Chair and organiser
Professor Christophe Fraser, University of Oxford, UK
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Professor Christophe Fraser, University of Oxford, UK
Professor Christophe Fraser, University of Oxford, UK
Christophe Fraser is Professor of Pathogen Dynamics in the Nuffield Department of Medicine at the University of Oxford. He heads the Pathogen Dynamics group in the University’s new Big Data Institute; the group analyses epidemics to understand how pathogens spread, evolve, and how best to control them. The Pathogen Dynamics research group is a multidisciplinary team of investigators, including laboratory science, clinical science, bioinformatics, computing, modelling and statistics. Christophe’s background and training is in mathematical physics, and as well as managing the group, his personal expertise is in mathematical modelling of dynamical and evolutionary processes.
Christophe is head of the PANGEA-HIV consortium, funded by the Bill and Melinda Gates Foundation, which uses pathogen genomics to assess and develop HIV prevention interventions. He led modelling and phylogenetics in HPTN 071 (PopART), and also leads the European BEEHIVE consortium.
https://www.bdi.ox.ac.uk/Team/christophe-fraser
http://pangea-hiv.org
https://www.beehive.ox.ac.uk
Epidemiology and population genetics of influenza in a tropical setting
Dr Maciej Boni, Oxford University Clinical Research Unit in Viet Nam, Vietnam
Abstract
The epidemic dynamics and evolution of influenza A virus occur on a global scale. Influenza epidemics in temperate zones are seasonal and more predictable than transmission dynamics in tropical and sub-tropical areas, but the dynamics of these two climatic zones are closely linked, with East and Southeast (E/SE) Asia likely playing a major role in driving global influenza circulation. Influenza evolution affects the dynamics of influenza epidemics globally, but we do not know which human populations or which epidemiological conditions drive antigenic evolution in influenza. To answer this question would would need genetic and epidemiological data from regions of the world that are suspected to play a large role in global influenza dynamics. I will describe several such studies initiated in Vietnam that are aimed at understanding the circulation of human influenza viruses in Southeast Asia, as well as some of the new analytical methods we are developing to analyze the incoming data. Understanding Vietnam’s role in global influenza circulation will help us determine how important of a role E/SE Asia play in global flu dynamics, and it will help us identify which components of the Vietnam data would allow for similar analyses to be done in other Asian countries.
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Dr Maciej Boni, Oxford University Clinical Research Unit in Viet Nam, Vietnam
Dr Maciej Boni, Oxford University Clinical Research Unit in Viet Nam, Vietnam
"Maciej Boni received his PhD in Ecology and Evolution in 2006, his doctoral work focusing on the evolutionary epidemiology of influenza as well as general patterns of drug-resistance evolution. In 2007, Maciej began a joint postdoc across economics and epidemiology departments to develop mathematical models of antimalarial treatment strategies in the context of drug resistance evolution, increased drug access and costs, and falling malaria prevalence. In 2008, Maciej moved to the Wellcome Trust Oxford unit in Ho Chi Minh City, Vietnam, where he has initiated influenza field studies to accompany his mathematical modeling research. He is currently looking at basic epidemiological questions surrounding tropical influenza, continuing his modeling work on antimalarial treatment strategies, and advising on aspects of dengue vaccination modeling that will be relevant for Southeast Asia."
Phylogenomics and phylodynamics of Streptococcus pneumoniae
Professor William Hanage, Harvard School of Public Health, USA
Abstract
The pneumococcus (Streptococcus pneumoniae) is a pathogen of global significance, for which effective vaccines are available against some serotypes. Molecular epidemiologic data, both genetic and genomic, have demonstrated that pneumococci experience a relatively high rate of recombination, which shuffles loci including resistance determinants among lineages and has generated vaccine escape genotypes. Effectively identifying those regions that have undergone recombination is a crucial step in the analysis of genomic data, such that genealogies may be constructed that are not distorted by horizontal gene transfer. It is essential that methods for identifying recombination work with emerging large genomic datasets, and ad hoc methods may be preferred in this context. The results also indicate that, as previously hypothesized, some pneumococcal lineages have experienced more recombination than others.
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Professor William Hanage, Harvard School of Public Health, USA
Professor William Hanage, Harvard School of Public Health, USA
"Bill Hanage studies the evolution and epidemiology of (mainly) bacterial pathogens. His PhD at Imperial College London was followed by postdoctoral work at Imperial College London and the University of Oxford. In 2010 he joined the faculty at Harvard School of Public Health. He is especially interested in subjects that combine clinical importance with fundamental biological questions, such as how pathogens respond to novel selective pressures in the form of antimicrobials and vaccines, or the link between transmission and virulence. He has also worked extensively on the phenomenon of homologous recombination in bacteria, studying how it can be detected and its consequences for how things respond to selection, and indeed the very notion of species. Increasingly he is involved with population genomic analyses of large numbers of very closely related pathogen isolates, to probe in detail their patterns of transmission and diversification. "
HIV as a model phylodynamic system
Dr Simon Frost, University of Cambridge, UK
Abstract
Human immunodeficiency virus type 1 (HIV-1) is perhaps the most widely studied organism in viral phylodynamic studies, which aim to combine the epidemiology of viral transmission with the evolution of the virus, and for good reason. Not only is HIV-1 infection a significant public health issue, but a vast amount of sequence data has been generated over a period of decades, which when combined with the clock-like nature of HIV-1 evolution, allows fairly accurate reconstruction of past transmission events. Insights that have been gained by HIV-1 sequence analysis include identifying the timing of origin of HIV-1 epidemics, identifying period of exponential epidemic growth, and identification of 'transmission clusters' of infection. To date, rather simple models underlie the analysis of viral sequence data, which consider neither the detailed natural history of viral infection, nor the biased way in which sequence data is typically collected. Using HIV as an example of a model phylodynamic system, I will consider the importance of heterogeneity in geographic location, risk group, duration of infection, and age at infection in determining the structure of viral phylogenetic trees. I will also highlight how non-uniform sampling is an important confounding factor. [Joint work with Erik Volz].
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Dr Simon Frost, University of Cambridge, UK
Dr Simon Frost, University of Cambridge, UK
"I am a mathematical biologist interested in all aspects of infectious diseases, ranging from within-host dynamics of infection, to the between host dynamics of disease transmission. I studied Natural Sciences (Zoology) in Cambridge, moving on to Oxford to study the within-host dynamics of HIV for my D.Phil. After completing postdoctoral posts in Princeton, Oxford, and Edinburgh, I moved to the University of California, San Diego. In 2008, I was awarded a Royal Society Wolfson Research Merit Award, and took up a Senior Lectureship in the Department of Veterinary Medicine, University of Cambridge. My current research focuses on on infectious disease dynamics in a range of host-parasite systems, and I am actively involved in developing computational and statistical methodology. "
The use of viral sequence data to evaluate the degree of disease superspreading, with an application to influenza H1N1pdm.
Professor Katia Koelle, Duke University, USA
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Professor Katia Koelle, Duke University, USA
Professor Katia Koelle, Duke University, USA
"I am a mathematical disease ecologist interested in understanding the population dynamics and evolutionary dynamics of infectious diseases. I earned my PhD in 2005 from the University of Michigan- Ann Arbor (PhD advisor Mercedes Pascual), where I studied the effects of climate forcing and strain interactions on cholera dynamics. As a post-doctoral researcher with Bryan Grenfell at Penn State’s Center for Infectious Disease Dynamics from 2006 to 2007, I continued to spend time focusing on strain interactions, with a heavier focus on rapidly evolving viral pathogens. Since starting a faculty position at Duke University in 2007, I have been working on the design of mathematical models to better understand the phylodynamics of RNA viral diseases, including influenza, dengue, and norovirus. I have also been working on the development of statistical approaches to fit epidemiological models to time series data and viral sequence data, and the application of these approaches to address questions of interest to disease ecologists."