I am a PhD candidate in Dr. Shweta Bansal’s lab in the Global Infectious Diseases program at Georgetown University. My research with the Bansal Lab examines the spatial and age dynamics of influenza from an epidemiological perspective through quantitative data analysis and modeling. In the long term, I am interested in applying my research on infectious disease dynamics to public health policy.
Studies suggest that spatial heterogeneity in influenza disease burden is driven by mechanisms as varied as vaccination coverage, subtype circulation, environmental factors, travel flows, demographic characteristics, and socioeconomic factors. These hypotheses are primarily supported by country-level or small-scale studies and they exist in fragmented sub-fields of influenza epidemiology. We leverage our access to high resolution medical claims data in the United States to conduct a statistical analysis that characterizes the important drivers driving influenza disease burden. Increasing enrollment in insurance that stems from the Affordable Care Act and improving methods for “big data” in epidemiology will only improve the coverage of medical claims in coming years. This work hopes to demonstrate the utility of medical claims for large-scale ecological analyses and proposes a flexible modeling approach that can be extended to other medical claims diagnosis codes and data sources, changing future applications of infectious disease surveillance data.
See this work on bioRxiv. (updated March 2017)
Influenza spread among human populations is characterized by a strong seasonality in the Northern Hemisphere. Low levels of year-round flu activity are marked by seasonal increases during the winter months; active flu surveillance and vaccination campaigns in the United States endure from October to March in typical seasons. Because children are a relatively susceptible population and classrooms enable children to contact many other individuals on a regular basis, school settings are thought to play a significant role in population-wide influenza spread. Many studies about the role of schools in influenza spread conflate planned school holidays with reactive school closures, and most studied closures are the result of pandemic influenza outbreaks. We expect, however, that school holiday induce population-level changes to disease-causing contact patterns and travel behaviors, and that these changes differ between planned and unplanned closures and seasonal and pandemic outbreaks. The goal of our study is to characterize the downstream effects of holiday contact and travel patterns on population-level transmission rates and spatial spread among children and adults.
See this work at The Journal of Infectious Diseases.
Cholera is a waterborne intestinal infection that causes 3-5 million cases and over 100,000 deaths per year globally. Due to the complex transmission and immunity dynamics of cholera, mathematical models for cholera vary greatly in their structures, in terms of transmission pathways and loss of immunity mechanisms. In this study, we developed multiple mathematical models of cholera transmission and loss of immunity to explore model identifiability, accuracy in parameter estimation, and epidemic forecasting using simulated datasets and empirical data from the 2006 cholera epidemic in Angola. Our goal was to develop an understanding of the types of cholera models best used for parameter estimation and epidemic forecasting in epidemic scenarios, and to shed light on unidentifiable model parameters in order to inform future cholera data collection and study design.
See this work at the Journal of Theoretical Biology. (updated January 2017)
Measures of population-level influenza severity are important for public health planning, but estimates are often based on case-fatality and case-hospitalization risks, which require multiple data sources, are prone to surveillance biases, and are typically unavailable in the early stages of an outbreak. Research on population contact structure suggests that school-aged children are responsible for the bulk of influenza transmission and have the high morbidity because they have the greatest number of contacts, while adults have greater prior immunity, a more heterogeneous immune landscape, and are responsible for connecting high-risk groups to highly connected groups. We are interested in leveraging our knowledge of age-related contact structure and data on age-specific morbidity to develop a proxy for population-level severity. Elucidating the differences between children and adults in flu epidemiology can help public health policymakers target their interventions to reduce health and cost burdens on individuals and healthcare facilities and systems.