Outline. Decision Support Systems. Mark Bronsvoort, MRCVS Centre for Tropical Veterinary Medicine, University of Edinburgh

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1 Decision Support Systems (making hard decisions with imperfect informatio Mark Bronsvoort, MRCVS Centre for Tropical Veterinary Medicine, University of Edinburgh 1 Outline What are decision support systems (DSS) What are the decisions What are the tools available What are the gaps What next 2 159

2 Why do we need DSS Decisions are becoming much more complex They often have far reaching effects in other economic sectors The livestock industry is undergoing major changes Important to make the right decision early Non-linearity in effects make prediction difficult and non intuitive - what is right at a local level may not be the best for the national herd 3 Decision Support Systems (DSS) Mainly computer systems Manage large amounts of data Summarise and integrate data to provide information in a timely manner (inferential) Allow decision makers to explore various what if scenarios (predictive) What is the best thing to do (normative) 4 160

3 Components Database Farms/animals/location Personnel Equipment Transport routes/trade routes GIS maps/spatial data Models Windspread/Rimpuff Interspread/InterFMD Optimization of resource allocation Prioritising dangerous contacts (DC) 5 161

4 Example of the number of databases being generated during the 2001 outbreak in Dumfries and Galloway (Thrusfield et al. 2005) 6 162

5 163 7

6 Models (from Matthews and Woolhous e 2005) 8 What are the decisions? 9 164

7 Decisions control FMD? Why control FMD? What are the benefits and costs of FMD control in EU? How will changes in the CAP affect this decision? If we control FMD, do we want to regain disease-free status or freedom with vaccination? What can we afford to do? If we want to be disease-free stamping out or vaccinate? DSS ARE ABLE TO HELP WITH THIS HIGH LEVEL DECISIONS 10 Decisions-risk assessments (prevention or eradication?) Expanded EU difficult to control borders Once within EU very difficult to trace animal products Integrated databases of imports available in real time Early warning Ready off the shelf assessments for all EU states and for all diseases eg Dutch system

8 12 Decisions Outbreak planning Where will FMD enter? Where to place resources Where are the high risk areas Where/who are the high risk farms Contact networks identify super spreaders (Source: Risk Solutions 2005)

9 Decisions-start of outbreak National movement ban Soon as possible (eg. 2 days earlier in UK 2001 could have reduced outbreak by ~50%) Target markets and dealers to ensure enforcement optimise cost of bans/effect on trade Pen-side tests Se/Sp Aging lesions Pictures on paper Differentials Digital tech transmit images to a pathologist Rapid valuation and slaughter 14 Welfare slaughter What are we trying to optimise? Cost getting back to trade quickly minimise number of slaughtered animals DSS WITH ECONOMIC COMPONENTS NEEDED

10 Decision outbreak -airborne spread 16 Decision-airborne spread Several models available Dependent on experimental data for reliable estimates Newer Puff models include topography RIMPUFF (Sorensen 2000) Well validated models LITTLE EVIDENCE FOR AIRBORNE SPREAD FROM PYRES

11 Decision how is epidemic spreading What species are affected? What parts of the country? How many clusters? How much long distance spread compared to local spread? HOW CAN THIS ALL BE SUMMARISED AND MODELLED TO PREDICT THE EPIDEMIC

12 How is the disease spreading? Long distance animal movement airborne Disease spread in 3 km zone 70% of UK2001 attributed to local spread Number of competing transmission mechanisms Poor resolution on local spread Total 177 IPs (Thrusfield et al 2005)

13 Decision - Control methods Stamping out IPs only IPs and DCs IPs, DCs and CPs Stamping out + vaccination suppressive vacc vacc to live vacc logistic ring regional 20 DEFRA contingency plan

14 Decision - vaccination What serotype and strain is it? How many doses are needed? Will vaccination work? What is the risk of sub-clinical disease/carriers? What strategy to use eg. ring, regional, targeted? Can the SVS actually achieve the minimum coverage in time? Implications for trade etc. 22 (Keeling et al 2003)

15 24 173

16 Summary from Risk Solutions Decision support tool Incursion locations Virus characteristics Regionalisations of economic impacts Vet/cull team resources DC tracing effectiveness

17 175

18 Decision - post vaccination sero surveillance How to do surveillance how good is the NSP test What is the likely seroprevalence Are seropositive herds to be culled are carriers identifiable serological profiling (Bergmann et al 2000)

19 Two stage testing- OIE

20 Decision - post slaughter cleanup What risk do depopulated IPs represent How long are they infectious for How can they be decontaminated at reasonable cost 29 Epidemic models Imperial model (Ferguson et al. 2001) Edinburgh/Cambridge model (Keeling et al. 2001) Interspread/InterFMD (Morris et al 2001) Silent Spread/ExoDis Model (Risk Solutions 2005) Lattice Model (Kao 2003) Davis Model (Thurmond et al 2004)

21 Imperial Model (Ferguson et al 2001) Differential equations few parameters quick to run assumes random mixing not spatially explicit deterministic no species differentiation 31 Edinburgh/Cambridge Model Microsimulation model few parameters computationally intense spatially explicit stochastic does not include airborne spread does not include logistics vaccination module being added models all spread as kernel density function accounts for species on farm

22 Interspread Microsimulation model many parameters computationally intense spatially explicit designed as a DSS logistic modules many transmission mechanisms explicit airborne spread included more widely used flexible accounts for different species on farm

23 Silent spread/exodis Risk Solutions 2005, developed for DEFRA Microsimulation model uses kernel density function also allows explicit modelling of other transmission mechanisms airborne module logistics module vaccination module spatially explicit moderate number of parameters includes intra-herd dynamics NEW

24 Intra-herd dynamics (Source: Risk Solutions)

25 Have combined epidemic model with economic model Potentially very powerful Silent spread/exodis Risk Solutions 2005, developed for DEFRA

26 Comparison of models Imperial Edinburgh/ Cambridge Interspread ExoDis No. parameters few few many some Spatially explicit Different species Airborne spread Different transmission mechanisms Intra-herd transmission dynamics Logistic/resources Vaccination strategies 37 Models - a word of caution All models are WRONG They should be used only to SUPPORT decisions Very useful for resource planning/allocation useful for exploring what if scenarios They are NOT good at predicting random behaviour Models often reflect the biases of the modeller and lack objectivity (James 2005) BE CAREFUL OF NUMBERS/ MODELS POSING AS TRUTH (lack

27 How good is the model? (Source: Risk Solutions 2005) 39 UK livestock densities (Source DEFRA)

28 Actionable items Databases trade/contacts/capture Improved quality and efficiency of veterinary investigation Better understanding of transmission molecular analysis Biosecurity Economic models incorporated Post vaccination surveillance currently underway Can we stamp out or vaccinate? Do we have the DSS to call this in time Models for endemic FMD control 41 GAPS Vaccination Validation Diagnostic test performance Epidemiological characteristics of new viruses Rapid detection of new cases Identifying high risk farms Risk of introduction Risk of spreading Local spread

29 Keep it simple Keep it flexible Make it transparent DSS 43 Thank you Questions? 44 (Sørensen et al. 2000; Keeling et al. 2001; Morris et al. 2001; Sørensen et al. 2001; Kao 2002; Morris et al. 2002; Tomassen et al. 2002; Kao 2003; Keeling et al. 2003) Kao, R. R. (2002). "The role of mathematical modelling in the control of the 2001 FMD epidemic in the UK." Trends in Microbiology 10(6): Kao, R. R. (2003). "The impact of local heterogeneity on alternative control strategies for foot-and-mouth disease." Proceedings of the Royal Society of London Series B- Biological Sciences 270(1533): Keeling, M. J., Woolhouse, M. E. J., Shaw, D. J., Matthews, L., Chase-Topping, M., Haydon, D. T., Cornell, S. J., Kappey, J., Wilesmith, J. and Grenfell, B. T. (2001). "Dynamics of the 2001 UK foot and mouth epidemic: Stochastic dispersal in a heterogeneous landscape." Science 294(5543): Keeling, M. J., Woolhouse, M. E. J., May, R. M., Davies, G. and Grenfell, B. T. (2003). "Modelling vaccination strategies against foot-and-mouth disease." Nature 421(6919): Morris, R. S., Wilesmith, J. W., Stern, M. W., SSanson, R. L. and Stevenson, M. A. (2001). "Predictive spatially modelling of alternative control strategies for footand-mouth disease epidemic in Great Britain, 2001." Veterinary Record 149:

30 144. Morris, R. S., Sanson, R. L., Stern, M. W., Stevenson, M. and Wilesmith, J. W. (2002). "Decision-support tools for foot and mouth disease control." Revue Scientifique Et Technique De L Office International Des Epizooties 21(3): Sørensen, J. H., Mackay, D. K. J., Jensen, C. O. and Donaldson, A. I. (2000). "An integrated model to predict the atmospheric spread of foot-and-mouth disease virus." Epidemiology and Infection 124(3): Sørensen, J. H., Jensen, C. O., Mikkelsen, T., Mackay, D. K. J. and Donaldson, A. I. (2001). "Modelling the atmospheric dispersion of foot-and-mouth disease virus for emergency preparedness." Physics and Chemistry of the Earth Part B- Hydrology Oceans and Atmosphere 26(2): Tomassen, F. H. M., de Koeijer, A., Mourits, M. C. M., Dekker, A., Bouma, A. and Huirne, R. B. M. (2002). "A decision-tree to optimise control measures during the early stage of a foot-and-mouth disease epidemic." Preventive Veterinary Medicine 54(4):

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