Professor David Aanensen
Director of The Centre for Genomic Pathogen Surveillance
- Professor and Senior Group Leader in Genomic Surveillance
- Group Leader at Wellcome Sanger Institute
For endemic pathogens (and outbreak scenarios). epidemiological data combined with genomics can inform control strategies and interventions on a local, national and international scale. Data generation, integration, analytical flow and interpretation in real-time is challenging, but crucial for decision making and action.
Within The Centre for Genomic Pathogen Surveillance David and team focus on data flow and the use of genome sequencing for surveillance of microbial pathogens through a combination of web application and software engineering, methods development and large-scale structured pathogen surveys and sequencing of microbes with delivery of information for decision making.
Working with major public health agencies such as the US CDC, the European CDC, Public Health England and the WHO, systems are utilised to interpret and aid decision making for infection control.
David is also Director of the NIHR funded Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance working with partners leading National AMR strategies in The Phillipines, Colombia, Nigeria and India to implement genomic surveillance and linking to routine phenotypic and epidemiological data for priority pathogens.
Major Applications include:
Epicollect5 - Mobile data gathering platform used globally and by major health agencies, citizen scientists, ecologists, epidemiologists, business analytics, schools and colleges (..largely initiatives outside of the initial use cases..) over 14,000 projects and > 28Million data points.
Microreact - Open data visualisation and sharing for genomic epidemiology. Used by major agencies such as CDC, eCDC and PHE for routine investigation of public health incidents.
Pathogenwatch - A global platform for genomic surveillance of microbial pathogens (including all major WHO Priority bacterial pathogens) Rapid prediction of resistant genotypes, and clustering giving epidemiological context.
Deep clustering of bacterial tree images.
Hayati M. et al, (2022), Philos Trans R Soc Lond B Biol Sci, 377
Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic
Attwood SW. et al, (2022), Nature Reviews Genetics, 23, 547 - 562
Europe-wide expansion and eradication of multidrug-resistant Neisseria gonorrhoeae lineages: a genomic surveillance study.
Sánchez-Busó L. et al, (2022), Lancet Microbe, 3, e452 - e463
Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package.
Griffiths EJ. et al, (2022), GigaScience, 11
Conservation of vaccine antigen sequences encoded by sequenced strains of Streptococcus equi subsp. equi.
Frosth S. et al, (2022), Equine veterinary journal