I am a postdoctoral fellow in the Infectious Disease Dynamics group in the Department of Epidemiology at the Johns Hopkins Bloomberg School of Public Health. I am interested in characterizing the spatiotemporal dynamics of infectious diseases, improving surveillance system design, and understanding the effects of measurement biases on epidemiological inference. The tools I use to answer these questions include: digital health-associated and behavioral data sources, statistical and mathematical models, and interdisciplinary social, ecological, and epidemiological approaches. My research in the ID Dynamics group focuses on mapping the global disease burden of cholera and optimizing the allocation of cholera vaccines.
I completed my PhD in Dr. Shweta Bansal’s lab at Georgetown University in 2017. My dissertation investigated the ability for medical claims to enhance influenza surveillance design and to characterize spatial and age patterns in influenza epidemiology.
The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that local mobility, state-specific vaccination and health insurance policies, and sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing these digital data streams to complement traditional surveillance and enhance surveillance in developing countries.
See this work in PLoS Computational Biology (updated March 2018)
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
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.
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