Global surveillance One World – One Health

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

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

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

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


• 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


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?


Monitoring large health populations


Metagenomics analysis – Quantification of all bacterial and virus including

AMR genes for surveillance

Nordahl Petersen T et al. 2015. Sci Rep.


Metagenomics analysis – Quantification of all bacterial and virus including

AMR genes for surveillance

Nordahl Petersen T et al. 2015. Sci Rep.


Disease hotspot surveillance - Slumcity of Kibera in Nairobi, Kenya


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


Disease hotspot surveillance - Slumcity of Kibera, Nairobi, Kenya


Global sewage surveillance - 2016



• 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)



Copenhagen according to sewage - 2016

“Real time” sharing of data: PRJEB13832 - Copenhagen Sewage Project (public – instant release of data)

http://www.google.dk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCN2yy9PZ7scCFarAcgoda8ID9A&url=http://www.sansebio.dk/lib_9.htm&psig=AFQjCNG3AAYjpYbrG6BHxnJFZ0Z_-u21xg&ust=1442051234486882


• 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


• 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

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

[email protected]

mailto:[email protected]

Education

Global surveillance One World – One Health