EPPIcenter physician scientist earns NIH/NIAID New Innovator Award for exceptionally creative early career investigators

The NIAID New Innovator Award is part of the NIH High-Risk, High-Reward Research Program, which was created to accelerate the pace of biomedical, behavioral and social science discoveries. It identifies and supports exceptionally creative early career investigators who propose innovative, high-impact projects within the NIH mission that may be considered risky to receive traditional grant funding. 

Dr. Nathan Lo’s team will use the $2.4 million dollar grant over 5 years to develop predictive modeling tools for California public health departments to allocate targeted vaccines to their highest-risk populations. They will then study the implementation and public health impact of using these predictive tools to guide real-time vaccination policy. 

There is limited evidence demonstrating how predictive models are used by public health departments, and whether their use translates into policies that reduce infectious diseases. This research will explore the pipeline between model prediction, response, and policy making. The work will measure how well vaccinations against pertussis, seasonal influenza, and hepatitis A are allocated among California’s highest-risk locations and populations. The tools developed in the study will remain available and open source, for use by health departments. 

“I feel very fortunate to have support from a NIAID New Innovator Award. This is a very exciting opportunity to bring predictive modeling from theory into practice for public health decision making in control of infectious diseases” - Dr. Lo.

The Lo research group has a strong track record of moving evidence through the pipeline to policy. He recently worked with the California Department of Public Health on COVID-19 vaccine prioritization and public health impact, as well as with the World Health Organization to develop guidelines on control and elimination of schistosomiasis. 

Dr Lo’s research group works within the EPPIcenter research unit at UCSF. Read more about the interface between predictive models and implementation science at the EPPIcenter’s site.