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Complex Adaptive System of Systems(CASoS) Engineering Initiativehttp://www.sandia.gov/CasosEngineering/

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

Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

Modeling Animal Movement to Help Control PPR

Walt Beyeler, Patrick Finley, Chrysm Watson Ross, William Fogleman

Animal Disease Control Meeting ‐ Dubai, UAEFebruary 16 – 20, 2013

SAND 2013‐0939C

CASoS Engineering

Animal Disease Control Meeting16‐20 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

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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.

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Assumptions

• Spread of disease is significantly influenced by animal movement

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Assumptions


• 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

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Assumptions


• 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

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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

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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.)

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Modeled Processes

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Modeled Processes

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Movement of Diseased Animals

Movement of disease ->Movement of animals ->Movement of people

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Modeled Processes

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Progression of infection in an animal

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Modeled Processes

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Detecting ConditionsDetection of disease (observation, diagnosis, reporting)

CASoS Engineering


Modeled Processes

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Transmitting InformationIncidence of disease at various locationsMay be partial, inaccurate

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Modeled Processes

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Planning

Responses

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Modeled Processes

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Planning

Responses

Deploying Information and Resources


Diverse kinds of controls might be considered

CASoS Engineering



Transmitting InformationP

lanningR

esponses



Detecting Conditions

Implementing Controls

Modeled Processes



Detection of disease (observation, diagnosis, reporting)

Incidence of disease at various locationsMay be partial, inaccurate

Diverse kinds of controls might be considered

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From Afghanistan PEACE Project’s Afghanistan Livestock Market AssessmentReport on Afghanistan Livestock Market Dynamics October 2008 – October 2009Dr. Catherine A. Schloeder and Dr Michael J. Jacobs

Space is described by a networkrather than a map

Model Structure ‐ I

Cultural practices and economics interact with

geography to shape patterns of movement.

CASoS Engineering







Animals are moved between very different kinds of locations (pasture, village markets, major

markets). These create very different opportunities for

disease transmission.

CASoS Engineering







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.

CASoS Engineering


Model Structure ‐ II

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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

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Network elements• Nodes are locations where animals stay for




Transmission events depend on disease stage, animal density, location, access to treatment

Disease moves through populations at locations

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LatentMean duration 1.25

days

Infectious presymptomatic

Mean duration 0.5 days

IR 0.25

Infectious symptomaticCirculate

Mean duration 1.5 daysIR 1.0 for first 0.5 day,

then reduced to 0.375 for final day

Infectious symptomaticStay home



Infectious asymptomaticMean duration 2 days

IR 0.25

Dead

Immune

Transition Probabilities

pS = 0.5pH = 0.5pM = 0

pHpS

(1-pS)

(1-pH)pM

pM

(1-pM)

(1-pM)

LatentMean duration 1.25

days



IR 0.25



IR 0.25

Infectious symptomaticCirculate



Infectious symptomaticStay home



Infectious asymptomaticMean duration 2 days

IR 0.25

Dead

Immune

Transition Probabilities

pS = 0.5pH = 0.5pM = 0

pHpS

(1-pS)

(1-pH)pM

pM

(1-pM)

(1-pM)

Model Structure ‐ IINetwork elements• Nodes are locations where animals stay for



System state• Number of animals in different disease states

at each location


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.

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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.

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Aspirations

Define Analysis

Evaluate Performance

Define and EvaluateAlternatives

Define Conceptual Model

Satisfactory? Done

Model Development: an iterative process that uses uncertainty

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Aspirations

Define Analysis




Satisfactory? Done


Decision to refine the modelCan be evaluated on the sameBasis as other actions

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Aspirations

Define Analysis




Satisfactory? Done



Action A

PerformanceRequirement

Action B


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Aspirations

Define Analysis




Satisfactory? Done



Action A


Action B


Model uncertainty permits distinctions

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Aspirations

Define Analysis




Satisfactory? Done



Action A


Action B



Action A


Action B


CASoS Engineering


Aspirations

Define Analysis




Satisfactory? Done

Action A


Action B




Action A


Action B


Model uncertainty obscures important distinctions, and reducing uncertainty has value


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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 couldn’t be implemented now?

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Complex Adaptive System of Systems(CASoS) Engineering Initiativehttp://www.sandia.gov/CasosEngineering/

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

Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

Scraps

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CASoS Engineering


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.

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