Other Slide by
Commentscomments powered by Disqus
Presentation Slides & Transcript
Presentation Slides & Transcript
Economic-epidemiological models to support artemisinin resistance containment in Myanmar Lisa White, Yoel Lubell, Tom DrakeMathematical And Economic Modelling Of Diseases, Mahidol-Oxford Tropical Medicine Research Programme, Bangkok, Thailand.
Challenges for malaria control in MyanmarContainment of artemisinin resistanceSparse or incomplete dataLimited resourcesHeterogeneous transmission Many simultaneous interacting projects Multiple candidates for interventionsDiagnosis and treatmentVector control and personal protectionTargeting migrant populationMono-therapy replacementOthers…..
Why model? Combine knowledge and data on the biology and economics of malaria control for logical evidence-based decision-makingDesign integrated cost-effective control strategy optimising partnership and collaborationaccounting for interactions between individual interventions and their individual and combined impactDefine indicators of success for integrated control strategiesAssess uncertainty when data are sparseRank new information in terms of its predictive and economic valueTrack record in other countries and/or diseases
Interaction between models, data and policyStatic health economic modelsto obtain ball-park estimates of the health and economic impact of strategy optionsDynamic transmission models to account for the interacting biological processes and population effectsSuite of modelsPolicy and scientific questionsAvailable dataPolicy decisions, new hypothesesPredictions and strategy designMonitoring and EvaluationCollection of new dataContinuous model developmentContinuous feedback to policymakers
Models can be simple or complex
A model for CambodiaStochastic Patch ModelTransmission and control mechanistic model [FLOWS]population density dataIncidence dataIntervention coverage dataPopulation behaviour and movement modelPopulation behaviour and movement dataSpaceTime
Population behavior and movement model3 subpopulations in each patchStaticPeople who do not move home but have connectivity with people from other patchesMobilePeople who between patchesRemotePeople who do not move home and have little connectivity with people from other patchesConnectivity between patches for static populations inversely proportional to distance Movement of mobile populations to be defined by data from the national census and from specially designed studies
Containment strategy designInclude interacting factorsTransmission settingPre-existing and proposed interventionsCost and DALYs
Containment strategy designScale up treatment of clinical casesIncidence is reducedThere is a monetary benefit
Containment strategy designScale up treatment of clinical cases AND bed net coverageContainment is achievedIncreased monetary benefit+
Containment strategy designReplace mono-therapyDelay spread of resistancePositive net monetary benefitmono
Containment strategy designScale up treatment of clinical cases AND bed net coverage AND reduce mono-therapyContainment is achieved without increasing the rate of spread of resistance+mono
Containment strategy designScale up treatment of clinical cases AND bed net coverage AND reduce mono-therapyIs not necessarily enough in some places.+mono
Containment strategy designScale up treatment of clinical cases AND bed net coverage AND reduce mono-therapyMass interventions may be required in some places+mono
Modelling can be useful at every stage of the malaria control and elimination programWhen to model?
AcknowledgementsMahidol-Oxford Tropical Medicine Research UnitNicholas J WhiteNicholas P J Day François H Nosten Elizabeth AshleyArjen M DondorpMalaria ConsortiumSylvia Meek CNMChar Meng Chuor Chea NguonSiv SovannnarothPo LyDuong SocheatSuite of modelsPolicy and scientific questionsAvailable dataPolicy decisions, new hypothesesPredictions and strategy designMonitoring and EvaluationCollection of new dataContinuous model developmentContinuous feedback to policymakers