Bingyi Yang, University of Hong Kong

Title of talk: Dynamics of human life-course antibody profile to influenza A(H3N2)

Abstract: Pre-existing antibodies against antigenically variable pathogens that arise from multiple infections over a life time interfere with immune responses to new exposures through both positive and negative feedbacks. Such complex interaction – analogical to ecological systems - may exhibit intrinsic temporal cycles but has not been investigated. This study reported the existence of intrinsic cycles in human antibody profiles of influenza A(H3N2) at both individual- and population-level using antibody profiles from a cohort in Guangzhou and found these cycles can be recovered by models incorporating cross-reactions between strains and the herd protection could recover these two cycles.

 

A paper from this work can be found here: https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1008635

 

Bio: I obtained my Ph.D. in infectious disease epidemiology from The University of Hong Kong and postdoctoral training in University of Florida with Prof. Derek Cummings. My research interest is understanding the transmission dynamics of infectious diseases with a focus on seroepidemiology.

Alasdair Henderson, London School of Hygiene & Tropical Medicine

Title of talk: Mathematical modelling of arbovirus outbreak dynamics in Fiji

Abstract: Diseases spread by the Aedes genus of mosquitoes are some of the fastest growing and fastest spreading viral pathogens in the world, including in Fiji and the wider Pacific. I conducted a serological survey in Fiji in 2017 and used these serological data, combined with mathematical modelling, to analyse transmission dynamics of arboviruses in Fiji. I found a diverse range of outbreak dynamics and serological responses to outbreaks of closely related dengue and Zika viruses in the same location. I found that both viruses can cause low level multi-year outbreaks as well as large single season epidemics. Despite these challenges, mathematical modelling can improve our understanding of arbovirus outbreak dynamics such that it is possible to accurately forecast outbreak dynamics in real-time.

James Hay, Harvard University

Title of talk: Using viral loads to improve COVID-19 surveillance

Abstract: Virologic testing has been central to tracking the COVID -19 pandemic. Most routine tests provide a quantitative result in the form of a cycle threshold (Ct) value—a metric proportional to the log viral load. These data are usually reported as a binary result, thereby removing much of the information inherent in the full quantitative value. We propose that, despite their caveats and variability, the Ct value is a useful measure that can be harnessed to improve public health surveillance. I will present three projects that combine mathematical models and an understanding of viral kinetics to generate insights into surveillance testing, efficient sample pooling and reconstruction of epidemic dynamics.

Bio: I completed my PhD with Steven Riley in 2019 at the Department of Infectious Disease Epidemiology, Imperial College London, where I developed methods to infer antibody kinetics, infection histories and epidemic dynamics using serological data. I have since been a postdoc in CCCD at Harvard SPH working with Michael Mina on COVID -19 testing (though I was meant to be using epitope-level serological data to study population immunology).

 

Sam Hong, KU Leuven

Title of talk: Challenges in SARS-CoV-2 Bayesian phylodynamic inference using BEAST

Abstract: Thanks to an unprecedented effort by laboratories around the world, there are now over 400,000 SARS-CoV-2 genomes available to the scientific community. Although this large amount of annotated genetic data presents us with a unique opportunity to learn about the ongoing evolution and spread of SARS-CoV-2, the sheer magnitude of the dataset makes analyses computationally challenging. In this presentation, I will talk about challenges we have encountered when performing Bayesian phylodynamic inference in BEAST with the available SARS-CoV-2 data, and the work we have done to try to overcome them.

Bio: I am currently doing my PhD at KU Leuven, where I work under the supervision of Dr. Guy Baele and Dr. Philippe Lemey on reconstructing the evolution and spread of pathogens using genome sequences and other epidemiological data. Prior to coming to Belgium, I worked at the University of Minnesota’s department of Veterinary Population Medicine, where I did research on the molecular epidemiology of antimicrobial resistance in food borne pathogens. My research interests revolve around the use of computational tools to understand the evolution and epidemic dynamics of infectious diseases in animal and human populations.

Alexis Robert, London School of Hygiene and Tropical Medicine

Title of talk: Modelling the risks of measles outbreaks near elimination

Abstract: Although the global burden of measles has been substantially reduced after the introduction of the measles-mumps-rubella (MMR) vaccine, outbreaks continue to affect populations around the world. Heterogeneity in past incidence and vaccine coverage lead to under-immunised areas, whereby importation of cases can cause long-lasting outbreaks. In this talk, I will present the methods we developed to identify the regions most vulnerable to outbreaks, and evaluate the overall risks of measles transmission in a country.

Bio: I am a mathematical modeller in epidemiology, I joined LSHTM in 2016 and started a PhD on developing methods to reconstruct measles transmission in October 2017. My work focuses on developing models to understand the mechanisms responsible for disease spread in diverse settings. I am especially interested in maximising the information that can be extracted from routinely collected data to evaluate the risks of transmission.

Aine O'Toole, University of Edinburgh

Title of talk: Tracking the international spread of SARS-CoV-2 lineages of concern

Abstract: Late in 2020, two genetically-distinct clusters of SARS-CoV-2 with mutations of biological concern were reported. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). Further, we have built a custom tracking website and tool (grinch) which is updated daily and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.

Olivia Boyd, Imperial College London

Title of talk: Developing an outbreak scanning tool to identify sars-cov-2 variants of interest

Abstract: Identifying the emergence of lineages and variants of interest in the on-going sars-cov-2 pandemic is of great concern, particularly in the context of antigenic and vaccine escape. This talk will present three statistical methods incorporated into a outbreak scanning tool for identifying clusters, lineages, or variants of concern with high growth rates. Furthermore, this talk will showcase a preliminary public-facing scanning tool where results from these methods can be filtered by statistical cut-off, lineage, or mutation of interest to identify potential variants or mutations linked to increased growth rates in the on-going outbreak.

Bio: My research interests include developing mathematical and statistical models, by incorporating phylogenetic and epidemiological data, to understand disease transmission in the context of sustained and pandemic outbreaks. I’ve recently started my PhD under the supervision of Dr. Erik Volz and Dr. Marc Baguelin at Imperial College London. My PhD project focuses on developing novel phylodynamic methods for forecasting hot-spots and transmission dynamics in RNA-virus outbreaks, with a current primary focus on the ongoing sars-cov-2 pandemic. Prior to starting my PhD I was working with Dr. Jeff Eaton on the estimation and modelling of HIV incidence in real-time in Malawi, primarily focussed on assessing the impact of increasing secondary school access to adolescent girls and young women on HIV and pregnancy incidence.

Saki Takahashi, University of California, San Francisco

Title of talk: SARS-CoV-2 serosurveillance: local efforts and global considerations

Abstract: Serosurveys are a key resource for measuring the proportion of the population that has been infected by SARS-CoV-2. However, reported assay performance characteristics are usually only applicable to early convalescent samples from hospitalized patients with severe disease, whereas accurate estimation of population seroprevalence relies on adequate characterization of assay sensitivity to detect prior infections in the general population. In this talk, I’ll present our recent work on characterizing the heterogeneity in antibody responses between assays, disease severity, and over time among participants in a post-infection cohort, as well as on developing a framework to incorporate these heterogeneities into more accurate seroprevalence estimation. I’ll also discuss a platform that our group has piloted to test remnant samples from two hospital networks in San Francisco for SARS-CoV-2 antibodies, which could be leveraged for broader application beyond this local context and for other pathogens.

Bio: I am a postdoctoral scholar and a Schmidt Science Fellow at the EPPIcenter at UCSF. I’m broadly interested in understanding the interactions between pathogens and the host immune response across scales. In my post-doc, I’ve been combining high-throughput serological data with models to study how individuals acquire clinical immunity to malaria, and using serosurveillance to understand the magnitude of the SARS-CoV-2 pandemic. I completed my PhD in 2019 at Princeton University with Jessica Metcalf and Bryan Grenfell.

Elaine Shuo Feng, University of Oxford

Title of talk: Correlates of protection of ChAdOx1 nCoV-19 vaccine against SARS-CoV-2 infections

Abstract: Understanding the relationship between immune response to SARS-CoV-2 vaccines and protection against clinical outcomes is urgently needed to facilitate further vaccine development. However, evidence on correlates of protection from SARS-CoV-2 vaccine trials is limited. Using phase II/III ChAdOx1 nCoV-19 vaccine trial data in the UK, we estimated the correlates of protection by immune markers against symptomatic COVID-19 disease using a case-cohort study design. Our preliminary analysis found higher immune marker levels are associated with lower risk of symptomatic disease and in particular lower risk of lower respiratory tract infections.

Dr Sam Wilks, Department of Zoology, University of Cambridge

Title of talk: Accounting for prior immunity and evolutionary uncertainty when vaccinating against influenza and other antigenically variable pathogens

Abstract: Twice yearly the WHO influenza vaccine strain selection committee meets to select which strains to include in the seasonal influenza vaccine for the upcoming season, trying to balance the need to update the vaccine to protect against upcoming antigenic variants and the need to maintain protection against current variants that may continue to circulate.  Understanding antigenic variation and epidemiological trends features heavily in this process but an often under-emphasised component is how pre-existing immunity in the recipient population varies and influences these decisions.  This talk will centre around our work to model and understand the role of population pre-vaccination immunity in determining optimal vaccine strain choices under such circumstances, including efforts to anticipate antigenic changes and get ahead of the evolutionary curve in a situation where time from vaccine strain selection to administration may be as long as 6-9 months.