global surveillance one world – one health
TRANSCRIPT
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Oksana Lukjancenko, PhD Research group of Genomics EpidemiologyNational Food Institute, Technical University of Denmarkwww.compare-europe.euwww.genomicepidemiology.org
Global surveillanceOne World – One Health
9th GMI meeting 25th May 2016
Rome, Italy
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DTU Food, Technical University of Denmark
• Dynamics of common infectious diseases are changing– Demographic change, population density, AMR, etc.
• New diseases emerge frequently – Deforestation, population growth, health system inequalities,
travel, trade, climate change
• Effects are difficult to predict due to complexity of problems– Rapid flexible response
• Public health and clinical response depend on global capacity for disease surveillance
– Rapid sharing, comparison and analysis of data from multiple sources and using multiple methodologies
Infectious disease situation 2015
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Clinical research response to ID outbreaks usually fragmented and too late
3
Infe
cted
pati
ents
Public Health response
Preclinical research response
time
clinical research response
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Clinical research response to ID outbreaks with improved detection and sharing of data
4
Infe
cted
pati
ents
Public Health response
Preclinical research response
time
clinical research response
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DTU Food, Technical University of Denmark
• Real-time sharing data on occurrences of all infectious agents including AMR data
• Tools for automatically detections of related clusters in time and space
• Possibilities to observe trends in clones and species as well as resistance, virulence, and other epidemiological markers
• Ability to rapidly compare between all types of data
What is needed!
There can be no real-time surveillance without real-time data sharing
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DTU Food, Technical University of Denmark
The Surveillance Pyramid
Population
exposures
Person becomes ill
Person seeks care
Specimen obtained
Lab tests for organism
Culture-confirmed case
Reported to health unit
• What if people will not share?
–How do we then get a global surveillance?
• How do we get down the surveillance
pyramid?
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DTU Food, Technical University of Denmark
Monitoring large health populations
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DTU Food, Technical University of Denmark
Metagenomics analysis – Quantification of all bacterial and virus including
AMR genes for surveillance
Nordahl Petersen T et al. 2015. Sci Rep.
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DTU Food, Technical University of Denmark
Metagenomics analysis – Quantification of all bacterial and virus including
AMR genes for surveillance
Nordahl Petersen T et al. 2015. Sci Rep.
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DTU Food, Technical University of Denmark
Disease hotspot surveillance - Slumcity of Kibera in Nairobi, Kenya
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DTU Food, Technical University of Denmark
Disease hotspot surveillance - Slumcity of Kibera, Nairobi, Kenya
• Monitoring the vulnerable populations of Kibera– Collected 2 sewage samples every day for 3 months
• Demonstrate the application of using a metagenomics approach– to detect potential disease outbreaks – to develop corresponding intervention and prevention
strategies
• Apply a temporal metagenomics analysis to identify and quantify human pathogens including bacteria and associated antimicrobial resistance, virus, and parasites
– correlate with the disease trends from collected syndromic surveillance data and visits to the clinic
• Currently working with EBI to share data– PRJEB13833 - Kibera Sewage Project
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DTU Food, Technical University of Denmark
Disease hotspot surveillance - Slumcity of Kibera, Nairobi, Kenya
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DTU Food, Technical University of Denmark
Global sewage surveillance - 2016
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Global sewage surveillance - 2016
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DTU Food, Technical University of Denmark
• Information about presence and distribution of (pathogenic) bacteria, virus and parasites on a global scale
• A proof-of-concept of large-scale population surveillance using state-of-the-art technologies, metagenomics
– Provide better and faster detection and control of health risks– Potentially reduce morbidity and mortality through rapid disease
detection– Reduce development of antimicrobial resistance.– Improve treatment outcome and minimize disease spread
• Sample processing - Samples are divided into fractions– 250 ml for DNA (bacteria / virus / parasites) & RNA (virus)extraction– 250 ml for bacterial plasmid purification– 150 - 400 ml for Residue analysis
• PRJEB13831 - Global Sewage Project (Currently working with EBI to share data among COMPARE partners before release)
Global sewage surveillance - 2016
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DTU Food, Technical University of Denmark
Copenhagen according to sewage - 2016
“Real time” sharing of data: PRJEB13832 - Copenhagen Sewage Project (public – instant release of data)
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DTU Food, Technical University of Denmark
• Project start: 23-11-2015
• Samples are collected weekly - 80 samples till 02-05-2016– 3 sewage treatment plants:
• Avedøre (12 samples) • Damhusåen (35 samples) • Lynetten (33 samples)
• Samples are picked up every two weeks and brought to DTU and processed within a week (turnaround time 3 weeks)
– 250 ml for DNA (bacteria / virus / parasites) & RNA (virus)extraction
– 250 ml for bacterial plasmid purification
• Sequenced in-house by MiSeq– The sequences are uploaded to EBI directly after sequencing
Copenhagen according to sewage - 2016
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DTU Food, Technical University of Denmark
• WGS/NGS is rapidly entering diagnostic and public health, with near real time data generation
• Metagenomic sequencing is superior to conventional and other genomic methods for quantification of AMR and pathogens
– Need for better databases
• Bottleneck at level of bioinformatics and data sharing– Need for infrastructure and agreements to meet the coming
demand
• Novel sites should be explored for sampling for example large healthy populations e.g. hotspots, mass gatherings, cities etc..
• Metagenomic data are complex– Perspectives to combine with advanced mathematical
modelling for predictions
Conclusions
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Thank you for your attention
Oksana Lukjancenko, PhDRene S. Hendriksen, PhD
Research group of Bacterial Genomics and Antimicrobial ResistanceWHO Collaborating Centre for Antimicrobial Resistance in Food borne Pathogens
European Union Reference Laboratory for Antimicrobial Resistance
National Food Institute, Technical University of Denmark