Enhancing Influenza Vaccines through Genetic Surveillance: Can Better Antigenic Match Improve Population Health?

My long-term career goal is to reduce the influenza burden by promoting data-driven and evidence-based influenza policies. My short-term goal is to address the role of vaccines’ antigenic match in mitigating influenza burden and to optimize vaccine strain selection process using genetic surveillance data. The proposed training and research program will position me to become an independent investigator and establish my research group at the intersection of influenza epidemiology, health policy, statistics, and decision science. Candidate: My background in health decision science has equipped me with training and research experience in comparative effectiveness and simulation modeling of infectious diseases. However, I lack methodological skills necessary to infer the effect of improving antigenic match on the risk influenza complications in real-world data and to assess the value of genetic surveillance data for better antigenic match. The training proposed in this K01 award will build on my background and provide me with the skills I currently lack to conduct the proposed research. Training plan: This plan includes formal coursework, workshops, seminars and personal mentoring. I will dedicate 75% effort to the training and research activities proposed, under the supervision of my primary mentor Dr. Anirban Basu. Dr. Janet Englund, Dr. Trevor Bedford, and Dr. Adam Szpiro will be my co-mentors. Dr. John Huddleston, Dr. Matthew Biggerstaff, and Dr. Richard Zimmerman will serve as advisors, and Dr.Vanja Dukic will be a collaborator. My specific training goals are: 1) To learn and implement spatio-temporal data analysis and visualization methods; 2) To develop an in-depth understanding of influenza virus evolution and incorporate it into an epidemiological model; 3) To learn value-of-information analysis in the context of using genetic surveillance for vaccine strain selection; 4) Engage with the influenza policy community and stakeholders; and 5) Improve my grant writing skills and submit an R01 grant application. Research plan: The overarching theme of this K01 research program is to improve the outcomes of seasonal influenza vaccines through enhanced genetic surveillance. My specific aims are to 1) Describe the spatio- temporal patterns of the vaccine antigenic match, vaccine uptake, and influenza health outcomes; 2) Estimate the real-world impact of enhancing the influenza vaccine’s antigenic match on influenza-related hospitalizations and deaths; and 3) Estimate the net benefit of collecting additional genetic sequencing data of influenza viruses in the surveillance system to improve vaccine strain selection. Impact: The findings from the proposed research will offer opportunities to better prepare for influenza seasons with low antigenic coverage and optimize resources allocation for influenza vaccine composition, enhancing the influenza surveillance landscape. Project Number: 1K01AI190049-01A1 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of Allergy and Infectious Diseases (NIAID) | Principal Investigator: Kyueun Lee | Institution: UNIVERSITY OF WASHINGTON, SEATTLE, WA | Award Amount: $182,422 | Activity Code: K01 | Study Section: Special Emphasis Panel[ZRG1 IIDA-J (81)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1K01AI19004901A1