Staff Bio
Jason Asher, PhD is the acting director of the Center for Forecasting and Outbreak Analytics (CFA) at CDC.
Role at CDC
As acting director of CFA, Dr. Asher is responsible for strengthening the nation's ability to use data, models, and analytics to enable timely, effective decision-making in response to public health threats. He has a distinguished career in mathematical modeling, conceptualizing and constructing novel methods for forecasting and evaluating mitigations during federal emergency responses to a variety of disease outbreaks.
Previous experience
Prior to joining CDC, Dr. Asher worked for Leidos, a technology leader in providing scientific, engineering, systems integration, information technology, and technical services that supported the Biomedical Advanced Research and Development Authority (BARDA) in the Administration for Strategic Preparedness and Response, US Department of Health and Human Services. In this role, he led a team responsible for executing a variety of quantitative tasks supporting emergency response outbreaks.
During the Ebola outbreak in West Africa from 2014 to 2015, Dr. Asher developed novel methods for forecasting disease incidence to help inform BARDA leadership about the potential impact of disease trends on the power of planned trials for vaccines and therapeutics.
Dr. Asher produced multiple modeling analyses that helped inform strategy for the Strategic National Stockpile (SNS) and the Pandemic Influenza Vaccine Stockpile.
He also developed models to inform stockpiling decisions for pre-pandemic influenza vaccines, including for H3N2v and H7N9 as well as strategic analyses for optimizing stockpile design.
During the Zika virus epidemic from 2015 to 2016, Dr. Asher worked with CDC to develop estimates of the potential treatment needs for Guillain-Barre Syndrome and the burden of microcephaly in Puerto Rico. He estimated needs for testing resources for screening of imported and locally acquired infection in response to National Security Council requests and developed methods for synthesizing multiple data sources to estimate the incidence of infection.