Bett, Bernard K.

Gachohi, John M.

Mariner, Jeffrey C.

CGSpace

Decision support framework for managing Rift Valley fever in the Horn of Africa    Jeffrey C. Mariner, Bernard Bett and John Gachohi  Presentation at a workshop on Rift Valley fever: Challenge, prevention and control,  Mombasa, Kenya, 13-15 November 2012. Overview • ILRI RVF Research Program – Decision Support Framework (DSF) – RVF Modelling – RVF Risk Factors – Economic Scenario Analysis of DSF • Risk-Based Decision Support Framework – 2006-2007 Impact Study  – Process and Publication – Future Directions  2 Overview Risk-Based Decision Support Framework (DSF) • Participatory Process: – Risk map – Matrix of actions matched to events  • RVF epizootic events list • Action categories • Stakeholder built – Selected information, resources and references 3 RVF Modellng • A spatial, agent based, stochastic model • Mechanisms of  RVF persistence • Predict risk, impact of RVF and interventions • Descriptive analyses  • Regression models: –Generalized Linear Mixed models   Poisson model for incidence  Logit models for prevalence –MCMC/spatial multiple membership model To account for spatial autocorrelation           Risk Factor Analysis Variable Source Description Livelihood zones FEWSNET Livelihood practices as at 2006 Land cover FAO on-line database Global land cover data, 2000 Precipitation ECMWF Monthly minimum, maximum and average for the period: 1979 - 2010 NDVI Spot Vegetation Monthly average, minimum, maximum values from: 1999 - 2010 Human population Kenya National Bureau of Statistics Human and household census for 1960, 1970, 1980, 1990, 1999 Elevation CSI SRTM Soil types FAO FAO’s Harmonized World Soil Database (HWSD), 2009 Wetlands (area as % of total) ILRI GIS Unit Parks/reserves (area as %) ILRI GIS Unit  Risk Factor Analysis - predictors Divisions that have had RVF outbreaks  in Kenya between 1912 and 2010 00.020.040.060.080.10.120.140.160.180%1%2%3%4%5%6%7%8%Month Proportion of divisions affected 14% 16% 18%  505 divisions -1999 population census   20.2 % (n = 102) of the divisions have had an outbreak at least once   Mean outbreak interval : 5.4 (4.4 – 6.4) years    Temporal distribution of RVF outbreaks: 1979 - 2010 Risk Factors Variable  Level β SE   β SE Fixed effects            Constant   -3.74 0.69   -6.18 0.92 Precipitation 0.11 0.03 0.16 0.04 NDVI   2.68 0.80   3.29 0.83 Soil types Solonetz 1.34 0.49 1.64 0.62 Luvisols 1.24 0.45 1.80 0.59 Elevation < 2300 m 0.00 - 0.00 -  > 2300 m -2.99 0.64   -3.79 0.95 Random effects             Livelihood zones   3.16 0.61   9.37 3.02 Deviance     841.57 Models for the persistence of outbreaks  Multi-level Poisson model MCMC/Bayesian model Sandik Case Definition: RVF Compatible Event  • Abortion • Heavy rains and mosquitoes • Froth from the nose, often with epistaxis • Salivation • Fever • Death, particularly in young animals  An outbreak in sheep and goats involving abortions during periods of heavy rain and abundance of mosquitoes, with two or more other listed clinical symptoms being observed in the herd, should be reported as RVF compatible disease to public health authorities.  Cattle in the same area will be affected with similar but less severe symptoms, and rarely camels.  Average Timeline Average time from:   •Onset of rains to mosquito swarm: 33.1 days  •Mosquito swarm to first animal case: 19.2 days  •First animal case to first human case: 21 days   •First humane case to medical service intervention: 35.6 days  •First medical service intervention to first veterinary intervention: 12.3 days •First animal case to veterinary service intervention: 68.9 days   Rains Vectors Livestock Human Human Vet33.1 days 19.2 days 21 days 35.6 days 12.3 daysRisk Factors Cases ResponseSo why was the response so late?  • All or nothing decision • Waiting for perfect information • Risk avoidance 11 Optimal Decision-Making • Recognizes – The need to balance the need information against the need for a timely response – That information will be imperfect – That decision making involves taking risk  • How can we make decision-making less risky – Phased – Shared  12 Lessons Learnt Decision Points • Early warning or alerts • Localized heavy rains observed • Localized flooding reported • Mosquito swarms • Livestock disease • Laboratory confirmation • Human disease • Laboratory confirmation  13 Progressive Risk Mitigation • Consequence x probability of outcome • Probability increases at each decision point • Justification for investment in risk mitigation increases  • Risk of making the wrong decision decreases Phased Decision-Making00.20.40.60.811.20 1 2 3 4 5Decision PointsProbablity of Outcome14 Decision-Making Trade Off 15   No Info   Perfect InfoRisk of Being to LateRisk of Being WrongMethods • Initial workshop  – RVF events sequenced – Interventions inventoried  – Actions matched to event sequences • Expert review • Follow-up workshop • Peer review 16 Methods Tool vs Framework • Original name caused confusion – Informative dialogue • Modellers assumed it was model – Efforts to ‘fix’ the tool – The tool itself should output the decision • Strength of the ‘framework’ – Created and owned by decision-makers – Models can inform the discussion, but not drive the process   17 The Future DSF managing risk in trade • Transparent framework for managing RVF • Market events and interventions • Regional meeting in Dubai – Horn of Africa, Middle East, OIE – regional framework for trade – extend to other disease. – Current Application • Kenya and Tanzania • Development partnerships?   18 

Decision support framework for managing Rift Valley fever in the Horn of Africa

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Decision support framework for managing Rift Valley fever in the Horn of Africa

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