Stephen Kissler, Postdoctoral Fellow at Harvard T.H. Chan School of Public Health
SARS-CoV-2 across scales
Mathematical models have provided key insights into SARS-CoV-2 dynamics at the global, local, and physiological scales. I will summarize some of our research efforts within each of these contexts. Early in the pandemic, a deterministic multi-strain coronavirus transmission model revealed how SARS-CoV-2 could behave during its transition from a pandemic to a seasonally epidemic virus and allowed us to compare a variety of intervention strategies. As the racial and ethnic disparities in COVID-19 morbidity and mortality became clear, Bayesian inferential methods helped us demonstrate that poor health outcomes in New York City were associated with high community prevalence in neighborhoods where individuals could not stay home from work. Most recently, models of within-host SARS-CoV-2 viral dynamics have allowed us to evaluate potential mechanisms for the increased infectiousness of the SARS-CoV-2 variant B.1.1.7. I will discuss the mathematical and epidemiological aspects of each of these efforts and discuss how they provide important context for one another.
Bio: Stephen Kissler is a postdoctoral fellow in Immunology and Infectious Diseases at the Harvard T.H. Chan School of Public Health. He studied applied mathematics at the University of Colorado Boulder (B.S., M.S.) and the University of Cambridge (Ph.D.) with applications in physiology and infectious disease epidemiology. He currently focuses on the transmission and evolutionary dynamics of pathogens, especially antibiotic-resistant bacteria and SARS-CoV-2.