We are a multidisciplinary research group focused on unraveling the drivers of infectious disease transmission as well as socially and spatially disparate outcomes in infection, morbidity and mortality. This work covers a broad array of pathogens ranging from tuberculosis to influenza, diarrheal disease, COVID-19, and others. Methodologically, our work sits at the interface between infectious disease data and statistical and simulation models. We are motivated by a strong commitment to global and domestic health equity backed by rigorous analysis.

Our work covers a broad array of methods and pathogens, but is grounded in the underlying philosophy of Bayesian inference. This means that we focus on the integration of sources of data across biological, social, and spatial scales, using models that can account for the information and uncertainty associated with these different sources. Because of this, our work is informed by ideas and data from an array of fields including infectious disease epidemiology, molecular genotyping/genomics, spatial statistics, data science, environmental epidemiology, clinical medicine, and others.

Meet the Team

Principal Investigator


Jon Zelner

Assistant Professor



Akshada Shinde

Masters Student


Alex Cao

Data Scientist


Kelly Broen

Doctoral Student


Nina Masters

PhD Candidate


Paul Delamater

Assistant Professor, UNC


Ramya Naraharisetti

Doctoral Student


Rob Trangucci

PhD Candidate


Ryan Malosh

Assistant Research Scientist


Stephanie Choi

UI/UX designer

Recent Publications

The Household Influenza Vaccine Effectiveness Study: Lack of Antibody Response and Protection Following Receipt of 2014–2015 Influenza Vaccine

Background: Antigenically drifted A(H3N2) viruses circulated extensively during the 2014–2015 influenza season. Vaccine effectiveness …

Next-Generation Sequencing of Influenza Viruses in a Household Cohort Accurately Identifies Transmission Pairs and Reveals a Bottleneck Size of Close to One

Background. A detailed understanding of influenza virus transmission is central to the evaluation of public health interventions. …

Identifying hotspots of multidrug-resistant tuberculosis transmission using spatial and molecular genetic data

Background: We aimed to identify and determine the etiology of “hotspots” of concentrated multidrug-resistant tuberculosis …

Influenza Vaccine Effectiveness in the 2014–2015 Season for Children 2–17 Years by Vaccine Type: US Influenza Vaccine Effectiveness Network

Background. Due to the lack of effectiveness of live attenuated influenza vaccine (LAIV) against pandemic H1N1 virus in children during …

Partially observed epidemics in wildlife hosts: modelling an outbreak of dolphin morbillivirus in the northwestern Atlantic, June 2013-2014

Morbilliviruses cause major mortality in marine mammals, but the dynamics of transmission and persistence are ill understood compared …

Recent Posts

Some light summer reading in spatial epidemiology

Spatial Epidemiology

Bringing it all together with GitLab CI

Using GitLab CI

It's not reproducible if it only runs on your laptop.

Make sure it runs.

Makefiles for fun and profit

All about Makefiles

Reproducibility starts at home

Reproducibility and some examples.


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