What’s next in public health?

Our division chairs on the public health innovations on the horizon

At UC Berkeley School of Public Health, we are all in on innovating solutions to the most pressing public health threats of our time—climate change, pandemic threats, chronic diseases, and social inequity—while training the next generation of public health changemakers with a first-rate education at a top school of public health.

From research on redlining to what’s next in AI, from genomics to the health effects of a warming planet, our faculty, researchers, and students are on the cutting edge of what’s next.

We asked our division chairs: What public health innovations are you most excited about as our school looks at the next 5–10 years in public health? Here’s what they had to say.

James C. Robinson, PhD, MPH, Professor & Division Chair, Health Policy and Management Division and Leonard D. Schaeffer Endowed Chair, Health Economics and Policy

In Health Policy and Management (HPM) we are most excited by the accelerating revolution in digital and biotechnological innovation. Artificial intelligence, genomics, cell therapies, wearable sensors, implantable neurostimulators, CRISPR gene editing; it does on and on.

These offer tremendous potential value to patients, and will stimulate waves of investment in product development, manufacturing, and distribution. But they challenge established policy pathways and the safeguards for safety, effectiveness, privacy, and fairness. They also challenge the traditional economic ecosystem of research universities, entrepreneurial startups, and large firms with global reach. There is much to be done. HPM alumni will be among those who do it.

Justin V. Remais, PhD, MS Professor & Chair, Environmental Health Sciences Division

Climate change is the defining issue of the 21st century, and the field of Environmental Health Sciences is at the leading edge of tackling the public health and health equity effects of climate-driven disasters, wildfires, energy production, climate sensitive infectious diseases, and other major challenges.

Our faculty, students, and alumni are developing the emerging technologies (e.g., sensor networks; satellite remote sensing), analytical methods (e.g., spatial data science; econometrics; epigenetics; metabolomics), and theoretical advances (e.g., gene-environment interactions; risk tradeoffs; environmental justice) necessary to create a more sustainable environment and just society.

Mahasin S. Mujahid, PhD, MS, FAHA, Professor & Chair, Epidemiology Division

UC Berkeley is at the forefront of epidemiology, using cutting-edge methods to address today’s most critical public health challenges. With the availability of big data and technological advances such as machine learning and artificial intelligence, we can analyze large datasets and identify complex patterns in disease transmission, risk factors, and outcomes. These advancements allow us to adopt more precise and personalized approaches to disease patterns, risk factors, and treatment responses.

In partnerships with local public health departments, the Centers for Disease Control and Prevention, and the World Health Organization, we’re working to develop robust surveillance and response systems to improve preparedness for infectious disease outbreaks, pandemics, and natural disasters. These collaborations provide unique opportunities for students to engage in real-world public health practice and make meaningful contributions to global health security.

Additionally, we’re dedicated to addressing the social determinants of health and adopting a more inclusive and community-engaged approach to health to achieve health equity. Our program emphasizes the importance of understanding the complex interplay between social, environmental, and biological factors that influence health outcomes; and we provide students with the skills and knowledge to positively impact the health of populations worldwide.

Denise Herd, PhD, Professor, Behavioral Sciences, Chair, Community Health Sciences Division

The Division of Community Health Sciences trains future leaders to address critical problems of our times, such as reproductive rights and health; environmental injustice, food insecurity, houselessness, community safety, and global conflict and health – all from the perspective of increasing health equity and social justice. The three programs housed in the division provide students with opportunities for community-engaged research and practice in the fields of Maternal and Child Health, Public Health Nutrition, and Health and Social Behavior.

Students have the opportunity to work using a range of methods and approaches from community based participatory research to ethnography, mixed-methods research and quantitative data analysis, to interface with communities, policy agencies and researchers to create healthier communities. The Division includes faculty with backgrounds in psychology, anthropology, law, political science, bioethics, global health, epidemiology, nutrition, and economics; so that students receive a transdisciplinary education that addresses the impact of the social determinants of health throughout the lifecourse.

Alan Hubbard, PhD, Professor & Chair, Biostatistics Division

Biostatistics is in a time of significant disruption, as more traditional methods and data types are being supplanted by AI and new forms of complex information (genomics, electronic health records, micro-sensors, and wearable technologies). Recent developments in machine learning and their wide availability mean that tomorrow’s biostatisticians will also have to become experts in computation and statistics. At the same time, they will still play a crucial role in translating scientific and policy questions into meaningful statistical quantities, often by using causal inference and ensuring the uncertainty of estimates is accurately reported.

Given the mounting evidence of a reproducibility crisis in data-driven research, there is a growing emphasis on pre-specification, methods that avoid arbitrary model bias, and increased transparency, using open-source software, standardized protocols, and data-sharing practices to enhance research quality and collaboration.

Our goal is to train students to harness advances in machine learning, causal inference, research reproducibility, and statistical theory toward robust approaches that will improve our understanding of disease mechanisms, treatment outcomes, and causes of health inequities.

More in category “BPH Opinions”: