong Nguyen, Yuqi Su