The postdoc will lead the development of a Bayesian model of antibody kinetics following COVID-19 vaccination and infection.
The Department of Epidemiology at the Harvard T.H. Chan School of Public Health studies the frequency, distribution, and determinants of disease in humans, a fundamental science of public health. In addition to pursuing ground-breaking global research initiatives, we educate and prepare future medical leaders and practitioners as part of our mission to ignite positive changes in the quality of health across the world.
The Center for Communicable Disease Dynamics (CCDD), in the Department of Epidemiology, is a key center for research and policy analysis on SARS-CoV-2 and has been at the forefront of the COVID-19 response. CCDD’s team, comprised of faculty, researchers, postdocs, and graduate students, focuses on innovative modeling techniques, interdisciplinary methods, and data analysis to understand infectious disease dynamics.
To learn more about the CCDD and the affiliated labs, please see https://ccdd.hsph.harvard.edu/.
The postdoctoral fellow would support Marc Lipsitch’s lab primarily working on developing a Bayesian model of antibody kinetics following COVID-19 vaccination and infection (see below).
The postdoctoral research fellow, working with Dr. Lipsitch’s lab and CCDD, will lead the development of a Bayesian model of antibody kinetics following COVID-19 vaccination and infection. Model development will be based on data from a large cohort of health care workers at Sheba Medical Center, Israel (1, 2), in a collaboration with the PI of that study, Dr. Gili Regev-Yochay, as well as Dr. Noam Barda (Sheba), and Dr. Nima Hejazi, Department of Biostatistics (Harvard T.H. Chan School of Public Health). The purpose of the Bayesian model is to provide estimates for the time course of levels of various immune markers (e.g. binding and neutralizing antibodies) such that with one or a few timepoint measurements, these markers can be predicted (with associated uncertainty) at other unmeasured timepoints. Application of the model will include estimating correlates of protection for time-varying (rather than fixed-timepoint) immune marker measurements.
- Regev-Yochay G, Gonen T, Gilboa M, et al. Efficacy of a Fourth Dose of Covid-19 mRNA Vaccine against Omicron. N Engl J Med. 2022;386(14):1377-80. doi:10.1056/NEJMc2202542
- Bergwerk M, Gonen T, Lustig Y, et al. Covid-19 Breakthrough Infections in Vaccinated Health Care Workers. N Engl J Med. 2021. doi:10.1056/NEJMoa2109072
- Harvard T.H. Chan School of Public Health
- Closing date
- December 1st, 2023
- Posted on
- August 17th, 2023 13:00
- Last updated
- August 17th, 2023 13:00