Aanensen Group

Genomic Epidemiology of Infectious Diseases

Our mission is to inform control strategies and interventions on a local, national and international scale.

Through our global research partnerships with the US CDC, the European CDC, the World Health Organisation and Food and Agriculture Organisation, National Institutes of Health and Public Health England we are helping lead the fight against antimicrobial resistance (AMR) – one of the biggest threats to global health security.

We achieve our mission through a combination of structured population surveys and whole genome sequencing to generate high quality openly available surveillance data and identify high risk clones; by developing software tools and technologies that make data visualisation and interpretation accessible to all; and by investing in capacity building across low- and middle-income countries (LMICs), helping to train future leaders of new national and emerging surveillance programmes.

Our international and diverse team brings together expertise in data modelling, software development, epidemiology, bioinformatics and machine learning, genomic technology, Good Financial Grant Practice (GFGP), training and capacity building.

Our team consists of staff employed on Campus and at Big Data Institute, University of Oxford. Within CGPS we produce intuitive sequence and metadata interpretation tools for genomic epidemiology along with sequncing key pathogenic species for provision of data to the global community.

Translating research into usable bioinformatic web applications forms one of the core deliverables of CGPS and our mix of scientists and software engineers reflects this.

Key collaborators on campus include: Prof Julian Parkhill, Prof Sharon Peacock, Prof Nick Thomson, Prof Stephen Bentley, Prof Gordon Dougan and Prof Stephen Baker

To enable the provision of contextual datasets for pathogen surveillance we work closely with reference laboratories across Europe through the EARS-NET initiative and collaborations with Prof. Hajo Grundmann (UMCG). Utilising structured surveys and providing WGS data through open web applications our aims are to firstly identify high risk pathogenic clones, secondly assess their risk (eg AMR and virulance) and thirdly to provide data in a format for action.