Transmission dynamics of infectious diseases. Mathematical modeling and other computational approaches to investigate how environmental change, human activities, and host-pathogen interaction may affect processes in the disease transmission system
Jon Zelner is an Assistant Professor in the Dept. of Epidemiology and Center for Social Epidemiology and Population Health in the UM School of Public Health. His work focuses on understanding the joint contributions of social, biological, and environmental factors to infectious disease transmission dynamics, with a particular focus on Tuberculosis (TB) transmission in high-burden contexts.
To do this, Jon uses mathematical and individual-based models to guide the design of studies and statistical tools for extracting information on infectious disease transmission from real-world spatiotemporal data. This ranges from small-scale simulation of household and community-based transmission to large-scale individual-based models of infectious disease transmission in megacities. A recurring methodological theme of this work is the challenge in navigating the tradeoff between fidelity to real-world processes and the need for parsimonious explanation of observable phenomena.
Marisa Eisenberg is an Associate Professor in the Department of Epidemiology, and in the Department of Mathematics. Her research revolves around mathematical epidemiology, focus on using and developing parameter estimation and identifiability techniques to model disease dynamics. Her group builds multi-scale models of infectious disease, including HPV, cholera and other environmentally driven diseases.
Rafael Meza is a Professor of Epidemiology and Global Health, in U-M School of Public Health, and an Honorary Professor at the Mexico National Institute of Public Health (INSP). Dr. Meza’s is interested in cancer risk assessment and the analysis of cancer epidemiology data using mechanistic models of carcinogenesis. He is also interested in the mathematical modeling of chronic and infectious disease dynamics and its applications in public health policy design.
Dr. Meza is Coordinating Principal Investigator of the Cancer Intervention and Surveillance Modeling Network (CISNET) lung group and Principal Investigator of the Center for the Assessment of Tobacco Regulations (CAsToR), and member of the UM Tobacco Research Network.
Currently, Dr. Meza is developing models to evaluate the impact of screening and smoking cessation on lung cancer risk. Additional projects include the development of methodologies to investigate the effects of infectious disease dynamics on the risk of cancers with infectious disease etiology, modeling the impact of policies on cigarette and smokeless tobacco use, and modeling the impact of diabetes prevention strategies in Mexico.
Jihyoun Jeon is an Assistant Research Scientist in the Department of Epidemiology, in the School of Public Health. She is also a member of the University of Michigan Comprehensive Cancer Center (UMCCC), and an affiliate at the Fred Hutchinson Cancer Research Center (FHCRC). Her research interests focus on developing biologically based mathematical models and statistical methods to evaluate the impact of risk factors on various cancers, and the efficacy of screening to reduce cancer incidence and/or mortality. The goal of these modeling efforts is to better understand the underlying mechanism of the natural history of cancer, develop innovative methodologies to solve important public health questions, and assist public health policy makers in their decision process.
She is a core member of large multidisciplinary national consortia: the Lung Cancer group of the NCI consortium ‘Cancer Intervention and Surveillance Modeling Network (CISNET)’, Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), and Colorectal Transdisciplinary (CORECT) Study in the Genetic Associations and Mechanisms in Oncology (GAME-ON). She is particularly interested in developing mathematical models and simulation tools to investigate the synergistic impacts of tobacco control policies and CT screening on lung cancer risk in the US and in some middle-income countries. And she is also interested in developing risk prediction models for colorectal cancer that incorporate genetic variants identified form GWAS study along with environmental risk factors and modifiable lifestyle factors in population based and prospective studies. These models would provide a more accurate risk stratification of individuals, which would be useful to suggest individually tailored health strategies given the person’s risk profile in terms of genetic variants as well as lifestyle and environmental risk factors collectively.