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.

Funding

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The Simons Foundation

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The Center for Disease Control and Prevention

Recent Publications

Has the relationship between wealth and HIV risk in Sub-Saharan Africa changed over time? A temporal, gendered and hierarchical analysis

This study examines the relationship between wealth and HIV infection in Sub-Saharan Africa to determine whether and how this …

The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures

Disease surveillance systems are essential for understanding the epidemiology of infectious diseases and improving population health. A well-designed surveillance system can achieve a high level of fidelity in estimates of interest (e.g., disease trends, risk factors) within its operational constraints. Currently, design parameters that define surveillance systems (e.g., number and placement of the surveillance sites, target populations, case definitions) are selected largely by expert opinion and practical considerations. Such an informal approach is less tenable when multiple aspects of surveillance design—or multiple surveillance objectives—need to be considered simultaneously, and are subject to resource or logistical constraints. Here we propose a framework to optimize surveillance system design given a set of defined surveillance objectives and a dynamical model of the disease system under study. The framework provides a platform for in silico surveillance system design, and allows the formulation of surveillance guidelines based on quantitative evidence, tailored to local realities and priorities. The framework is designed to facilitate greater collaboration between health planners and computational and data scientists to advance surveillance science and strengthen the architecture of surveillance networks.

There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk

Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease …

Evaluating Michigan’s Administrative Rule Change on Nonmedical Vaccine Exemptions

OBJECTIVES: Vaccine hesitancy is a growing threat to health in the United States. Facing the fourth highest vaccine exemption rate in …

Pneumonia Following Symptomatic Influenza Infection Among Nicaraguan Children Before and After Introduction of the Pneumococcal Conjugate Vaccine

Influenza is associated with primary viral and secondary bacterial pneumonias; however, the dynamics of this relationship in …

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

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Meet the Team

Principal Investigator

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Jon Zelner

Assistant Professor

Team

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Hannah Steinberg

PhD Student

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Joey Dickens

Postdoctoral Fellow

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Kelly Broen

Doctoral Student

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Krzysztof Sakrejda

Statistical Epidemiologist

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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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Stephanie Choi

UI/UX designer

Former Members

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Nina Masters

PhD Candidate

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Ryan Malosh

Assistant Research Scientist

Contact

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