This series spotlights the amazing students working with faculty from the YSPH Epidemiology of Microbial Diseases (EMD) Department. This month focuses on students who are conducting research in Public Health Modeling. Here, PhD student Yi-Ting Chew tells us about the program and some of her exciting research in a Q&A format.

Please tell us a little about yourself and what inspired you to pursue a PhD in the EMD Department at the Yale School of Public Health?

Yi-Ting: I have a background in systems biology and virology, and I love reading about zoonotic diseases throughout the course of human history. I think it would be especially fascinating to try to reconstruct this history using what we know of pathogen evolution and emergence. Thus, I was excited to start my PhD in the EMD Department because of the fantastic research done here across academic disciplines, particularly in vector ecology, public health surveillance methods, and statistical inference.

What is the current focus of your research?

I research the seasonal dynamics of dengue virus transmission, particularly the spatial variation in how seasonality is disrupted and maintained, and how interactions between dengue variants and other arboviruses impact their epidemiology. Coming from Singapore, I am fascinated by how the same disease unfolds in distinct ways across the Asias and the Americas. I am particularly interested in how consecutive dengue seasons impact circulating virus variants and population-level immunity.

Please give a brief introduction to your work in Public Health Modeling

I mostly work on spatiotemporal models to identify important drivers of transmission, as well as gathering pathogen genomic data for phylogenetic analyses. I also like to draw inspiration from ecology and evolution to develop tools for reconstructing transmission routes.

What are some of the most significant findings or innovations from your research?

I will present my preliminary findings on seasonal dengue dynamics in Brazil at the 2025 VIII PanDengue Network Meeting. Briefly, the emergence of Zika and COVID-19 had contrasting impacts on dengue seasonality, with the former causing widespread interruptions not seen for the latter. I also detected a recent increase in the seasonality of dengue incidence in Southern Brazil.

Which emerging trends or technologies in Public Health Modeling do you find most exciting?

I am most excited for the increasing public availability of pathogen genomes and frameworks for modeling their evolution and epidemiology. I think that these mathematical models are invaluable in testing what we know of their impact on public health and generating interesting ideas for primary research.

Where do you see the field of Public Health Modeling heading in the next few years, and what role do you hope your work will play in this?

I think that Public Health Modeling will be crucial in addressing the uncertainty we have around an increasing number of data sources describing the public health impact of climate change, vaccination strategies, and vector control efforts. I hope to develop useful models, leveraging what we know of infectious disease ecology and evolution, to best identify the public health impact of disease control strategies.

How has the EMD Department supported your research and academic goals?

There are so many wonderful people to work with in the EMD Department, and I also really love the collaborations we have with other departments in the school. I feel incredibly lucky to do my PhD here alongside people who are excited about a broad range of topics in statistical inference and modeling techniques.

What advice would you give to prospective students considering applying to the EMD training programs at Yale?

I think it's important to know which research areas excite you and inspire your commitment, and to then consider how a graduate program can help you achieve those goals. Researching course requirements and how supportive the research community is towards current students and alumni are also key in choosing any graduate program.