Daniel Weinberger, PhD

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.