Makita, K.

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Epidemiology for strategic control of Neglected ZoonosesPresented at FAO-APHCA/OIE/USDA Regional Workshop onPrevention and Control of Neglected Zoonoses in AsiaJuly 16, 2015Obihiro, JapanAssociate Professor of Veterinary EpidemiologyKohei MakitaDivision of Health and Environmental Sciences (DHES), Rakuno Gakuen University, JapanInternational Livestock Research Institute (ILRI)Motivation• Problem of Neglected Zoonoses– Neglected because they are ‘invisible’– Cannot be controlled because ‘resource is limited’– Cannot be controlled because ‘responsibility is fragmented’– Persistency – ‘prediction‘ is needed to plan long-term policyOverview• Tool box to tackle with ‘invisibility’, ‘limited resources’, ‘fragmented responsibility’, and ‘needs of prediction’– Epidemiology cycle– Inter-disciplinarity– Risk-based surveillance– Risk assessment– Mathematical modelling– Animal Health EconomicsEpidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health EconomicsNoticing that disease/ problemis in a populationEpidemiology cycleDescriptive epidemiologyForming a hypothesisTesting a hypothesis(Analytical epidemiology)Removing a risk factorAdding a preventive factor(Intervention study)Assessment of interventionDetermination of the risk factorEpidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health EconomicsBrucella infected farms in Kampala, UgandaMakita K. (2009) PhD Thesis. The University of Edinburgh. Any hypothesis?Herd size and Brucella sero-positivityMakita et al. (2011) BMC Veterinary Research 7:60.Makita K. (2009) PhD Thesis. The University of Edinburgh.Any hypothesis?Number of cows in a herdProportion of brucellosis positive cows0.00.10.20.30.40.51 2 3 4 5 6 7 8 10 11 13 19 24Causal websMultivariable analysis(Generalized Linear Model)• Risk factors– Herd size• OR 1.3 (95%CI: 1.1-1.5), p<0.001– Recent abortion• OR 4.1 (95%CI: 1.0-17.6), p=0.059• Confounder– Free-grazing• OR=2.7, p=0.2• Removal of the factor from the model changes estimate of herd size by 20%Brucellosis is causing abortionで in large-scale farmswhich have lands for free-grazingNoticing that disease/ problemis in a populationEpidemiology cycleDescriptive epidemiologyForming a hypothesisTesting a hypothesis(Analytical epidemiology)Removing a risk factorAdding a preventive factor(Intervention study)Assessment of interventionDetermination of the risk factor VisualizationVisualizationVisualizationEpidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health EconomicsInter-disciplinary and trans-disciplinary approachesExample of disciplines：Medicine, Veterinary Medicine, Environmental Sciences, Socio-economicsDisciplineDisciplineDisciplineDisciplineInter-disciplinaryTrans-disciplinaryResearchPolicyCommunityEpidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health EconomicsJoint field activities among Medicine, Veterinary Medicine and Anthropology- Safe food fair food project in Mali (ILRI) -Brucellosis diagnosisof cattle (Veterinary)Human brucellosis diagnosis (Medicine)Learning food culture and farming(Anthropology)Joint field activities among Medicine, Veterinary Medicine and Anthropology- Safe food fair food project in Mali (ILRI) -Brucellosis diagnosisof cattle (Veterinary)Human brucellosis diagnosis (Medicine)Learning food culture and farming(Anthropology)Shared responsibility!Shared costs!Surveillance• Systematic and continuous collection, analysis, and interpretation of data, closely integrated with the timely and coherent dissemination of the results and assessment to those who have the right to know so that action can be taken. (A dictionary of Epidemiology 5th Ed. 2008)ActionAnalysisInterpretationDissemination PlanCollectionEpidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health EconomicsAuthorityFieldPassive surveillance・Report-based・Active collection of data fora specific purposeActive surveillanceRisk-based surveillance• Set a priority• Allocate resources effectively and efficiently• Selecting hazard and/or sub-population2006, 6:20Animal source foods• Two-thirds of human pathogens are zoonotic – many of these transmitted via animal source food• Animal source food is a single most important cause of food-borne disease• Many food-borne diseases cause few symptoms in animal host• Many zoonotic diseases controlled most effectively in animal host/reservoirEpidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health EconomicsFormal marketingInformal marketing insub-Saharan Africa (90-95%)Dominance of informal markets in developing countries“Absence of structured sanitary inspection” 19Informal ≠ Illegal20Risk AssessmentRiskManagementRisk CommunicationCodex Alimentarius CommossionFood safety risk analysisA tool for decision-making under uncertainty*Risk is a probability of occurrence of a scenario and its size of impact (Vose, 2008)Risk AssessmentRiskManagementRisk CommunicationFood safety risk analysisin informal marketing systemParticipatory methodsCodex Alimentarius CommissionRisk assessment framework (CAC/GL-30 (1999))Hazard identificationExposure assessmentHazard characterizationRiskcharacterizationValue chainProducers ConsumersMiddle menActors in informal milk sales in Kampala, Uganda• Plus milk retail shop without refrigerator and dairy farmers selling at farmsShop with a bulk cooler Shop with a small refrigerator Boiling centreTrader with cans on a bicycle Roadside vendor Roadside vendor25Quantitative dairy value chain in Kampala, UgandaSource: Makita K. et al. (2010). How human brucellosis incidence in urban Kampala can be reduced most efficiently? A stochastic risk assessment of informally-marketed milk. PLoS ONE 5 (12): e14188.Sources of the risk by production areasNakasongola2.6%Kayunga4.1%Peri-urban Kampala UrbanKampala15.9%Mbarara70.3%7.0% Brucellosis example (Uganda)Sources of the risk by milk sellersWithout refrigerator1.0%Roadside vendor1.1%Bicycle7.3%Small refrigerator12.8%Farm gate15.1%Bulk cooler62.7%27Brucellosis example (Uganda)Control options (90% of enforcement)Control options Reduction Inputs Feasibility Negative impact AssessmentNot to take any option 0.0 None High Risk remains Not recommendableConstruct a boiling centre in Mbarara62.3A boiling centre, legislation, fuelMiddle-highPrice up RecommendableConstruct boiling centres in peri-urban Kampala75.4Boiling centres, legislation, fuelMiddle Price up RecommendableEnforce milk shops to boil milk or to buy boiled milk68.9Legislation, fuel, facilities, enforceVery lowPrice up, many shops cannot affordNot recommendableBan of farm gate milk sales12.3Legislation, enforcementLowAlternative sales may not boilSingle measure does not change the riskBan of urban dairy farming14.8Legislation, enforcementMiddleLivelihood of urban farmers, milk supplyNot recommendableBan of milk sales by traders with a bicycle in urban areas6.6Legislation, enforcementHighLivelihood of traders, alternative transport may not boilSingle measure does not change the riskBan of roadside milk sales 0.8Legislation, enforcementHighLivelihood of traders, alternative transport may not boilSingle measure does not change the riskBan of milk sales at shops without a refrigerator0.8Legislation, enforcementHighLivelihood of traders, alternative transport may not boilSingle measure does not change the riskBrucellosis example (Uganda)Sensitivity Tornado -0.5 00.5 11.5 22.5p / 1 to 2days G13Cont rate B24Boiling C24p / Day 0 F131960 / Cont rate B111960 / Cont rate B16p / 3 to 4 days H131960 / Boiling C161960 / Boiling C11109/291 (Arcuri 2010Temperature D10N0 D4Mean of Incidence rate29Sensitivity analysis(From S. aureus food poisoning example)Prob. SA has SE genesProb. farmers boilProb. consumers boilContamination, farmStore milk 3,4 daysContamination, centreConsume on day 0Prob. centres boilContamination, farmStore milk 1,2 daysTemperatureInitial bacteria population*It provides efficient control optionsSource: Makita K. et al. (2012). Int. J. Food Microbiol.Advantage of participatory risk assessment• -Speed• -Affordability• -Flexibility in application• -Understanding of culture• -Best control option• -Potential to change behavior30Infectious disease modelling• Basic reproduction number (R0)– Total number of individuals directly infected by a single infected individual, when introduced to totally susceptible population – R0<1 Infection dies out– R0=1 Infection is maintained– R0>1 Infection takes overEpidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health EconomicsR0 as a communication tool• Example of Ebola epidemiology– Nature (2014 Oct 16)32SIR model and calculation of R0S IS: SusceptibleI: InfectiousRR: RecoveredSIR modelSIR model and calculation of R0S IdS/dt = -βSIRβSI αIdI/dt = βSI - αIdR/dt = αIModelling disease dynamicssir <- function(time, state, parameters) {with(as.list(c(state, parameters)), {dS <- -beta * S * IdI <- beta * S * I - gamma * IdR <- gamma * Ireturn(list(c(dS, dI, dR)))})}…….In the case of endemic diseases-Modelling deaths-S IdS/dt = -βSI + μN - μSRβSI αIdI/dt = βSI – αI - μIdR/dt = αI - μRμS μI μRμNSIR model and calculation of R0S IβSI = (α + μ)IRβSI αIR (Effective reproductive ratio) = {β/(α + μ)}*SμS μI μRμNβ/(α + μ)*S = 1SIR model and calculation of R0S I*In case all individuals are susceptible（S0 = N)RβSI αIR0 = {β/(α + μ)}*NμS μI μRμNForce of infectionEffect of vaccination against rabiesFinal size simulation (Hokkaido, Japan)Kadowaki, H., Makita, K. et al. In preparation75/200(37.5%)55/200(27.5%)14/200(7%)： 0%： 51.7%： 80%Vaccination coverageProportions exceeding two casesper introduction of a rabid doginto JapanApplication of mathematical modelling in Neglected Zoonoses control• Finding effective control options– Modelling is flexible– Solving parameters– Changing values of parameters to see how much R0 changesIndirect costsDirect costsEconomic effects of diseaseDiseaseResources Outputs Human Benefitdestroys resources through mortalitylowers efficiency e.g. through reduced feed conversionreduces quantity and qualitycosts from avoiding diseaseZoonosessub-optimal exploitation of resources due to adoption of production models to disease situation e.g. trypanotolerant cattle with low milk productionSource: McIneryney (1996)Epidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health EconomicsEg. Cost comparison for S. aureus mastitis control(Comparison of two dipping products)Product A： cheap butmany casesProduct B： expensive but fewer mastitisMakita K, Yamamoto H, et al. (2013) Journal of Veterinary EpidemiologyEpidemiology can provide solutions to…• Problem of Neglected Zoonoses– Neglected because they are ‘invisible’– Cannot be controlled because ‘resource is limited’– Cannot be controlled because ‘responsibility is fragmented’– Persistency – ‘prediction‘ is needed to plan long-term policyEpidemiology cycle Inter-disciplinarity Risk-based surveillance Risk assessment Mathematical modelling Animal Health Economics• Thank you for your attention!Email to: kmakita@rakuno.ac.jp

Epidemiology for strategic control of neglected zoonoses

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Epidemiology for strategic control of neglected zoonoses

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