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.

Recent Publications

Measuring the impact of spatial perturbations on the relationship between data privacy and validity of descriptive statistics

Background: Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which …

Measuring Multiple Dimensions and Indices of Non-Vaccination Clustering in Michigan: 2008-2018

Michigan experienced a significant measles outbreak in 2019 amidst rising rates of non-medical vaccine exemptions (NMEs) and low …

Racial disparities in COVID-19 mortality are driven by unequal infection risks

Background: As of November 1, 2020, there have been more than 230K deaths and 9M confirmed and probable cases attributable to …

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. …

Talks and Tutorials

Estimating treatment effects from observational spatial data using a difference-in-differences approach

For most problems involving some kind of spatial exposure, e.g. to a neighborhood environment, it is either unethical, impractical, or …

Using simulation to understand frequentist confidence intervals and Bayesian credible intervals as tools for inference

In this example, we’re going to go back to basics, and use both a formula and simulation to calculate confidence intervals for a sample …

How does residential segregation impact infection risk? An interactive exploration

In this tutorial, we’re going to walk through ways in which patterns of residential segregation may impact infectious disease risk …

Likelihood and model fit: A visual tour

Likelihood is a concept that underlies most statistical modeling that falls under the heading of generalized linear model or GLMs.

When …

Smoothing! An interactive tutorial approach to univariate and spatial interpolation

In this tutorial, we will introduce some key concepts and tools for smoothing and visualizing potentially non-linear data. We will …

Recent Posts

Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data

The Backstory: Rising Cases of Measles Despite High Vaccination Coverage
Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data

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.

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Makefiles for fun and profit

All about Makefiles

Meet the Team

Principal Investigator

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Jon Zelner

Assistant Professor

Team

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Joey Dickens

Postdoctoral Fellow

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Kelly Broen

Doctoral Student

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Nina Masters

PhD Candidate

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Paul Delamater

Assistant Professor, UNC

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Ramya Naraharisetti

Doctoral Student

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Rob Trangucci

PhD Candidate

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Ryan Malosh

Assistant Research Scientist

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Stephanie Choi

UI/UX designer

Alumni

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Akshada Shinde

Masters Student

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Alex Cao

Data Scientist

Contact

  • 734-647-9755
  • 2667 SPH Tower 1415 Washington Heights, Ann Arbor, MI 48109-2029