I am currently working on models that integrate climate and genomic data to improve our understanding and prediction of infectious disease outcomes in a changing climate. My primary focus is on arboviruses, such as dengue virus, Chikungunya virus, and Zika virus, which are strongly influenced by temperature and precipitation. I am conducting this work under the guidance of Dr. Nathan Grubaugh, an Associate Professor specializing in arboviruses, and Dr. Colin Carlson, an Assistant Professor focused on climate epidemiology. I am also fortunate to collaborate with Abbey Porzucek and Yi Ting Chew, PhD students in the department.

My work focuses on two key areas. First, I adapt existing models to integrate novel data sources, such as newly available genomics metadata to enhance predictions of virus evolution and long-term climate data to improve estimates of climate change's impact on disease patterns. The second part of my work focuses on developing new models that integrate these data sources more effectively than traditional epidemiological approaches. For example, we've been working on models that distinguish the effects of climate change from natural climate cycles, such as the El Niño Southern Oscillation (ENSO) climate pattern.

Being part of the Public Health Modeling Unit (PHMU) has been invaluable to this effort. Engaging with colleagues working on a wide range of infectious diseases and public health challenges has introduced me to new and unexpected ways of thinking. Bridging the gap between the microcosm of viruses and the macrocosm of climate requires remaining open to ideas from everyone.

Climate is an inherently complex and dynamic system that influences infectious diseases through many different pathways. In recent years, we've seen how climate change can amplify routine epidemics, underscoring the urgent need for accurate data to quantify these impacts. When we provide concrete data for trends that are real but driven by complex mechanisms, we can better equip decision-makers with the insights they need to take effective action.

Being able to anticipate how infectious disease dynamics will unfold in the short term is fundamental to effective public health practice. Our modeling efforts aim to provide decision-makers with tools that better account for complex climate evolution cycles and trends, enhancing the prediction of disease incidence and distribution to enable them to better address challenges like the upcoming dengue season in the Americas. By offering scenarios with clear, quantitative outcomes, we can make public health action more actionable. Looking ahead, we envision making our models openly accessible, creating valuable resources for understanding and responding to the intersection of climate change and infectious diseases. Ultimately, this will help shape strategies to reduce the burden of infectious diseases in an evolving climate.