GRF 2nd One Health Summit 2013: Presentation by Matthias Niedrig, Robert Koch Institut

1. Improved Public Health by
creating an interface between
assessment and modeling.
Matthias Niedrig, Kerstin Dressel
Robert Koch Institut,
Berlin, Germany
sine-Institut, gGmbH,
Munich, Germany

2. Public Health
Health
promotion
Surveillance
Monitoring
Research
Risk assessment
Risk evaluation
Risk awareness
Outbreaks
Communication
Analysis
Risk management
Epidemics

3. Diagrams of a vector borne
disease transmission cycle
Primary vector:
Mosquitoes, ticks,
rodents, phlebotomes,
culecoides, etc.
Primary host:
Disease/Infection
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Primary host:
Wild animals, e.g:
birds, rodents,
deers, etc.,
domestic animals
Bridge vector:
transmission
?
wild animals, e.g.:
birds, rodents,
deers, etc.,
domestic animals
Disease/Infection
Primary vector:
Mosquitoes, ticks,
rodents, phlebotomes,
culecoides, etc.
Mosquitoes, ticks,
rodents, phlebotomes,
culecoides, etc.
Dead end host:
Humans,
domestic animals
Disease

4. Factors influencing the
probability of infections by
Vector borne disease pathogens

5. Analysing outbreak related
paramters
• analysing data
• evaluating parameters
Host in
Host in
public health focus
modelling focus
Host related parameters
Enviromental related parameters
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Experience from previous outbreaks
Number of cases
Population density
Pre-existing immunity in the population
Pre-existing immunity in the vector
Immunology naive population
Host i
Diagnostic assays available
Perception of health risk in the population
Severity of disease/ symtomes / fatality
Effected population (children, adult, elderly)
Assecibility of the host for the pathogen
Existing knowledge by physicians
Existing knowledge of the population at risk
Behaviour of the population with health risk
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Experience from previous outbreaks
Weather conditions (temperature,
precipitation)
Climate conditions (temperature dynamics)
Host in
Reservoir distribution (country /urban site)
Vector distribution (mosquito abundance)
Vector density
Vector competence
Accesicibility of the vector for pathogens

6. Public health: risk communication &
control strategy for vector‐borne diseases
Interactions between Public Health & Modeling
Public Health measures
& prepardness activities
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React on foreseeable trends.
Cope with upcoming risks.
Strategy to handle uncertainties
Communication of future
developments
Early warning
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Planning oriented strategy
relevant parameters are known and
the develoment can be predicted
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Preventive strategy
Changing parameters are
acceptable and can be handled
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Experience
from previous
outbreak
scenarios:
Acute
outbreak
scenario:
Data analysis / risk preception
Parameter
Scanning
Monitoring
Predictive
model:
Scenario
creation
Model for future
outbreaks scenarios:
 Scenario analysis
 Scenario prognosis
Precautionary strategy
Many parameters are unpredictable
but trying to anticipate the
scenario by focusing on the most
important ones
Scenario
transfer
Scenario forecasting:
 Best case scenario
 Worst case scenario

7. Public health measures &
preparedness plans for different
scenarios.
human disease
outbreak
event
small
< 10 cases
mild
<1% fatality
Sandfly fever
Hanta, Tick borne
medium
10 -100 cases encephalitis, West
medium
1-10% fatality
severe
> 10% fatality
Crimean Congo
Haemorraghic
Fever
Rabies
MersCoV, Japan
Encephalitis
Yellow Fever, Lassa,
Ebola, Marburg
Influenza
SARS, pandemic
Influenza, HIV
Nile, Norovirus
big
> 100 cases
Dengue
rough classification for different scenarios

8. Evaluating the different
courses of diseases severity.
death
require intensive care
stay in a hospital
stay in home
visit physician
can’t work
feel unwell, can work
Financial impact
severity of disease
number of people duration of
preception
affected
disease
financial
burden

9. Knowledge for different
outbreak scenarios
Disease
N° of cases
Severity
Financial
impact
Sandfly
fever
19 cases Northern Italy (2013)
mild
unknown
mild, unknown
unknown
261 cases
Tick borne
encephalitis Germany (2013)
Hanta
123 cases
Germany (2013)
ca. 150,000 to 200,000 cases of
HFRS are hospitalized each year
world wide
unknown
West Nile
226 cases Europe
mild, unknown
unknown
(2013)
252 cases Angola 227 deaths
Marburg,
Ebola, Lassa (2004)
unknown
SARS
8273 cases world
wide (2003)
775 deaths
unknown
pandemic
Influenza
375,000-1.6 Mill.
UK (2009)
18,000 death, ca. 284,500 people
were killed by the disease
unknown

10. Analysing and combining the different
parameters for a one public health model.
severity of disease
best case scenario
N° of affected people
targeted PH measures
duration of disease
financial burden
financial costs
for PH measures
risk perception
predictive model for
estimated N° of
cases based on the
analysis of available
parameters
worst case scenario
adapted PH measures

11. Questions?
• Do you think that we need such a model
for improving PH management?
• What are the next steps to develop the
interaction between the modellers and
PH institutions?
• Does such a model help to improve risk
perception for PH issues?

12. Lessons Learned
Trust, time and persistence are needed to cross disciplinary barriers
An institutional home is needed to fund investigative studies to find out
who to involve, as well as when, where, & how to do involve them.
Translation is key. Modellers and PH practitioners should
identify and include relevant PH inputs in their model design
collaborate with the PH community to interpret outputs
adapt outputs for range of users highlighting PH information
model more than disease
New approaches, visions and clear prioritised and structured
strategies are required for complex, uncertain, multidimensional and
multidisciplinary problems involving many stakeholders: vets and
doctors, vulnerable people and animals, academics, organisations,
government, etc etc.
This approach is of course a central tenet of One Health and needs to
be incorporated in funding streams like Horizon 2020.

13. Questions for Discussion
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Who are the users? How do we define the level of
involvement of the public (Health) and how do we
implement concern assessment and risk
perception in modelling approach?
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How do modellers need to adapt modelling
practice?
What is risk? How do we produce interpretations
relevant to PH risk assessment?
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Do the structured strategies improve risk
perception for PH issues
What are the next steps to develop the interaction
between the modellers and PH institutions
Do you have examples of similar evolution of
transdiscipline understanding