Instructors

Virginia Pitzer ScD is Associate Professor in the Department of Epidemiology of Microbial Diseases at Yale School of Public Health. Her research focuses on mathematical modeling of the transmission dynamics of infectious diseases, including rotavirus, respiratory syncytial virus, and typhoid fever. She studies how interventions such as vaccination, improved treatment of cases, and improvements in sanitation affect disease transmission at the population level. During the COVID-19 pandemic, she has worked on examining how changes in testing practices affect estimates of coronavirus transmission; estimating incidence of SARS-CoV-2 infections from observed cases and deaths (www.covidestim.org); and determining the impact of vaccination on household transmission. She is a member of the World Health Organization’s Immunization and Vaccine-related Implementation Research Advisory Committee and Deputy Editor-in-Chief of PLoS Computational Biology.

A. David Paltiel MBA PhD is Professor, Department of Health Policy and Management, Yale School of Public Health and Yale School of Management. His research focuses on model-based cost-effectiveness of health and medical interventions. He has served on five expert panels convened by the Institute of Medicine under mandates from the United States Congress and on the editorial boards of both Medical Decision Making and Value in Health. During the COVID-19 pandemic, he has advised more than 20 US colleges on their reopening plans and has led numerous high-profile studies of vaccine implementation and home-based rapid testing for SARS-CoV-2.

Dr. Cohen is an infectious disease epidemiologist whose primary research focus is tuberculosis. He is particularly interested in understanding how TB drug-resistance and medical comorbidities such as HIV frustrate current efforts to control epidemics, with an ultimate goal of developing more effective approaches to limit the morbidity caused by this pathogen. Dr. Cohen's training is in epidemiology and clinical medicine, and his work includes mathematical modeling, fieldwork, and analysis of programmatic data. His research program is currently funded by NIH and US CDCAwards.

Dan Weinberger is an Associate Professor in Epidemiology of Microbial Diseases at Yale School of Public Health. His research uses a combination of quantitative analysis, laboratory experiments and field work to understand the epidemiology and biology of respiratory infections. Recent work has focused on developing novel analytical methods for the evaluation of vaccines using time series and spatial data. He collaborates widely with public health agencies and academic organizations around the world on these issues. He earned his PhD in biological sciences from Harvard School of Public Health, with a focus on Infectious Disease Epidemiology, and completed a postdoctoral fellowship in the Division of International Epidemiology and Population Studies in the Fogarty International Center at the NIH.Research: The research in the Weinberger Lab is at the intersection of microbiology and epidemiology. We focus on understanding the biological and epidemiological drivers of respiratory infections, including pneumococcus, RSV, influenza, and Legionella. Major research areas include understanding the biological drivers of the emergence of rare pneumococcal serotypes following vaccine introduction, developing novel statistical approaches to evaluate vaccine impact from observational data, evaluating the importance of interactions among respiratory pathogens, and understanding environmental drivers of Legionellosis. We employ a variety of tools including experimental and quantitative approaches. Our work is funded by grants from the NIH/NIAID, the Bill and Melinda Gates Foundation, and the Emerging Infections Program (a collaboration between the CDC, the Connecticut Department of Public Health, and Yale). You can learn more about our research here. Teaching: I teach the Public Health Surveillance course at YSPH. This class uses a mix of lectures, cases studies, and hands on data analysis exercises. Students learn to perform common surveillance analyses including aberration detection (e.g., CUSUM), time series analysis, and spatial cluster detection (SATSCAN). Students learn to do these analyses in either SAS or R.