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Revolutionizing Health Data

We will develop and apply novel data science approaches that integrate clinical, social, structural epidemiological, economic, social media and sensor data to improve health for all.
Photo by Brad Clift
A. David Paltiel is co-director of the Yale School of Public Health's Public Health Modeling Concentration and a professor in the Department of Health Policy and Management. Trained in the field of operations research, Dr. Paltiel designs and implements policy models and cost-effectiveness analyses.

The modern application of data sciences can advance public health and patient care and save millions of lives. YSPH and Yale are uniquely positioned to be international leaders during this transformative moment in health data collection, modeling, analysis and management. Public health modeling, biostatistics, biomedical data science, implementation science and health informatics are areas of excellence at YSPH that have received national and international attention. Faculty research — identifying patterns, curating phenotypes and revealing underlying biological processes for disease onset, progression and treatment — is providing essential insights that are helping to prevent disease outbreaks, advance cures, and inform policy around the world.

The Biostatistics Department’s Health Informatics Division, the Center for Methods in Implementation and Prevention Science and the Public Health Modeling Unit (PHMU) are examples of essential resources staffed by talented YSPH faculty and students who are revolutionizing research and the ways in which health data is applied. For example, the Health Informatics Division works to assess and address risks to public health, using cutting-edge forecasting and mitigation through the science of biostatistics, health informatics and data sciences using “big data” in many spheres: health, hospital, personal, social media, weather, satellite, cell phone, surveillance, geographic information systems and more.

YSPH researchers are engaged in precision medicine and, more broadly, precision public health. Faculty and students are developing powerful computational, statistical and visualization tools that use clinical, demographic, sensor, exposure and multi-omics data collected from millions of individuals through various biobanks, including the Generations Project at Yale, for personalized risk assessment, screening, treatment and monitoring. There is also a special emphasis on improving health equity through analytical advances. The crossdepartmental PHMU brings together researchers in epidemiology, genomics, biostatistics, operations research, decision science, causal inference and mathematical modeling to understand the dynamics of epidemics like HIV, tuberculosis, SARS-CoV2 and enteric infections such as rotavirus and typhoid. The Unit evaluates the epidemiological and economic trade-offs and population-level effectiveness of disease mitigation strategies for both infectious and chronic diseases. The PHMU has a truly global footprint working with data from around the world and in collaboration with partners on every continent. PHMU faculty are also leaders in methods development, pioneering new statistical, epidemiological and modeling approaches for addressing pressing public health problems. Those methods range from Bayesian spatio-temporal modeling of chronic and infectious disease to new ways of addressing questions in causal inference with observational data, to adaptive algorithms to improve disease control efforts.

Students who engage these data opportunities and master the methods of analysis will be the next generation of leaders in a world increasingly driven by complex data that must be integrated and understood to further human health. Students from our programs are already pursuing research careers in academia and holding leadership positions in clinical medicine and public health institutions around the world. We are especially motivated to train students from groups that are underrepresented in the quantitative sciences in public health.