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Center for Nonlinear Studies |
Assessing the transmissibility of a pathogen primarily involves quantifying the amount, speed, and patterns of transmission. During the COVID-19 pandemic, these dynamics varied by time and location, driven in part by the emergence of variants of concern (VOCs). Successive waves of infection prompted the continuous reassessment of transmission dynamics and public health policies. However, large-scale epidemic models faced challenges in rapidly evaluating changes in transmissibility and capturing transmission heterogeneities in high-risk settings, such as in hospitals and care homes.
The unprecedented availability of epidemic surveillance data enabled statistical inference of transmission chains (who infected whom), providing opportunities to reveal detailed insights into local transmission, but also to characterise broader epidemiological properties of SARS-CoV-2. Nevertheless, limited SARS-CoV-2 genetic diversity hindered the resolution of transmission trees. My research investigates the extent to which outbreak reconstruction tools can enhance our understanding of SARS-CoV-2 transmission dynamics and how they can be integrated into routine surveillance to improve public health response.
In the first half of the seminar, I will show how we can leverage household surveillance data at the national level to reconstruct transmission chains revealing that 20% of transmission events had negative serial intervals across VOCs. In the second half, I will present a novel method to quantify group-level transmission assortativity and how it can be used to assess the contribution of healthcare workers and patients to nosocomial transmission.
Integrating outbreak reconstruction tools into surveillance systems, even in the absence of genetic data, holds significant potential to support realM-bM-^@M-^Ptime modelling and public health response.Leveraging diverse data streams, at various scales, to address different needs, this research demonstrates how Bayesian inference of transmission chains can provide valuable insights into SARS-CoV-2 dynamics.
Bio
Cyril Geismar is a PhD student in the Department of Infectious Disease Epidemiology at Imperial College London, supervised by Dr. Anne Cori, Dr. Thibaut Jombart, and Prof. Peter White. His doctoral research focuses on developing Bayesian methods to infer and analyse transmission chains ("who infected whom") using epidemiological, contact, and genetic data (outbreaker2 R package) to characterise SARS-CoV-2 transmission. His previous work as part of the University College London COVID-19 study, Virus Watch, includes analysing antibody depletion post-vaccination and infection, assessing the risk of infection by occupation, and comparing the symptomatic profiles of SARS-CoV-2 with other respiratory viruses.
Teams: Join the meeting now
Meeting ID: 267 492 837 107
Passcode: 7th9ys3Y
Host: Lauren Castro (A-1)