Why call it BA.2.12.1? A guide to the tangled Omicron family
The Oxford Martin Programme on
Pandemic Genomics
The Challenge
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.
COVID-19 response
Given the informal and preliminary nature of these pre-prints, papers, and blog posts they may be subject to change.
Monitoring key epidemiological parameters of SARS-CoV-2 transmission
Crowding and the shape of COVID-19 epidemics
Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil
Researchers use virus genomic tracking to reveal the rise and fall of the COVID-19 epidemic in Guangdong Province
Genomic epidemiology of SARS-CoV-2 in Guangdong Province, China
China coronavirus cases: Measures could have prevented 700,000 COVID-19 cases
China lockdown may have blocked 700,000 virus cases: Researchers
China’s control measures may have prevented 700,000 COVID-19 cases
An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China
Asian countries risk new waves of coronavirus infections when they lift lockdowns. The same could happen in the rest of the world.
What the cruise-ship outbreaks reveal about COVID-19
The spread could have been worse: Lockdowns and travel restrictions stifled the coronavirus outbreak in China from Wuhan by 92 percent, new study reveals
The effect of human mobility and control measures on the COVID-19 epidemic in China
COVID-19: Study shows that travel restrictions are most useful in the early and late phase of an epidemic
Epidemiological data from the COVID-19 outbreak, real-time case information
I’m a researcher who’s helped change how we tackle pandemics like coronavirus forever – this is what we’ve learned
Online Map Tracks Coronavirus Outbreak in Real Time
First cases of coronavirus disease (COVID-19) in Brazil, South America
Strategies shift as coronavirus pandemic looms
Coronavirus and the race to distribute reliable diagnostics
COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread
Coronavirus: Egypt, Algeria and South Africa are the main gates for importation in Africa
Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study
Open access epidemiological data from the COVID-19 outbreak
Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020
Potential for global spread of a novel coronavirus from China
Phylodynamic analysis of nCoV-2019 genomes
Air travel could spread Wuhan pneumonia to further international locations
Pneumonia of Unknown Etiology in Wuhan, China: Potential for International Spread Via Commercial Air Travel
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Alpha variant spread via ‘super-seeding’ event within UK
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people
Moritz Kraemer
Associate Professor of Computational and Genomic Epidemiology
Angela McLean
Professor of Mathematical Biology
Janey Messina
Associate Professor in Quantitative Social Science Methods
Oliver Pybus
Professor of Evolution & Infectious Disease
Alexander Zarebski
Postdoctoral Research Assistant
Christopher Dye
Former Visiting Fellow (2019-20)
Joseph Tsui
Bernardo Gutierrez
Postdoctoral Research Fellow
publications
View all
A molnupiravir-associated mutational signature in global SARS-CoV-2 genomes
Convergent trends and spatiotemporal patterns of Aedes-borne arboviruses in Mexico and Central America
Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1
Dispersal Patterns and Influence of Air Travel During the Global Expansion of SARS-CoV-2 Variants of Concern
Global disparities in SARS-CoV-2 genomic surveillance
The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK
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