Presentation at the 19th meeting of ECDC National Focal Points for Microbiology (NMFP), held on 16-17 October 2018 in Stockholm (Sweden)

1. How do advances in technology help
us respond to Emerging Infections ?
Maria Zambon NMFP UK

2. Presentation title - edit in Header and Footer
Public Health England 2018

3. Presentation title - edit in Header and Footer

4. Response : Stepwise Risk Management
Formal risk
Assessment
Develop Diagnostics
Deploy diagnostics into wider
networks
Assess population susceptibility
Develop candidate vaccine
strains/countermeasures
Commission vaccine production
Clinical vaccine studies
Modelling for prediction of cost effective vaccination
Scale up production/stockpile
Mass vaccination

5. known
by self
ask
unknow
n by self
1 2
3 4
open/free
area
known
by
others
tell
unknow
n by
others
self-disclosure/exposure
hidde
n
area
unknown
area
feedback
solicitation
blind
area
shar
ed
disc
ov-
ery
others’
observation
self-
discovery
Johari
Window
Model 1955
Luft &
Ingham

6. Johari Window Model applied to
emerging threats
Known knowns Unknown knowns
Known
unknowns
Unknown
unknowns
• Seasonal Influenza
• HCAI infections
• New species Influenza
• Ebola
• Monkey Pox
• Lassa Fever
• M Chimera
• Avian Influenza
• Swine Influenza
• Pandemic influenza
• AMR/AVR
• MERS/SARS
• Zika

7. Techniques for response
capabilities
Known knowns Unknown knowns
Known
unknowns
Unknown
unknowns
• NAAT technologies
• Accurate and
targetted genomics
• Monitor diversity
Syndromic detection
Serology
WGS genomics
One health
NAAT technologies
WGS genomics
• Agnostic detection
Data linkage
• Serology
• Antibody engineering

8. Key Technologies for Risk Management
Genomics
Serology
Biological Sampling
Presentation title - edit in Header and Footer
Biological Sampling
Data Linkage
Antibody Engineering
Vaccine Production Platforms

9. Multiple
parallel NAAT
analysis
• Cost effective
• Rapid
Sensitive
PoC devices
• Improve sensitivity
• Rapid cohorting
• infection control
Metagenomics
• Improve
detection of
unknowns
Electronic
data transfer
& linkage
• Local detection
• Early warning
• Real time sequencing
• Trend analysis
What diagnostics improvements are in the
pipeline?

10. Genomics
Rapid detection based on knowldege of sequence
Track evolution
Analyse relatedness

11. Presentation title - edit in Header and FooterM Chimaera CID, Chand et al, 2016
Sequence Data Analysis
Compare & contrast
Inference from sequences
Influenza global update every 3-7 days
TB data sets 12 months, longer to get
international sharing of outbreak data
Skills & database limitations

12. Next 10 years of sequencing technology
development
BI-BBI = Bioinforrmatics
NOW
HiSeq/MySeq
Few pipelines
Limited BI capability
Largely Centralised
Develop workforce
capability
Improve field
capabilities
2028
More decentralised
More pipelines & platforms
Consolidation of databases
Greater BI capability
More rapid local capabilities
MiNion
Used in field
for Ebola
2016

13. Use of Metagenomics :Unusual syndromes
Acute Flaccid Paralysis
Full genome Cox B1
recovered
2018 AFP case : 20 year old returned from India
PCR Enterovirus positive faeces
Unable to sequence VP1 region
No growth in culture
……………………Agnostic metagenomics

14. Y
Y
* Type 2 γ or μ globulin
capture assay
Y
Y
* Type 1 indirect or anti-
globulin assay
*
Type 3 competitive assay
Y * Type 4 immunometric or antigen
sandwich
Serology : ELISADiversity of assay formats
Innovation
• Link ELISA approaches to automation
• Recombinant antigens/ Viral pseudotypes
• Bioassay/neutralisation/receptor binding

15. Using data on time to
seroconversion after infection
Plus the proportion of total cases
per week (from surveillance)
It is possible to work out
the probability
distribution of total
cumulative incidences
given the data
And thus model the most
likely rate of change of
seroprevalence (incidence)
over a new epidemic
Serology linked to mathematical modelling
Use of maximum likelihood estimation for estimating
seroincidence from sequential serological data.
Plus the change in
antibody prevalence
by day/week in the
samples tested

16. Dengue & Zika
Complex interactions require careful serology
Balsameda et al, PNAS 2017
Choice of ELISA format determines the reliability of results
to underpin modelling. Serology is a key technique for
response to emerging infections

17. Which is the right sample ?
Day 1 Days 3, 7,14,21,28
Directed BAL Non-directed BAL
Nose & throat swabs, oral fluid swabs, serology,
paxgene & whole blood
Nose &
throat
swabs
Oral fluid
swab
Serology
Paxgene
Whole
Blood
Clinical & diagnostic data
New materials

18. How to sample :
Can we use self sampling ?

19. Link sampling to information flow
Crowd sourcing ?

20. 7,092
records
10,488
records
884
non-respiratory
isolates
542
with NHS
number
5,954
with NHS number
495
patients
81 4,758
patients
4,263
patients
possible
cases
54
8
10
probable
cases
DIRECT CASE
REPORTING
18
REFERENCELABDATAROUTINELABDATA
• case note by study team + Trusts
Case finding: M. avium complex
within 4 years of surgery on bypass
National Risk
assessment
for M chimera

22. Vaccine Effectiveness
Comparisonsbetweenseasonsandcountriesnowpossible

23. Antibody Engineering in response to
Emerging Infections

24. Vaccine Innovation: invest in new
platforms for products in regular use
24

26. Strong commitment to R&D

27. • Surveillance & Cluster detection
• Syndromic diagnostics
• Rapid & broad response capability
• Old and New: Agnostic techniques
• Harness genomics capabilities
• Linkage & clinical networks essential
• Regulatory pathways improvements
for new interventions
Response Capabilities
Unknown Unknowns

28. Emerging Infections:
Challenges for Risk Management
•Decision making in real time
•Reliable methods credible scientists
•Information Management & Data Linkage
•Multidisciplinary & Multiagency response…?
•Collaborative working: Behaviour in
peacetime underpins response in emergency
•Rapid Public Release of Information.
•New Therapeutics..regulatory challenges