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Complex Adaptive System of Systems (CASoS) Engineering Initiative Sandia National Laboratories is a multi-program.

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Presentation on theme: "Complex Adaptive System of Systems (CASoS) Engineering Initiative Sandia National Laboratories is a multi-program."— Presentation transcript:

1 Complex Adaptive System of Systems (CASoS) Engineering Initiative Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energys National Nuclear Security Administration under contract DE-AC04-94AL Modeling Animal Movement to Help Control PPR Walt Beyeler, Patrick Finley, Chrysm Watson Ross, William Fogleman Animal Disease Control Meeting - Dubai, UAE February 16 – 20, 2013 SAND C

2 CASoS Engineering Animal Disease Control Meeting February 2013 Overview Problem How a model of animal movement might help Model outline Dealing with uncertainties ­ Within the model ­ About the model Questions we hope to answer during the meeting 2

3 CASoS Engineering Animal Disease Control Meeting February 2013 Economic Impact of PPR PPR is highly contagious, spreading quickly through flocks, with high mortality. Goats and sheep are sources not only of milk and meat, but also as sources of easily mobilized income, particularly in lean times and such households have very low capacity to absorb external shocks including disease outbreaks. PPR not only causes animal death but also leads to reductions in milk yield and weight loss. 62.5% of the global domestic small ruminant population is at risk. 3

4 CASoS Engineering Animal Disease Control Meeting February 2013 Assumptions Spread of disease is significantly influenced by animal movement 4

5 CASoS Engineering Animal Disease Control Meeting February 2013 Assumptions Spread of disease is significantly influenced by animal movement Information about animal movement can be used to help control disease spread ­ Monitoring specific locations for early warning ­ Targeting controls to conserve resources/effort ­ Surveilling and/or accessing known static reservoirs 5

6 CASoS Engineering Animal Disease Control Meeting February 2013 Assumptions Spread of disease is significantly influenced by animal movement Information about animal movement can be used to help control disease spread ­ Monitoring specific locations for early warning ­ Targeting controls to conserve resources/effort ­ Surveilling and/or accessing known static reservoirs We can get enough of this information, and connect it appropriately to practical actions, to help 6

7 CASoS Engineering Animal Disease Control Meeting February 2013 Characteristics of the System Information about incidence of infection is hard to obtain and communicate Diagnostic criteria might be hard to satisfy – how useful could partial or uncertain information be? Resources for intervening are scarce, badly distributed, and hard to deploy and apply 7

8 CASoS Engineering Animal Disease Control Meeting February 2013 Possible Controls Will need to be modeled themselves (rather than taken for granted) because of the special logistical difficulties in Afghanistan Possible kinds of controls: Vaccination Quarantine? Culling? (as a control dynamic for the model, at least)? Constraints on administering controls that may constrain selection of a response Communication of decisions Compliance Movement of required resources (vaccines, personnel, etc.) 8

9 CASoS Engineering Animal Disease Control Meeting February 2013 Modeled Processes 9

10 CASoS Engineering Animal Disease Control Meeting February 2013 Modeled Processes 10 Movement of Diseased Animals Movement of disease -> Movement of animals -> Movement of people

11 CASoS Engineering Animal Disease Control Meeting February 2013 Modeled Processes 11 Movement of Diseased Animals Movement of disease -> Movement of animals -> Movement of people Progression of infection in an animal

12 CASoS Engineering Animal Disease Control Meeting February 2013 Modeled Processes 12 Movement of Diseased Animals Movement of disease -> Movement of animals -> Movement of people Progression of infection in an animal Detecting Conditions Detection of disease (observation, diagnosis, reporting)

13 CASoS Engineering Animal Disease Control Meeting February 2013 Modeled Processes 13 Movement of Diseased Animals Movement of disease -> Movement of animals -> Movement of people Progression of infection in an animal Detecting Conditions Detection of disease (observation, diagnosis, reporting) Transmitting Information Incidence of disease at various locations May be partial, inaccurate

14 CASoS Engineering Animal Disease Control Meeting February 2013 Modeled Processes 14 Movement of Diseased Animals Movement of disease -> Movement of animals -> Movement of people Progression of infection in an animal Detecting Conditions Detection of disease (observation, diagnosis, reporting) Transmitting Information Incidence of disease at various locations May be partial, inaccurate Planning Responses Planning Responses

15 CASoS Engineering Animal Disease Control Meeting February 2013 Modeled Processes 15 Movement of Diseased Animals Movement of disease -> Movement of animals -> Movement of people Progression of infection in an animal Detecting Conditions Detection of disease (observation, diagnosis, reporting) Transmitting Information Incidence of disease at various locations May be partial, inaccurate Planning Responses Planning Responses Deploying Information and Resources Diverse kinds of controls might be considered

16 CASoS Engineering Animal Disease Control Meeting February 2013 Movement of Diseased Animals Transmitting Information Planning Responses Planning Responses Deploying Information and Resources Detecting Conditions Implementing Controls Modeled Processes Movement of disease -> Movement of animals -> Movement of people Progression of infection in an animal Detection of disease (observation, diagnosis, reporting) Incidence of disease at various locations May be partial, inaccurate Diverse kinds of controls might be considered 16

17 CASoS Engineering Animal Disease Control Meeting February 2013 From Afghanistan PEACE Projects Afghanistan Livestock Market Assessment Report on Afghanistan Livestock Market Dynamics October 2008 – October 2009 Dr. Catherine A. Schloeder and Dr Michael J. Jacobs Space is described by a network rather than a map Model Structure - I Cultural practices and economics interact with geography to shape patterns of movement.

18 CASoS Engineering Animal Disease Control Meeting February 2013 From Afghanistan PEACE Projects Afghanistan Livestock Market Assessment Report on Afghanistan Livestock Market Dynamics October 2008 – October 2009 Dr. Catherine A. Schloeder and Dr Michael J. Jacobs Space is described by a network rather than a map Model Structure - I Cultural practices and economics interact with geography to shape patterns of movement. Animals are moved between very different kinds of locations (pasture, village markets, major markets). These create very different opportunities for disease transmission.

19 CASoS Engineering Animal Disease Control Meeting February 2013 From Afghanistan PEACE Projects Afghanistan Livestock Market Assessment Report on Afghanistan Livestock Market Dynamics October 2008 – October 2009 Dr. Catherine A. Schloeder and Dr Michael J. Jacobs Space is described by a network rather than a map Model Structure - I Cultural practices and economics interact with geography to shape patterns of movement. Animals are moved between very different kinds of locations (pasture, village markets, major markets). These create very different opportunities for disease transmission. Certain elements of geography are relevant: seasonality, security, terrain, (pasturage value and transportability) political/tribal affiliations.

20 CASoS Engineering Animal Disease Control Meeting February 2013 Model Structure - II 20

21 CASoS Engineering Animal Disease Control Meeting February 2013 Model Structure - II 21 Network elements Nodes are locations where animals stay for significant periods (e.g. pasture, villages) or change condition (e.g. markets, butchers) Links are pathways between nodes. Animals conditions might change as they move along links People and animals move on the network

22 CASoS Engineering Animal Disease Control Meeting February 2013 Model Structure - II 22 Network elements Nodes are locations where animals stay for significant periods (e.g. pasture, villages) or change condition (e.g. markets, butchers) Links are pathways between nodes. Animals conditions might change as they move along links People and animals move on the network Transmission events depend on disease stage, animal density, location, access to treatment Disease moves through populations at locations

23 CASoS Engineering Animal Disease Control Meeting February 2013 Model Structure - II Network elements Nodes are locations where animals stay for significant periods (e.g. pasture, villages) or change condition (e.g. markets, butchers) Links are pathways between nodes. Animals conditions might change as they move along links System state Number of animals in different disease states at each location People and animals move on the network Transmission events depend on disease stage, animal density, location, access to treatment Disease progresses within animals Disease moves through populations at locations Individual (representative) animals are modeled in different stages of disease. 23

24 CASoS Engineering Animal Disease Control Meeting February 2013 Uncertainties Information about incidence is partial and may be inaccurate. Modeling imperfections and delays in monitoring the system allows to design control measures that will work in the real environment. Many features of the modeled system are uncertain (disease characteristics, nomadic patterns, cross-border interactions). Including these uncertainties in policy evaluation, and using them to guide information collection, is a central part of the approach. 24

25 CASoS Engineering Animal Disease Control Meeting February 2013 Aspirations Define Analysis Evaluate Performance Define and Evaluate Alternatives Define Conceptual Model Satisfactory? Done Model Development: an iterative process that uses uncertainty

26 CASoS Engineering Animal Disease Control Meeting February 2013 Aspirations Define Analysis Evaluate Performance Define and Evaluate Alternatives Define Conceptual Model Satisfactory? Done Model Development: an iterative process that uses uncertainty Decision to refine the model Can be evaluated on the same Basis as other actions

27 CASoS Engineering Animal Disease Control Meeting February 2013 Aspirations Define Analysis Evaluate Performance Define and Evaluate Alternatives Define Conceptual Model Satisfactory? Done Model Development: an iterative process that uses uncertainty Decision to refine the model Can be evaluated on the same Basis as other actions Action A Performance Requirement Action B Performance Requirement

28 CASoS Engineering Animal Disease Control Meeting February 2013 Aspirations Define Analysis Evaluate Performance Define and Evaluate Alternatives Define Conceptual Model Satisfactory? Done Model Development: an iterative process that uses uncertainty Decision to refine the model Can be evaluated on the same Basis as other actions Action A Performance Requirement Action B Performance Requirement Model uncertainty permits distinctions

29 CASoS Engineering Animal Disease Control Meeting February 2013 Aspirations Define Analysis Evaluate Performance Define and Evaluate Alternatives Define Conceptual Model Satisfactory? Done Model Development: an iterative process that uses uncertainty Decision to refine the model Can be evaluated on the same Basis as other actions Action A Performance Requirement Action B Performance Requirement Model uncertainty permits distinctions Action A Performance Requirement Action B Performance Requirement

30 CASoS Engineering Animal Disease Control Meeting February 2013 Aspirations Define Analysis Evaluate Performance Define and Evaluate Alternatives Define Conceptual Model Satisfactory? Done Action A Performance Requirement Action B Performance Requirement Decision to refine the model Can be evaluated on the same Basis as other actions Model uncertainty permits distinctions Action A Performance Requirement Action B Performance Requirement Model uncertainty obscures important distinctions, and reducing uncertainty has value Model Development: an iterative process that uses uncertainty 30

31 CASoS Engineering Animal Disease Control Meeting February 2013 Questions for the Group Basic ­ Does animal movement contribute to the spread of disease? Or is this only believed to be the case? Does a network representation make sense? ­ What role does economics play in controlling movement? ­ What do we want to measure (death from PPR, sale of healthy animals, income from animal sales, ….)? ­ What are the specific kinds of controls we want to look at? Can animal movement be controlled in practice? Formulation given network structure ­ What are kinds of locations and processes that we should worry about first? ­ Should we concern ourselves with endemic (wild) population reservoirs? ­ Are there bottlenecks to regional animal movement? ­ Is there a useful SIR-type model for PPV Practical ­ Do published data correctly reflect conditions in the ground in Afghanistan? ­ Do we need to access transnational data and, if so, how reliable is it? ­ What kinds of controls would be supported? Are there ways build support for controls that couldnt be implemented now? 31

32 CASoS Engineering Animal Disease Control Meeting February 2013 Questions for the Group Basic ­ Does animal movement contribute to the spread of disease? Or is this only believed to be the case? Does a network representation make sense? ­ What role does economics play in controlling movement? ­ What do we want to measure (death from PPR, sale of healthy animals, income from animal sales, ….)? ­ What are the specific kinds of controls we want to look at? Can animal movement be controlled in practice? Formulation given network structure ­ What are kinds of locations and processes that we should worry about first? ­ Should we concern ourselves with endemic (wild) population reservoirs? ­ Are there bottlenecks to regional animal movement? ­ Is there a useful SIR-type model for PPV Practical ­ Do published data correctly reflect conditions in the ground in Afghanistan? ­ Do we need to access transnational data and, if so, how reliable is it? ­ What kinds of controls would be supported? Are there ways build support for controls that couldnt be implemented now? 32

33 CASoS Engineering Animal Disease Control Meeting February 2013 Questions for the Group Basic ­ Does animal movement contribute to the spread of disease? Or is this only believed to be the case? Does a network representation make sense? ­ What role does economics play in controlling movement? ­ What do we want to measure (death from PPR, sale of healthy animals, income from animal sales, ….)? ­ What are the specific kinds of controls we want to look at? Can animal movement be controlled in practice? Formulation given network structure ­ What are kinds of locations and processes that we should worry about first? ­ Should we concern ourselves with endemic (wild) population reservoirs? ­ Are there bottlenecks to regional animal movement? ­ Is there a useful SIR-type model for PPV Practical ­ Do published data correctly reflect conditions in the ground in Afghanistan? ­ Do we need to access transnational data and, if so, how reliable is it? ­ What kinds of controls would be supported? Are there ways build support for controls that couldnt be implemented now? 33

34 Complex Adaptive System of Systems (CASoS) Engineering Initiative Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energys National Nuclear Security Administration under contract DE-AC04-94AL Scraps 34

35 CASoS Engineering Animal Disease Control Meeting February 2013 Food Processing Farms Consumers Other Inputs We want to use our understanding of the food processing and distribution system to inform three questions: 1)If some specified contaminant is introduced at specific location(s) what is the effect on consumers? (how many would be affected, how quickly, and how badly) 2)What are the worst plausible scenarios for question (1) 3)If some contaminant or effect is observed in the system how effectively (quickly and accurately) can we identify the points of introduction? Can quick response reduce casualties? Where else might it have gone? 4)What changes to the system (protection, information collection, etc.) would best reduce consequences and speed trace-back? A network description which reflects processor operations, business rules, and logistical constraints, defines the possible flows of products We want a conceptual description that is clear and grounded, but is general enough to cover a broad range of applications. This is an investment in capability and a way of transferring insights across applications. We want a specific implementation that covers the current application without the overhead of features that might possibly be useful some day. We want to concentrate our efforts on describing this network as fully as possible, including explicitly characterizing our epistemic and aleatory uncertainties. 35


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