Jon Zelner

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Jon Zelner, PhD, is Assistant Professor in the department of Epidemiology in the University of Michigan School of Public Health. Dr. Zelner holds a second appointment in the Center for Social Epidemiology and Population Health.

Dr. Zelner’s┬áresearch is focused on using spatial analysis, social network analyisis and dynamic modeling to prevent infectious diseases, with a focus on tuberculosis and diarrheal disease. Jon is also interested in understanding how the social and biological causes of illness interact to generate observable patterns of disease in space and in social networks, across outcomes ranging from infection to mental illness.

 

A large spatial cluster of multi-drug resistant tuberculosis (MDR-TB) cases in Lima, Peru is highlighted in red. A key challenge in my work is understanding why these cases cluster in space: can social, spatial, and genetic data tell us where transmission is occurring and how to interrupt it?

A large spatial cluster of multi-drug resistant tuberculosis (MDR-TB) cases in Lima, Peru is highlighted in red. A key challenge in my work is understanding why these cases cluster in space: can social, spatial, and genetic data tell us where transmission is occurring and how to interrupt it?

 

 

Edward Ionides

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Time series analysis with applications to ecology, epidemiology, health economics, cell motion and neuroscience. Methodological work on inference for partially observed stochastic dynamic systems.

The IF2 algorithm (Ionides, EL, D Nguyen, Y Atchade, S Stoev and AA King., 2015, "Inference for dynamic and latent variable models via iterated, perturbed Bayes maps," PNAS 112:719-724).

The IF2 algorithm (Ionides, EL, D Nguyen, Y Atchade, S Stoev and AA King., 2015, “Inference for dynamic and latent variable models via iterated, perturbed Bayes maps,” PNAS 112:719-724).