Improved Public Health by creating an interface between concern assessment and modeling.

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

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Improved Public Health by creating an interface between concern assessment and modeling.

  1. 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. 2. Public Health Health promotion Surveillance Monitoring Research Risk assessment Risk evaluation Risk awareness Outbreaks Communication Analysis Risk management Epidemics
  3. 3. Diagrams of a vector borne disease transmission cycle Primary vector: Mosquitoes, ticks, rodents, phlebotomes, culecoides, etc. Primary host: Disease/Infection ? 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. 4. Factors influencing the probability of infections by Vector borne disease pathogens
  5. 5. Analysing outbreak related paramters • analysing data • evaluating parameters Host in Host in public health focus modelling focus Host related parameters Enviromental related parameters • • • • • • • • • • • • • • • • 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 • • • • • • 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. 6. Public health: risk communication & control strategy for vector‐borne diseases Interactions between Public Health & Modeling Public Health measures & prepardness activities     React on foreseeable trends. Cope with upcoming risks. Strategy to handle uncertainties Communication of future developments Early warning   Planning oriented strategy relevant parameters are known and the develoment can be predicted  Preventive strategy Changing parameters are acceptable and can be handled  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. 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. 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. 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. 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. 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. 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. 13. Questions for Discussion • 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? • How do modellers need to adapt modelling practice? What is risk? How do we produce interpretations relevant to PH risk assessment? • • • • 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

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