Agenda

All times are noted in Eastern Daylight Time (GMT-4).  This is a free Zoom event. Please REGISTER to attend.

9:00 A.M. | Session 1  |  A Year in Review: Lessons Learned and Best Practices

9:00 A.M. — F. DuBois Bowman (Dean, School of Public Health, Professor, Biostatistics, University of Michigan)
OPENING REMARKS

Photo of F. DuBois Bowman

F. DuBois Bowman
Dean and Professor
School of Public Health
University of Michigan

Bio: A renowned expert in the statistical analysis of brain imaging data, F. DuBois Bowman is dean of the University of Michigan School of Public Health, one of the nation’s top public health programs. Dr. Bowman’s work mines massive data sets and has important implications for mental and neurological disorders such as Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, and substance addiction. His research has helped reveal brain patterns that reflect disruption from psychiatric diseases, detect biomarkers for neurological diseases, and determine more individualized therapeutic treatments. Additionally, his work seeks to determine threats to brain health from environmental exposures and optimize brain health in aging populations.

9:10 A.M. — Bhramar Mukherjee (Biostatistics and Epidemiology, U-M)
Predictions, Role of Interventions and the Crisis of Virus in India: A Data Science Call to Arms

Bhramar MukherjeeBhramar Mukherjee
John D. Kalbfleisch Collegiate Professor and Chair of Biostatistics, Professor
Biostatistics and Epidemiology
University of Michigan

Talk Title: Predictions, Role of Interventions and the Crisis of Virus in India: A Data Science Call to Arms

Abstract: India, world’s largest democracy, underwent five phases of lockdown from March 25-June 30 and several phases of unlocking since then. The virus curve appears to have turned the corner from late September and case-counts have been steadily going down. In this presentation, we provide a brief chronicle of the modeling experience of our study team over the last eight months. We start by discussing an extended SIR model for predicting case-counts in India. We then discuss methodological innovations by incorporating selective and imperfect viral testing when using case-counts in an extended SEIR model for COVID-19. We conclude with a strategic visioning exercise on what comes next? This is joint work with many, with all supporting research materials and products available at covind19.org.

Bio: Bhramar Mukherjee is John D. Kalbfleisch Collegiate Professor and Chair, Department of Biostatistics; Professor, Department of Epidemiology, University of Michigan (UM) School of Public Health;  Research Professor and Core Faculty Member, Michigan Institute of Data Science (MIDAS), University of Michigan. She also serves as the Associate Director for Quantitative Data Sciences, The University of Michigan Rogel Cancer Center. She is the cohort development core co-director in the University of Michigan’s institution-wide Precision Health Initiative. Her research interests include statistical methods for analysis of electronic health records, studies of gene-environment interaction, Bayesian methods, shrinkage estimation, analysis of multiple pollutants. Collaborative areas are mainly in cancer, cardiovascular diseases, reproductive health, exposure science and environmental epidemiology. She has co-authored more than 250 publications in statistics, biostatistics, medicine and public health and is serving as PI on NSF and NIH funded methodology grants. She is the founding director of the University of Michigan’s summer institute on Big Data. Bhramar is a fellow of the American Statistical Association and the American Association for the Advancement of Science.  She is the recipient of many awards for her scholarship, service and teaching at the University of Michigan and beyond. Including the Gertrude Cox Award, from the Washington Statistical Society in 2016 and most recently the L. Adrienne Cupples Award, from Boston University in 2020.

9:30 A.M. — Jonathan Zelner (Epidemiology, University of Michigan)

Jon ZelnerJonathan Zelner
Assistant Professor
Epidemiology
University of Michigan

Bio: Jonathan Zelner is an Assistant Professor in the Department of Epidemiology and the Center for Social Epidemiology and Population Health at the University of Michigan. Dr. Zelner is an infectious disease epidemiologist whose research is focused on the intersection of social and biological mechanisms in spatiotemporal patterns of infectious disease transmission. His work covers a broad range of infections with a primary focus on respiratory infections including tuberculosis, influenza and COVID-19, as well as vaccine-preventible diseases and diarrheal disease. Prior to returning to U-M, Jon completed a postdoctoral fellowship in the Dept. of Epidemiology of Microbial Diseases at the Yale School of Public Health (2016), was a Robert Wood Johnson Foundation Health and Society Scholar at Columbia University (2014-16), and was an NIH Research and Policy for Infection Disease Dynamics (RAPIDD) postdoctoral fellow in the Dept. of Ecology and Evolutionary Biology at Princeton University (2011-2014).

9:50 A.M. — Joseph Eisenberg (Epidemiology, U-M)
Trade-offs between quarantine length and compliance when optimizing COVID-19 control

Photo of Joseph EisenbergJoseph Eisenberg
Department Chair, Epidemiology
John G Searle Professor of Public Health
Epidemiology, Global Public Health
University of Michigan

Talk Title: Trade-offs between quarantine length and compliance when optimizing COVID-19 control

 Abstract: Guidance on quarantine duration is often based on the maximum observed incubation periods assuming perfect compliance. However, the impact of longer quarantines may be subject to diminishing returns. The largest benefits of quarantine occur over the first few days while the probability of presenting with symptoms after the median incubation period decreases over time. Additionally, the financial and psychological burdens of quarantine are cumulative, and may motivate increases in non-compliance behavior over time. We present a quantitative analysis to determine the optimal length of quarantine to minimize transmission. We use a deterministic transmission model to simulate the interaction between non-pharmaceutical interventions (contact tracing, quarantine, and isolation) and disease progression. Numerous non-compliance scenarios are considered with recommended quarantine durations of 7, 10, and 14 days.  The relation between non-compliance behavior and disease risk is modeled using a time-varying function of leaving quarantine among the quarantined individuals across all stages of disease progression before symptom onset.

Bio: Dr. Eisenberg is the John G. Searle endowed Chair and Professor of Epidemiology in the School of Public Health at the University of Michigan. Dr. Eisenberg received his PhD in Bioengineering in the joint University of California, Berkeley/University of California, San Francisco program, and an MPH from the School of Public Health at the University of California, Berkeley. Dr. Eisenberg studies infectious disease epidemiology with a focus on waterborne and vectorborne diseases. His broad research interests, global and domestic, integrate theoretical work in developing disease transmission models and empirical work in designing and conducting epidemiology studies. He is especially interested in the environmental determinants of infectious diseases.

10:10 A.M. — Break

Session 1 will resume at at 10:20 A.M.

10:20 A.M. — Peter Song (Biostatistics, U-M)
Lessons Learned from the COVID-19 Pandemic by a Statistician: a Health Data Science Perspective

Peter Song
Professor
Biostatistics
University of Michigan

Talk Title: Lessons Learned from the COVID-19 Pandemic by a Statistician: a Health Data Science Perspective

Abstract: In this talk I would like to share some lessons that I learned from the COVID-19 pandemic from the perspectives of health data science. To fight for such an unprecedented global health crisis of the infectious disease, statistical methods have been applied in almost every corner of efforts in the scientific community to generate relevant and rigorous information, knowledge and points of views, assisting the developments of disease monitoring systems, effective control measures, medical treatments, vaccines and distribution strategies, and policies. An overview of strengths and weaknesses on data collection, data processing, statistical modeling, and information dissemination can help advance health data science in significant ways. It motivates to further develop adequate methodologies and toolboxes in support of a continuous effort on our ongoing battle against the COVID-19 pandemic and a better preparation for the outbreak of a future pandemic.

Bio: Peter Song is a Professor of Biostatistics at the Department of Biostatistics, School of Public Health, University of Michigan. He received his PhD in Statistics from the University of British Columbia in 1996. Prior to the appointment at the University of Michigan, he was a faculty member at the Department of Statistics and Actuarial Science, University of Waterloo (2004-2007) and a faculty member at the Department of Mathematics and Statistics, York University, Toronto (1996-2004). Peter Song’s research interests include bioinformatics, longitudinal data analysis, missing data problems in clinical trials, statistical genetics, and time series analysis. He is interested in methodological developments related to modelling, statistical inference and applications in biomedical sciences. In particular, Dr. Song’s research projects are strongly motivated from real world data analysis. In 2007 he published a monograph “Correlated Data Analysis: Modeling, Analytics and Applications” by Springer.

10:40 A.M. — Sabrina Corsetti & Thomas Schwarz (Physics, U-M)
Data-driven Modeling of the COVID-19 Pandemic Using Penalized Linear Regression

Talk Title: Data-driven Modeling of the COVID-19 Pandemic Using Penalized Linear Regression

Abstract: Since early 2020, the push to subdue the COVID-19 pandemic has brought unprecedented levels of attention to disease modeling efforts. The U-M COVID-19 model presented in this talk was developed in response to a demand for COVID-19 spread, hospitalization, and mortality predictions. The model makes regional, state, and national predictions for these three data categories using ridge regression, a machine learning algorithm rooted in penalized linear regression. To make its predictions, the model learns the relationship between consecutive sets of COVID-19 data points. Once the model has learned the necessary relationships, it can produce future predictions indefinitely, with uncertainties given by the boot-strapping method. As of April 2021, the model makes its predictions based on varying combinations of case, hospitalization, death, social distancing, and testing data. However, the model is highly flexible and capable of making predictions based on any combination of inputs. This talk presents the underlying mathematics of the model, as well as its prediction performance at the national and regional levels.

Thomas SchwarzThomas Schwarz
Associate Professor
Physics
University of Michigan

Bio: Professor Schwarz is an experimental particle physicist who has performed research in astro-particle physics, collider physics, as well as in accelerator physics and RF engineering. His current research focuses on discovering new physics in high-energy collisions at the Large Hadron Collider (LHC) at CERN.

The LHC is the world’s largest (17-mile circumference) and highest-energy (~ 13 TeV) particle collider. Colliding protons at these energies allows scientists to reproduce the conditions of the Universe 1/100th of a billionth of a second after the Big Bang. By studying these collisions, scientists at the LHC hope to answer fundamental questions about the nature of the Universe, such as why do particles have mass, why is there more far more matter than antimatter, and what is the nature of the dark matter.

At the LHC, Professor Schwarz performs research at the ATLAS experiment, a 7000-ton detector the size of a 7-story building built around the collision point of the beam. His work at ATLAS focuses on searching for evidence of dark matter produced in proton collisions, possible new physics in the Higgs sector, and studying the top quark, which is the heaviest known fundamental particle (more massive than an atom of Silver).

Sabrina CorsettiSabrina Corsetti
Undergraduate Student Researcher
Physics and Mathematical Science
University of Michigan

Bio: Sabrina Corsetti is a Senior in LSA majoring in Honors Physics and Mathematical Sciences. Sabrina’s journey with particle physics began in Fall 2017. Her experience includes a stint with the MUSE Collaboration under Professor Lorenzon as well as a 2019 semester at CERN, funded by US ATLAS SUPER, working for the ATLAS Collaboration under Professor Junjie Zhu. Sabrina continues to work at U-M under Professor Tom Schwarz. She plans to pursue a PhD in Applied Physics and transition into renewable energy research.

11:00 A.M. — Andrew Brouwer (Epidemiology, U-M)
Modeling to Guide Early COVID-19 Policy: Forecasts, Scenarios, and Counterfactuals

Andrew Brouwer HeadshotAndrew Brouwer
Assistant Research Scientist and Adjunct Lecturer
Epidemiology
University of Michigan

Talk Title: Modeling to Guide Early COVID-19 Policy: Forecasts, Scenarios, and Counterfactuals

Abstract: Early on in the COVID-19 epidemic in Michigan, there were many unknowns but action was needed. Policymakers turned to modelers to better understand where the outbreak was likely to go and how interventions might impact it. Collaborations quickly developed between infectious disease modelers at the University of Michigan and epidemiologists at the Michigan Department of Health and Human Services. To meet the needs of the State in the face of parameter uncertainty and unidentifiability, we employed a hybrid sampling-estimation approach to develop short-term forecasts. Over the course of the first waves of infections, the needs of policy makers evolved, and we pivoted to exploring longer-term social distancing scenarios and evaluating the likely impact of the Michigan Stay Home, Stay Safe order. In this talk, we will provide an overview of our modeling approach and activities early in the outbreak, as well as some of the challenges and lessons learned.

Bio: Andrew Brouwer is mathematical epidemiologist and modeler. He is currently an Assistant Research Scientist in the Department of Epidemiology at the University of Michigan. He received his BA in mathematics and chemistry and MA in mathematics from the SUNY College at Potsdam (2009) and his MS in environmental science and engineering from Clarkson University (2011). Andrew also earned his MS in applied and interdisciplinary mathematics (2013), MA in statistics (2015), and PhD in applied and interdisciplinary mathematics (2015) at the University of Michigan. He was a postdoctoral research fellow in the Department of Epidemiology at the University of Michigan (2015-16) prior to joining the faculty as a research faculty member.

11:20 A.M. — Break

Session 2 will begin at at 11:30 A.M.

11:30 A.M. | Session 2  |  Moving into the Vaccine Era

11:35 A.M. — Siqian Shen (Industrial and Operations Engineering, & Civil and Environmental Engineering, U-M)
Decision Models for Resource Allocation, Preparation and Response to Future COVID-19 Infection Uncertainty and other Disease Outbreaks

Siqian ShenSiqian Shen
Associate Professor
Industrial & Operations Engineering, Civil and Environmental Engineering
University of Michigan

Talk Title: Decision Models for Resource Allocation, Preparation and Response to Future COVID-19 Infection Uncertainty and other Disease Outbreaks

Abstract: The outbreak of coronavirus disease 2019 (COVID-19) has created a global health crisis and responses in different areas are deeply influenced by local, national and global policies and decisions. In this talk, we first discuss the impacts of the COVID-19 pandemic on transportation, logistics & supply chains, healthcare and the related social inequality issues. We show how mathematical models and data analytics tools can be used for improving decisions under the uncertainty of future COVID-19 infection trends, so that we can better prepare and respond more effectively and efficiently to future disease outbreaks. This talk includes a few examples of our group’s work on COVID-19 related issues and problem solving, including models for isolated facility de-desification, public transit system redesign, as well as allocation and distribution of vaccine or other critical medical resources during the pandemic. The approaches are validated using real COVID-19 infection, business and local mobility data. Results of these work are published and updated at: https://sites.google.com/umich.edu/decision-tools-for-covid19/

Bio: Siqian Shen is an Associate Professor of Industrial and Operations Engineering at the University of Michigan and also serves as an Associate Director in the Michigan Institute for Computational Discovery & Engineering (MICDE). She obtained a B.S. degree from Tsinghua University in 2007 and Ph.D. from the University of Florida in 2011. Her theoretical research interests are in integer programming, stochastic/robust optimization, and network optimization. Applications include optimization and risk analysis of energy, healthcare, cloud computing, and transportation systems. She is a recipient of the IIE Pritsker Doctoral Dissertation Award, IBM Smarter Planet Innovation Faculty Award, and Department of Energy (DoE) Early Career Award, and several best paper prizes from INFORMS.

11:55 A.M. — Jim Koopman (Epidemiology, U-M) & Carl Simon (Mathematics, Complex Systems, & Public Policy, U-M)
Modeling SARS-CoV-2 Vaccine Control of Variant Viruses

Talk Title: Modeling SARS-CoV-2 Vaccine Control of Variant Viruses

Abstract: SARS-CoV-2 variants have appeared that enhance replication and/or evade immunity from prior infection or vaccination. Should we adopt surveillance, modeling, and control strategies with annual changes in the vaccine like those used for influenza? To deal with this question, we have developed a model that can be used for either influenza or SARS-CoV-2. This model differs from all previous transmission models in that it integrates immune waning and genetic drifting into ongoing processes where waned immunity leads to the reinfections subject to a selective force for escape mutations. The genetic proof-reading mechanism of coronaviruses, which influenza lacks, leads to a higher proportion of genetic changes that enhance replication or evade immunity. A pattern of bouncing between extreme escape mutations emerges which eventually settles down to equilibrium conditions. We will discuss how this is relevant to control issues and future development of better models and better data for vaccine composition decisions.

Jim KoopmanJim Koopman
Professor Emeritus
Epidemiology
University of Michigan

Bio: Jim Koopman is a professor emeritus of epidemiology currently living in Florida. He started his career in public health service and after getting tenure and taking a leave to set up a national epidemiology program in Mexico, he changed his career to become a complex systems scientist modeling infection transmission. Since that time, 35 years ago, his motto has been “Developing Theory that Serves the Public Health”. His theory development has been in general areas of infection surveillance and control, HIV, rotavirus, and diverse other infections. He developed a general theory of how to categorize and investigate joint effects of multiple variables that is applicable to both infectious and non-infectious diseases. He was pulled out of retirement by the pandemic and resumed working with Carl Simon, with whom he has shared two honors from prominent journals for the best paper in the journal.

Photo of Carl SimonCarl Simon
Professor Emeritus
Mathematics, Complex Systems, Public Policy
University of Michigan

Bio: Carl P. Simon is professor emeritus of mathematics, economics, complex systems, and public policy. He was the founding director of the U-M Center for the Study of Complex Systems and a former director of the Science, Technology, and Public Policy program at the Ford School. His research centered on the theory and application of dynamical systems: from economic systems in search of equilibrium, to political systems in search of optimal policies, ecosystems responding to human interactions, and especially to the dynamics of the spread of contagious diseases. His current research centers on the spread of crime, the initiation of teenage smoking, and health issues that affect SES. He was named the LSA Distinguished Senior Lecturer for 2007 and received the U-M Distinguished Faculty Achievement Award in 2012. He taught calculus at the Ford School, including “algebraic aerobics.” He received his PhD in mathematics from Northwestern University.

12:15 P.M. — Lisa Prosser (Pediatrics, & Health Management and Policy, U-M) & David Hutton (Health Management and Policy, U-M)
Cost-Effectiveness of Vaccination to Prevent COVID-19 Illness

Talk Title: Cost-Effectiveness of Vaccination to Prevent COVID-19 Illness

Abstract: Although COVID-19 vaccines are currently being provided free of cost to recipients, it is important to measure the economic value of government investment in this vaccination program to inform ongoing decisions for this and future pandemics. This study will use simulation modeling to project costs and health outcomes for available and pending vaccine products to evaluate the cost-effectiveness of vaccination against COVID-19 illness, with special emphasis on pediatric populations. Variation by different age/risk groups and assuming various levels of risk mitigation strategies will be considered. We will pursue two separate but related modeling approaches for evaluating the cost-effectiveness of alternative vaccination strategies: (1) Model 1. A static model which will evaluate the cost-effectiveness of vaccination without considering transmission effects to other individuals and (2) Model 2. A combined dynamic transmission and economic model in which we will use information from Model 1 to estimate mean costs and QALYs associated with COVID-19 illness and combine these with the projected number of cases in a second quantitative model to generate cost-effectiveness results that incorporate the effects of transmission. The results of this analysis will be directly relevant for policy decisions on vaccination recommendations.

Photo of Lisa ProsserLisa Prosser
Assistant Dean for Research Faculty, Medical School
Professor, Pediatrics and Health Management & Policy
University of Michigan

Bio: Dr. Prosser is a Professor and Director of the Susan B. Meister Child Health Evaluation and Research Center. Her research focuses on measuring the value of childhood health interventions using methods of decision sciences and economics. Current research topics include newborn screening programs, vaccination programs, and methods for valuing family spillover effects of illness. She also uses quantitative survey methods (health utility assessment, conjoint analysis) to measure health-related quality of life and preferences for health interventions.

Dr. Prosser’s research evaluating the cost-effectiveness of vaccination programs has been used in setting national vaccine policy for children and adults. Her work with the Evidence Review Group for the Advisory Committee on Heritable Disorders in Newborns and Children using decision science modeling to project long-term health outcomes for proposed newborn screening programs has been used to inform national newborn screening policy decisions. She is currently a member of the evidence review group for the Advisory Committee on Heritable Disorders in Newborns and Children and the ACIP Zoster Working Group.

Dr. Prosser has contributed to faculty development programs at the department, school, and institutional level. She currently serves as the Assistant Dean for Research Faculty at the Medical School. Dr. Prosser also holds an adjunct faculty appointment at the Harvard School of Public Health.

Photo of David HuttonDavid Hutton
Associate Professor
Health Management & Policy, Global Public Health, and Industrial & Operations Engineering
University of Michigan

Bio:David Hutton holds a PhD from Stanford’s department of Management Science and Engineering with a focus on health policy modeling. Prior to joining Stanford’s PhD program, David worked for a consulting company that focused on mathematical modeling and for several silicon valley software companies. David’s current research is focused on health policy and medical decision making, in particular the use of mathematical models to assist with the allocation of resources for health. His research and influence on national and international hepatitis B policy earned him the first place prize in the “Doing Good with Good OR student paper competition” from the Institute for Operations Research and Management Science. He has served as a consultant, advisor, and/or collaborator with the World Health Organization, the US Department of Health and Human Services, and the Centers for Disease Control and Prevention.

12:35 P.M. — BREAK

Session 3 will begin at 1:00 P.M.

1:00 P.M. | Session 3  |  Modeling in a Campus Context

1:00 P.M. — President Mark S. Schlissel
SESSION 3 OPENING REMARKS

8/5/15 University of Michigan President Mark S. Schlissel.

Mark S. Schlissel
University of Michigan President

Dr. Mark S. Schlissel is the 14th president of the University of Michigan and the first physician-scientist to lead the institution.

Read more about University of Michigan President Mark S. Schlissel.

 

 

 

1:05 P.M. — Marisa Eisenberg (Epidemiology, Complex Systems, & Mathematics, University of Michigan)

Photo of Marisa EisenbergMarisa Eisenberg
Associate Professor
Epidemiology, Complex Systems, and Mathematics
University of Michigan

Bio: Marisa Eisenberg received her PhD and M.S. in Biomedical Engineering from the University of California, Los Angeles in 2009. She then spent three years as a postdoctoral fellow studying mathematical biology at the Mathematical Biosciences Institute at Ohio State University, before joining the faculty at University of Michigan as an assistant professor in the Department of Epidemiology.

Dr. Eisenberg’s lab’s research program is in mathematical epidemiology, and is focused on using and developing parameter estimation and identifiability techniques to model disease dynamics. Much of their work is in building multi-scale models of infectious diseases, including examining cholera, environmentally driven diseases, and HPV dynamics. Their research blends mathematics, statistics, and epidemiology to understand transmission dynamics, inform optimal intervention strategies, and improve forecasting.

1:35 P.M. — Jesse Capecelatro (Mechanical Engineering, U-M)
Addressing challenges of COVID-19 through the lens of fluid dynamics

Jesse CapecelatroJesse Capecelatro
Assistant Professor
Mechanical Engineering
University of Michigan

Talk Title: Addressing challenges of COVID-19 through the lens of fluid dynamics

Abstract: It is now widely recognized that COVID-19 is predominantly spread through airborne transmission and most often in indoor settings. Assessing the risk of transmission remains challenging. The number of infectious particles that could be inhaled is typically estimated using simplified models under the assumption of well-mixed environments. However, enclosed spaces are rarely well mixed. In this talk, I will present past and ongoing efforts to characterize transmission of COVID-19 for several campus activities. Using high-resolution simulations of a realistic cough and simulations of disease transmission on campus buses and classrooms, I will show how fluid dynamics controls the spread of COVID-19 and how such data can be leveraged to provide better tools for analyzing risk.

Bio: Jesse Capecelatro is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan. His research group develops numerical methods and data-driven approaches for the prediction and optimization of “messy turbulent flows” relevant to energy and the environment (often multiphase and reacting). Prior to joining Michigan in 2016, Dr. Capecelatro was a research scientist at the Center for Exascale Simulation of Plasma-coupled Combustion (XPACC) at the University of Illinois. He received a B.S. from SUNY Binghamton in 2009, a M.S. from the University of Colorado Boulder in 2011, and a Ph.D. from Cornell in 2014. He is a recipient of the National Science Foundation CAREER Award, Office of Naval Research Young Investigator Award, and the ASME Pi Tau Sigma Gold Medal Award.

1:55 P.M. — Gustavo de los Campos (Epidemiology and Biostatistics, Michigan State University) & Jade Mitchell (Biosystems & Agricultural Engineering, Michigan State University)

Photo of Gustavo de los CamposGustavo de los Campos
Professor of Epidemiology
Associate Chair and Director of Biostatistics
Michigan State University

Bio: Gustvavo de los Campos is an Associate Professor in the Department of Epidemiology and Biostatistics.  His background and research interests center on quantitative and statistical genetics with a focus on the analysis and prediction of complex traits and diseases.

His research involves methods and software development for the analysis of big omic data and applications across different traits and diseases in humans as well as in plants and animals.

Photo of Jade MitchellJade Mitchell
Associate Professor
Biosystems and Agricultural Engineering
Michigan State University

Bio: Dr. Jade Mitchell is Associate Professor in Biosystems and Agricultural Engineering. Her research focuses on human health risk analysis. She is collaborating principal investigator in the Center for Advancing Microbial Risk Assessment (CAMRA), where her work includes characterizing the risks associated with human exposures to pathogenic agents in environmental media (i.e. air, water and soil). Using quantitative microbial risk assessment her work informs policy and risk management decisions about the appropriate level of concern for public health and public safety related to bio-defense, food safety and water quality. Mitchell uses a number of computational tools including statistical analyses, contaminant fate and transport modeling, human exposure modeling, cost-benefit analyses and decision analytic modeling to evaluate both microbial and chemical stressors in her current work.

2:15 P.M. — BREAK

Session 3 will resume at 2:25 P.M.

2:25 P.M. — Jenna Wiens (Computer Science and Engineering, U-M)
Identifying Patients at Greatest Risk of Complications from COVID-19

Photo of Jenna WiensJenna Wiens
Associate Professor
Computer Science and Engineering
University of Michigan

Talk Title: Identifying Patients at Greatest Risk of Complications from COVID-19

Abstract: In response to the strain on the hospital system induced by a growing number of COVID-19 cases, our interdisciplinary team composed of computer scientists and clinicians developed M-CURES – the Michigan COVID-19 Utilization and Risk Evaluation System. This machine learning based approach to clinical decision support aims to identify who among patients diagnosed with COVID-19 is at highest (and lowest) risk of requiring intensive care during hospitalization. Applied to a held-out cohort of COVID-19 patients at Michigan Medicine, the proposed model outperformed a widely implemented proprietary deterioration index. We are now exploring the integration of this model into the EHR and clinical workflow. Going forward, such a model could help clinicians during a future surge in hospitalizations or as an early warning system applied to the general inpatient population.

Bio: Professor Wiens currently heads the MLD3 research group, and is the Co-Director of Precision Health at the University of Michigan. Her primary research interests lie at the intersection of machine learning, data mining, and healthcare. Within machine learning, she is particularly interested in time-series analysis, transfer/multitask learning, causal inference, and learning intelligible models. The overarching goal of her research is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge. Her work has applications in modeling disease progression and predicting adverse patient outcomes. For several years now, Prof. Wiens has been focused on developing accurate patient risk stratification approaches that leverage spatiotemporal data, with the ultimate goal of reducing the rate of healthcare-associated infections among patients admitted to hospitals in the US. In addition to her research in the healthcare domain, she also spends a portion of my time developing new data mining techniques for analyzing player tracking data from the NBA.

Professor Wiens received her PhD in 2014 from MIT.  In 2015, she was named Forbes 30 under 30 in Science and Healthcare, shereceived an NSF CAREER Award in 2016, and in 2017 she was named to the MIT Tech Review’s list of 35 Innovators Under 35. Most recently, Prof. Weins received a Sloan Fellowship in Computer Science.

2:45 P.M. — Luis Zaman (Ecology and Evolutionary Biology, & Complex Systems, University of Michigan)
Learning to Swim in the Sea: Lessons for a Post-2020 World

Photo of Luis ZamanLuis Zaman
Assistant Professor
Complex Systems, Ecology and Evolutionary Biology
University of Michigan

Talk Title: Learning to Swim in the Sea: Lessons for a Post-2020 World

Bio: Luis Zaman received a dual-degree Ph.D. in Computer Science and Ecology, Evolutionary Biology & Behavior from Michigan State University in 2014. From 2014 to 2017, Luis was an NSF Postdoctoral Fellow in the Department of Biology at the University of Washington. His research blends computational and microbial evolution experiments in carefully controlled/engineered environments to better understand how complex communities shape what traits are adaptive.

Luis describes his work this way: Although my academic life started in computer science, my research interests are centered around evolutionary biology. I’m interested in using whatever technology and technique I can to understand fundamental processes in evolution. I once heard this kind of approach described as “undisciplined science,” and I’m quite fond of that phrasing. Prior to his current appointment, Luis has been working as an NSF Postdoctoral Fellow at the University of Washington in the Department of Biology.

3:05 P.M. — Seth Guikema (Industrial and Operations Engineering, & Civil and Environmental Engineering, University of Michigan)

Photo of Seth GuikemaSeth Guikema
Professor
Civil & Environmental Engineering, Industrial & Operations Engineering
University of Michigan

Bio: Seth Guikema is a Professor in the departments of Industrial & Operations Engineering and Civil and Environmental Engineering at the University of Michigan. He is also a Professor II (adjunct) in the Department of Safety, Economics, and Planning at the University of Stavanger as well as a Data Science Research Fellow at One Concern, Inc. The research in Professor Guikema’s group is described in this web site, particularly under the Research tab.

Professor Guikema completed a B.S. in Civil & Environmental Engineering at Cornell University, a M.S. in Civil & Environmental Engineering at Stanford, a M.Eng. by thesis in Civil Engineering at the University of Canterbury (New Zealand), and a Ph.D. in Engineering Risk and Decision Analysis in the Department of Management Science and Engineering at Stanford University. He was a postdoctoral research at Cornell University (Civil & Environmental Engineering). Prior to the University of Michigan he was an Assistant Professor in the Civil Engineering department at Texas A&M University and an Assistant and tenured Associate Professor in the Department of Geography and Environmental Engineering at Johns Hopkins University. He moved to the University of Michigan in 2015.

In 2020 Professor Guikema was the President of the Society for Risk Analysis (SRA). He previously served as the chairperson of both the Foundations of Risk Analysis and Engineering and Infrastructure specialty groups and served a three-year term on the SRA Council. In 2021 he was named a Fellow of the Society for Risk Analysis. He currently serves as the Area Editor for the Natural Hazards area for Risk Analysis and as the Area Editor for the Mathematical Modeling area for Risk Analysis. He is active in INFORMS and served on the INFORMS Decision Analysis Society Council for three years. He also is active in the American Statistical Association and the American Society of Civil Engineers.

3:25 P.M. — BREAK

The panel discussion will begin at 3:30 P.M.

3:30 P.M. | Panel Discussion

Moderated by Emily Martin (Epidemiology, University of Michigan)

Panel Discussion moderated by Emily Martin
(Epidemiology, University of Michigan)

We will collect questions throughout the day. Please submit questions using THIS FORM. We will address as many as time allows.

Emily Martin

(Moderator)

Associate Professor
Epidemiology
University of Michigan

Dr. Martin received a Ph.D. and M.P.H.in Epidemiology from the University of Washington, and a B.S. in Microbiology from the University of Michigan. Dr. Martin's laboratory focuses on the molecular epidemiology of viral and bacterial pathogens, including a specific focus on persistent and co-infecting pathogens. She conducts infectious disease studies in hospitals and outpatient settings and has a particular interest in severe and outcomes in individuals with chronic conditions. Dr. Martin currently participates in two CDC-funded U.S. influenza vaccine effectiveness networks based in ambulatory care and hospital settings.

Emily Martin
(moderator)
Associate Professor
Epidemiology
University of Michigan

Jesse Capecelatro

Assistant Professor
Mechanical Engineering
University of Michigan

Jesse Capecelatro is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan. His research group develops numerical methods and data-driven approaches for the prediction and optimization of “messy turbulent flows” relevant to energy and the environment (often multiphase and reacting). Prior to joining Michigan in 2016, Dr. Capecelatro was a research scientist at the Center for Exascale Simulation of Plasma-coupled Combustion (XPACC) at the University of Illinois. He received a B.S. from SUNY Binghamton in 2009, a M.S. from the University of Colorado Boulder in 2011, and a Ph.D. from Cornell in 2014. He is a recipient of the National Science Foundation CAREER Award, Office of Naval Research Young Investigator Award, and the ASME Pi Tau Sigma Gold Medal Award.

Jesse Capecelatro
Mechanical Engineering
University of Michigan

Amy Cohn

Professor
Industrial and Operations Engineering
University of Michigan

Amy Ellen Mainville Cohn is an Alfred F. Thurnau Professor in the Department of Industrial and Operations Engineering at the University of Michigan, where she also holds an appointment in the Department of Health Management and Policy in the School of Public Health. Dr. Cohn is the Associate Director of the Center for Healthcare Engineering and Patient Safety (CHEPS). She holds an A.B. in applied mathematics, magna cum laude, from Harvard University and a PhD in operations research from the Massachusetts Institute of Technology. Her primary research interests are in applications of combinatorial optimization, particularly to healthcare and aviation, and to the challenges of optimization problems with multiple objective criteria.

Amy Cohn
Industrial and Operations Engineering
University of Michigan

Gustavo de los Campos

Dr. Gustvavo de los Campos is an Associate Professor in the Department of Epidemiology and Biostatistics. His background and research interests center on quantitative and statistical genetics with a focus on the analysis and prediction of complex traits and diseases.

His research involves methods and software development for the analysis of big omic data and applications across different traits and diseases in humans as well as in plants and animals.

Gustavo de los Campos
Epidemiology and Biostatistics
Michigan State University

Marisa Eisenberg

Marisa Eisenberg
Associate Professor
Epidemiology, Complex Systems, and Mathematics
University of Michigan

Marisa Eisenberg received her PhD and M.S. in Biomedical Engineering from the University of California, Los Angeles in 2009. She then spent three years as a postdoctoral fellow studying mathematical biology at the Mathematical Biosciences Institute at Ohio State University, before joining the faculty at University of Michigan as an assistant professor in the Department of Epidemiology.

Dr. Eisenberg’s lab’s research program is in mathematical epidemiology, and is focused on using and developing parameter estimation and identifiability techniques to model disease dynamics. Much of their work is in building multi-scale models of infectious diseases, including examining cholera, environmentally driven diseases, and HPV dynamics. Their research blends mathematics, statistics, and epidemiology to understand transmission dynamics, inform optimal intervention strategies, and improve forecasting.

Marisa Eisenberg
Epidemiology, Mathematics, Complex Systems
University of Michigan

Seth Guikema

Professor
Civil & Environmental Engineering, Industrial & Operations Engineering
University of Michigan

Seth Guikema is a Professor in the departments of Industrial & Operations Engineering and Civil and Environmental Engineering at the University of Michigan. He is also a Professor II (adjunct) in the Department of Safety, Economics, and Planning at the University of Stavanger as well as a Data Science Research Fellow at One Concern, Inc. The research in Professor Guikema’s group is described in this web site, particularly under the Research tab.

Professor Guikema completed a B.S. in Civil & Environmental Engineering at Cornell University, a M.S. in Civil & Environmental Engineering at Stanford, a M.Eng. by thesis in Civil Engineering at the University of Canterbury (New Zealand), and a Ph.D. in Engineering Risk and Decision Analysis in the Department of Management Science and Engineering at Stanford University. He was a postdoctoral research at Cornell University (Civil & Environmental Engineering). Prior to the University of Michigan he was an Assistant Professor in the Civil Engineering department at Texas A&M University and an Assistant and tenured Associate Professor in the Department of Geography and Environmental Engineering at Johns Hopkins University. He moved to the University of Michigan in 2015.

In 2020 Professor Guikema was the President of the Society for Risk Analysis (SRA). He previously served as the chairperson of both the Foundations of Risk Analysis and Engineering and Infrastructure specialty groups and served a three-year term on the SRA Council. In 2021 he was named a Fellow of the Society for Risk Analysis. He currently serves as the Area Editor for the Natural Hazards area for Risk Analysis and as the Area Editor for the Mathematical Modeling area for Risk Analysis. He is active in INFORMS and served on the INFORMS Decision Analysis Society Council for three years. He also is active in the American Statistical Association and the American Society of Civil Engineers.

Seth Guikema
Industrial & Operations Engineering
University of Michigan

Jade Mitchell

Jade Mitchell
Associate Professor
Biosystems and Agricultural Engineering
Michigan State University

Dr. Jade Mitchell is Associate Professor in Biosystems and Agricultural Engineering. Her research focuses on human health risk analysis. She is collaborating principal investigator in the Center for Advancing Microbial Risk Assessment (CAMRA), where her work includes characterizing the risks associated with human exposures to pathogenic agents in environmental media (i.e. air, water and soil). Using quantitative microbial risk assessment her work informs policy and risk management decisions about the appropriate level of concern for public health and public safety related to bio-defense, food safety and water quality. Mitchell uses a number of computational tools including statistical analyses, contaminant fate and transport modeling, human exposure modeling, cost-benefit analyses and decision analytic modeling to evaluate both microbial and chemical stressors in her current work.

Jade Mitchell
Biosystems and Agricultural Engineering
Michigan State University

Jenna Wiens

Associate Professor
Computer Science and Engineering
University of Michigan

Professor Wiens currently heads the MLD3 research group, and is the Co-Director of Precision Health at the University of Michigan. Her primary research interests lie at the intersection of machine learning, data mining, and healthcare. Within machine learning, she is particularly interested in time-series analysis, transfer/multitask learning, causal inference, and learning intelligible models. The overarching goal of her research is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge. Her work has applications in modeling disease progression and predicting adverse patient outcomes. For several years now, Prof. Wiens has been focused on developing accurate patient risk stratification approaches that leverage spatiotemporal data, with the ultimate goal of reducing the rate of healthcare-associated infections among patients admitted to hospitals in the US. In addition to her research in the healthcare domain, she also spends a portion of my time developing new data mining techniques for analyzing player tracking data from the NBA.

Professor Wiens received her PhD in 2014 from MIT. In 2015, she was named Forbes 30 under 30 in Science and Healthcare, shereceived an NSF CAREER Award in 2016, and in 2017 she was named to the MIT Tech Review’s list of 35 Innovators Under 35. Most recently, Prof. Weins received a Sloan Fellowship in Computer Science.

Jenna Wiens
Computer Science and Engineering
University of Michigan

Luis Zaman

Luis Zaman
Assistant Professor
Complex Systems
University of Michigan

Luis Zaman received a dual-degree Ph.D. in Computer Science and Ecology, Evolutionary Biology & Behavior from Michigan State University in 2014. From 2014 to 2017, Luis was an NSF Postdoctoral Fellow in the Department of Biology at the University of Washington. His research blends computational and microbial evolution experiments in carefully controlled/engineered environments to better understand how complex communities shape what traits are adaptive.

Luis describes his work this way: Although my academic life started in computer science, my research interests are centered around evolutionary biology. I’m interested in using whatever technology and technique I can to understand fundamental processes in evolution. I once heard this kind of approach described as “undisciplined science,” and I’m quite fond of that phrasing. Prior to his current appointment, Luis has been working as an NSF Postdoctoral Fellow at the University of Washington in the Department of Biology.

Luis Zaman
Complex Systems, Ecology and Evolutionary Biology
University of Michigan

1:00 P.M. | Session 3  |  Modeling in a Campus Context

1:05 P.M. — Marisa Eisenberg (Epidemiology, Complex Systems, & Mathematics, University of Michigan)

Photo of Marisa EisenbergMarisa Eisenberg
Associate Professor
Epidemiology, Complex Systems, and Mathematics
University of Michigan

Bio: Marisa Eisenberg received her PhD and M.S. in Biomedical Engineering from the University of California, Los Angeles in 2009. She then spent three years as a postdoctoral fellow studying mathematical biology at the Mathematical Biosciences Institute at Ohio State University, before joining the faculty at University of Michigan as an assistant professor in the Department of Epidemiology.

Dr. Eisenberg’s lab’s research program is in mathematical epidemiology, and is focused on using and developing parameter estimation and identifiability techniques to model disease dynamics. Much of their work is in building multi-scale models of infectious diseases, including examining cholera, environmentally driven diseases, and HPV dynamics. Their research blends mathematics, statistics, and epidemiology to understand transmission dynamics, inform optimal intervention strategies, and improve forecasting.

1:35 P.M. — Jesse Capecelatro (Mechanical Engineering, U-M)
Addressing challenges of COVID-19 through the lens of fluid dynamics

Jesse CapecelatroJesse Capecelatro
Assistant Professor
Mechanical Engineering
University of Michigan

Talk Title: Addressing challenges of COVID-19 through the lens of fluid dynamics

Abstract: It is now widely recognized that COVID-19 is predominantly spread through airborne transmission and most often in indoor settings. Assessing the risk of transmission remains challenging. The number of infectious particles that could be inhaled is typically estimated using simplified models under the assumption of well-mixed environments. However, enclosed spaces are rarely well mixed. In this talk, I will present past and ongoing efforts to characterize transmission of COVID-19 for several campus activities. Using high-resolution simulations of a realistic cough and simulations of disease transmission on campus buses and classrooms, I will show how fluid dynamics controls the spread of COVID-19 and how such data can be leveraged to provide better tools for analyzing risk.

Bio: Jesse Capecelatro is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan. His research group develops numerical methods and data-driven approaches for the prediction and optimization of “messy turbulent flows” relevant to energy and the environment (often multiphase and reacting). Prior to joining Michigan in 2016, Dr. Capecelatro was a research scientist at the Center for Exascale Simulation of Plasma-coupled Combustion (XPACC) at the University of Illinois. He received a B.S. from SUNY Binghamton in 2009, a M.S. from the University of Colorado Boulder in 2011, and a Ph.D. from Cornell in 2014. He is a recipient of the National Science Foundation CAREER Award, Office of Naval Research Young Investigator Award, and the ASME Pi Tau Sigma Gold Medal Award.

1:55 P.M. — Gustavo de los Campos (Epidemiology and Biostatistics, Michigan State University) & Jade Mitchell (Biosystems & Agricultural Engineering, Michigan State University)

Photo of Gustavo de los CamposGustavo de los Campos
Professor of Epidemiology
Associate Chair and Director of Biostatistics
Michigan State University

Bio: Gustvavo de los Campos is an Associate Professor in the Department of Epidemiology and Biostatistics.  His background and research interests center on quantitative and statistical genetics with a focus on the analysis and prediction of complex traits and diseases.

His research involves methods and software development for the analysis of big omic data and applications across different traits and diseases in humans as well as in plants and animals.

Photo of Jade MitchellJade Mitchell
Associate Professor
Biosystems and Agricultural Engineering
Michigan State University

Bio: Dr. Jade Mitchell is Associate Professor in Biosystems and Agricultural Engineering. Her research focuses on human health risk analysis. She is collaborating principal investigator in the Center for Advancing Microbial Risk Assessment (CAMRA), where her work includes characterizing the risks associated with human exposures to pathogenic agents in environmental media (i.e. air, water and soil). Using quantitative microbial risk assessment her work informs policy and risk management decisions about the appropriate level of concern for public health and public safety related to bio-defense, food safety and water quality. Mitchell uses a number of computational tools including statistical analyses, contaminant fate and transport modeling, human exposure modeling, cost-benefit analyses and decision analytic modeling to evaluate both microbial and chemical stressors in her current work.

2:15 P.M. — BREAK

Session 3 will resume at 2:25 P.M.

2:25 P.M. — Jenna Wiens (Computer Science and Engineering, U-M)
Identifying Patients at Greatest Risk of Complications from COVID-19

Photo of Jenna WiensJenna Wiens
Associate Professor
Computer Science and Engineering
University of Michigan

Talk Title: Identifying Patients at Greatest Risk of Complications from COVID-19

Abstract: In response to the strain on the hospital system induced by a growing number of COVID-19 cases, our interdisciplinary team composed of computer scientists and clinicians developed M-CURES – the Michigan COVID-19 Utilization and Risk Evaluation System. This machine learning based approach to clinical decision support aims to identify who among patients diagnosed with COVID-19 is at highest (and lowest) risk of requiring intensive care during hospitalization. Applied to a held-out cohort of COVID-19 patients at Michigan Medicine, the proposed model outperformed a widely implemented proprietary deterioration index. We are now exploring the integration of this model into the EHR and clinical workflow. Going forward, such a model could help clinicians during a future surge in hospitalizations or as an early warning system applied to the general inpatient population.

Bio: Professor Wiens currently heads the MLD3 research group, and is the Co-Director of Precision Health at the University of Michigan. Her primary research interests lie at the intersection of machine learning, data mining, and healthcare. Within machine learning, she is particularly interested in time-series analysis, transfer/multitask learning, causal inference, and learning intelligible models. The overarching goal of her research is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge. Her work has applications in modeling disease progression and predicting adverse patient outcomes. For several years now, Prof. Wiens has been focused on developing accurate patient risk stratification approaches that leverage spatiotemporal data, with the ultimate goal of reducing the rate of healthcare-associated infections among patients admitted to hospitals in the US. In addition to her research in the healthcare domain, she also spends a portion of my time developing new data mining techniques for analyzing player tracking data from the NBA.

Professor Wiens received her PhD in 2014 from MIT.  In 2015, she was named Forbes 30 under 30 in Science and Healthcare, shereceived an NSF CAREER Award in 2016, and in 2017 she was named to the MIT Tech Review’s list of 35 Innovators Under 35. Most recently, Prof. Weins received a Sloan Fellowship in Computer Science.

2:45 P.M. — Luis Zaman (Ecology and Evolutionary Biology, & Complex Systems, University of Michigan)

Photo of Luis ZamanLuis Zaman
Assistant Professor
Complex Systems, Ecology and Evolutionary Biology
University of Michigan

Bio: Luis Zaman received a dual-degree Ph.D. in Computer Science and Ecology, Evolutionary Biology & Behavior from Michigan State University in 2014. From 2014 to 2017, Luis was an NSF Postdoctoral Fellow in the Department of Biology at the University of Washington. His research blends computational and microbial evolution experiments in carefully controlled/engineered environments to better understand how complex communities shape what traits are adaptive.

Luis describes his work this way: Although my academic life started in computer science, my research interests are centered around evolutionary biology. I’m interested in using whatever technology and technique I can to understand fundamental processes in evolution. I once heard this kind of approach described as “undisciplined science,” and I’m quite fond of that phrasing. Prior to his current appointment, Luis has been working as an NSF Postdoctoral Fellow at the University of Washington in the Department of Biology.

3:05 P.M. — Seth Guikema (Industrial and Operations Engineering, & Civil and Environmental Engineering, University of Michigan)

Photo of Seth GuikemaSeth Guikema
Professor
Civil & Environmental Engineering, Industrial & Operations Engineering
University of Michigan

Bio: Seth Guikema is a Professor in the departments of Industrial & Operations Engineering and Civil and Environmental Engineering at the University of Michigan. He is also a Professor II (adjunct) in the Department of Safety, Economics, and Planning at the University of Stavanger as well as a Data Science Research Fellow at One Concern, Inc. The research in Professor Guikema’s group is described in this web site, particularly under the Research tab.

Professor Guikema completed a B.S. in Civil & Environmental Engineering at Cornell University, a M.S. in Civil & Environmental Engineering at Stanford, a M.Eng. by thesis in Civil Engineering at the University of Canterbury (New Zealand), and a Ph.D. in Engineering Risk and Decision Analysis in the Department of Management Science and Engineering at Stanford University. He was a postdoctoral research at Cornell University (Civil & Environmental Engineering). Prior to the University of Michigan he was an Assistant Professor in the Civil Engineering department at Texas A&M University and an Assistant and tenured Associate Professor in the Department of Geography and Environmental Engineering at Johns Hopkins University. He moved to the University of Michigan in 2015.

In 2020 Professor Guikema was the President of the Society for Risk Analysis (SRA). He previously served as the chairperson of both the Foundations of Risk Analysis and Engineering and Infrastructure specialty groups and served a three-year term on the SRA Council. In 2021 he was named a Fellow of the Society for Risk Analysis. He currently serves as the Area Editor for the Natural Hazards area for Risk Analysis and as the Area Editor for the Mathematical Modeling area for Risk Analysis. He is active in INFORMS and served on the INFORMS Decision Analysis Society Council for three years. He also is active in the American Statistical Association and the American Society of Civil Engineers.

3:25 P.M. — BREAK

The panel discussion will begin at 3:30 P.M.

3:30 P.M. | Panel Discussion

Moderated by Emily Martin (Epidemiology, University of Michigan)

We will collect questions throughout the day. Please submit questions using THIS FORM. We will address as many as time allows.

Panelists


Jesse Capecelatro
Mechanical Engineering
University of Michigan


Amy Cohn
Industrial and Operations Engineering
University of Michigan


Gustavo de los Campos
Epidemiology and Biostatistics
Michigan State University


Marisa Eisenberg
Epidemiology, Mathematics, Complex Systems
University of Michigan


Seth Guikema
Industrial and Operations Engineering,
Civil and Environmental Engineering
University of Michigan


Jade Mitchell
Biosystems and Agricultural Engineering
Michigan State University


Jenna Wiens
Computer Science and Engineering
University of Michigan


Luis Zaman
Complex Systems, Ecology and Evolutionary Biology
University of Michigan


Emily Martin (Moderator)
Epidemiology
University of Michigan