Why call it BA.2.12.1? A guide to the tangled Omicron family
The Oxford Martin Programme on
The control of emerging infectious diseases is a defining problem of our age. Growing global trade and mobility are connecting pathogens with new populations and as a result outbreaks of disease have become increasingly frequent.
Predicting which bacterium or virus will cause the next epidemic is not currently possible. Instead we must detect and analyse new human pathogens as soon as they emerge, and plan their control using all the information at our disposal.
Since the 1980s, the science of infectious disease dynamics has developed a sophisticated mathematical framework that is widely used to inform pandemic control and support policy decisions. This framework uses data on the number of cases through time and space to quantify past spread, predict future transmission, and calculate the intensity of interventions needed to end an epidemic.
However current approaches do not always fully exploit new sources of information about epidemic behaviour. Incorporating these promising data sources into epidemiological models has the potential to improve the accuracy and certainty of epidemic predictions
Two new types of information relating to epidemics have become increasingly available. Firstly, genome sequences from emerging pathogens can be generated almost as fast as cases are counted. Bacteria and viruses evolve as they spread, so their genomes contain a record of who-infected-whom – a ‘genetic footprint’ of past transmission events. Secondly, digital data sets that describe human population density and mobility in unprecedented detail are now accessible.
The Pandemic Genomics programme is bringing together mathematical epidemiology, pathogen phylodynamics, and human geography to build a new body of theory capable of co-analysing these distinct sources of information. The programme will develop and validate a prototype that will be tested on data from past and future outbreaks of emerging infectious disease.
Alongside this basic research, the programme is engaging with science advisors to develop a suite of policy tools that will explore how data on pathogen genomes and human mobility can better inform epidemic decision making.
The core scientific aim is to produce an analytical framework for emerging infections for the 21st century - one built on the assumption that pathogen genome sequences and human mobility data will be readily available during future outbreaks.
Given the informal and preliminary nature of these pre-prints, papers, and blog posts they may be subject to change.
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Associate Professor of Computational and Genomic Epidemiology
Professor of Mathematical Biology
Associate Professor in Quantitative Social Science Methods
Professor of Evolution & Infectious Disease
Postdoctoral Research Assistant
Former Visiting Fellow (2019-20)
Postdoctoral Research Fellow
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