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Probabilistic Probabilistic Scenario Analysis (PSA)Scenario Analysis (PSA)

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PSA -HistoryPSA -History

In 1940’s - work on the atomic bomb In the 1950's - used as "what if" scenarios for

nuclear proliferation (Cooke, 1991) By 1960 - financial analysis, engineering

applications, and general economic evaluations Plant and animal health (Kaplan, 1993; Miller et

al., 1993; McElvaine et al., 1993)

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PSA – Risk TripletPSA – Risk Triplet

1. What can go wrong?

  2. How likely is that to happen?

    3. If it does happen, what are the

consequences?

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9 Points PSA Methodology9 Points PSA Methodology

State the questionIdentify the hazard of interestDevelop a scenario tree that outlines the

pathway of expected events and all the failure which could occur, culminating the occurrence of the identified hazard

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9 Points PSA Methodology9 Points PSA Methodology

Label the scenario tree and assign unitsGather and document evidenceAssign values to the branches of the

scenario treePerform the calculations to summarize

the likelihood of the hazard occurringConsider risk management optionsPrepare a written report

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Linking PSA to Risk AssessmentLinking PSA to Risk Assessment

Risk Assessment Probabilistic Scenario Analysis

1 What can go wrong that could lead to an outcome of hazard exposure? (Identification & Characterization of risk)

1. State the question to be investigated 2. Identify the hazard of interest. 3. Develop a scenario tree 4. Label scenario tree

2 How likely is this to happen? (Quantification of Risk)

5. Gather and document evidence 6. Quantify the nodes of the event tree 7. Perform the calculations to

summarize the likelihood of the hazard occurring

3 If it happens, what consequences are expected?

8. Consider risk management options 9. Prepare a written report

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What can go wrong: HazardsWhat can go wrong: Hazards

Hazard Examples

Food Safety E. coli in meat, Salmonella in eggs, Mercury in fish

Human Health HIV infection, Malaria infection, WNV infection

Animal Health FMD introduction, BSE introduction

Chemical Toxic chemicals released from chemical process

Thermal High energy explosion from a chemical reactor

Mechanical Kinetic or potential energy from a moving object.

Electrical Potential difference, electrical and magnetic fields, electrical shock

Ionizing Radiation Radiation released from a nuclear plant

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What can go wrong - an outcome of What can go wrong - an outcome of hazard exposure?hazard exposure?

Given a system or process with defined goals and methodologies: failures of components, procedures, safeguards, and mitigations can occur leading to hazards

To determine what can go wrong:State the question to be investigated Identify the hazard of interestDevelop a scenario tree

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What can go wrong - an outcome of What can go wrong - an outcome of hazard exposure?hazard exposure?

The process of defining a possible scenarios that lead to outcomes or events of interest, is called Scenario Analysis

The graphic depiction of all events, successes and failures of safeguards, procedures, and components that lead to outcomes of hazard exposure is called an Event Tree, Scenario Tree, or Risk Pathway Tree

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What can go wrong - exampleWhat can go wrong - example

Semen used in artificial insemination can be a means of exporting disease

You are importing semen from EuropeIs the semen that you receive infected?In Europe:

– Herds are selected– Boars are selected from the herds– Semen is collected from the boars– The semen is then sent to you

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Scenario Tree, Event Tree Scenario Tree, Event Tree

Herd Infected ?

Boar Infected ?

Boar Infected ?

Semen Infected ?

Semen Infected ?

Semen Infected ?

Semen Infected ?Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Is infected semen collected?

Infected Semen Collected ?

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Simplified Scenario Tree/Event TreeSimplified Scenario Tree/Event Tree

Herd Infected ?

Boar Infected ?

Boar Infected ?

Semen Infected ?

Semen Infected ?

Semen Infected ?Yes

No

Yes

No

No

No

No

Yes

No

Is infected semen collected? Infected Semen Collected ?

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Risk TripletRisk Triplet

1. What can go wrong ?

2. How likely is that to happen?

3. If it does happen, what are the

consequences?

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2.2. How likely is that to happen?How likely is that to happen?

A scenario tree has been developed to depict what can go wrong or right

The next step is to quantify how likely it is for the hazard depicted in the scenario/event tree to occur

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2.2. How likely is that to happen?How likely is that to happen?

Identify the specific question to be answered:What is the probability of imported sperm

from one animal being infected?What is the probability of imported sperm

from at least 1 animal being infected? In order to answer these questions, we need to

assign probabilities to the branches of each node in the tree

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Pictorial representation of probability: Event TreePictorial representation of probability: Event Tree A = Herd infected B = Boar infected, given herd infected C = Semen infected, given herd and boar infected

Herd Infected ?

Y

N

Boar Infected ?

Boar Infected ?

Semen Infected ?

Semen Infected ?

Semen Infected ?

Semen Infected ?

Y

N

Y

N

Y

N

Y

N

Y

N

Y

N

p = P(A)

p = 1-P(A)

p = P(B)

p = 1-P(B)

p = 0

p = 1

p = P(C)

p = 1-P(C)

p = 0

p = 1

p = 0

p = 1

p = 0

p = 0

p = P(A)*P(B)*P(C)

p = P(A)*P(B)*[1-P(C)]

p = P(A)*[1-P(B)]

p = P(A)*[1-P(B)]*0 = 0

p = [1-P(A)]*0*0 = 0

p = [1-P(A)]*0*0 = 0

p = [1-P(A)]*1*0 = 0

p = [1-P(A)]*1*1 = 1-P(A)p = 1 - [1 - P(A)*P(B)*P(C)]n

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Pictorial representation of probability: Risk Pathway Tree-Pictorial representation of probability: Risk Pathway Tree-no Mitigationsno Mitigations

NIs the Herd the boar is picked from Infected ?

Y

Is the Boar Infected ?

Y

N

Is the Semen Infected ?

Y

N

Infected Semen Exported to the USA

Boars Per Year from which Sperm is collected for Export

Initiating Event:Decision to collect Sperm from boars for Export

No Risk

No Risk

No Risk

A = Herd infected B = Boar infected, given herd infected C = Semen infected, given herd and boar infected

Prob. Imported semen from a boar is infected: p = P(A)*P(B)*P(C)Prob. Imported Semen from at least 1 boar is infected: p = 1 - [1 - P(A)*P(B)*P(C)]n

Frequency of importing infected semen: f = F*P(A)*P(B)*P(C)

p = P(A)

p = P(B)

p = P(C)

F

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Pictorial representation of probability: Risk Pathway Tree- Pictorial representation of probability: Risk Pathway Tree- with Mitigationswith Mitigations

NIs the Herd the boar is picked from Infected ?

Y

Infection Detected during Inspection of Herd ? Y

N

Is the Boar Infected ?

Y

N

Y

NInfection Detected during pre-semen Collection Inspection of Boar ?

Is the Semen Infected ?

Y

N

Infected Semen Exported to the USA

Boars Per Year from which Sperm is collected for Export

Initiating Event:Decision to collect Sperm from boars for Export

No Risk

As Planned - No Risk

No Risk

As Planned - No Risk

No Risk

F

P1

P2

P3

P4

P5

Prob. Imported semen from a boar is infected: p = P1*P2*P3*P4*P5Prob. Imported Semen from at least 1 boar is infected: q = 1 - [1 - p]n

Frequency of importing infected semen: f = F*p

Prob. Imported semen from a boar is infected: p = P1*P2*P3*P4*P5Prob. Imported Semen from at least 1 boar is infected: q = 1 - [1 - p]n

Frequency of importing infected semen: f = F*p

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Evidence GatheringEvidence Gathering

Label & Identify each parameter of the tree– F, Number of boars per year from which sperm is

collected for export– P1, Probability that the herd the boar is picked from is

infected – P2, Probability that infection is detected during

inspection of the infected herd from which the boar is picked

– P3, Probability that a boar is infected, given that it is from an infected herd that was not detected at inspection

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Evidence GatheringEvidence Gathering– P4, Probability that Infection is detected in an

infected boar prior to semen collection, given that it is from an infected herd that was not detected at inspection

– P5, Probability that the semen of an infected boar is infected, given that infection was not detected in the boar prior to semen collection, and given that it is from an infected herd that was not detected at inspection

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Evidence GatheringEvidence Gathering

For each Node/Parameter – Gather evidence, associate it with the

appropriate node/parameter, and reference it in a bibliography

– Evaluate the evidence quantitatively or descriptively. Determine the min, ml, and max values of each parameter that are consistent with the available evidence

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Uncertainty in P1, Herd PrevalenceUncertainty in P1, Herd Prevalence

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

y1y2y3

Probability density function (PDF)•PDF - Expresses the probability that a continuous random variable falls within some very small interval.•PMF - Expresses the probability that a discrete random variable takes on a specific value.•CDF - Cumulative distribution function F(x) = Prob (P1 ≤ x)

The flatter the PDF, the more the uncertainty.

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Monte Carlo SimulationMonte Carlo Simulation• A computer based methodology that uses statistical sampling techniques in obtaining

a probabilistic approximation to the solution of a mathematical equation or model

Symbol MIN ML MAX PDF

P1

P2

P3

P4

p=P1*P2*P3*P4*P5 or q = 1 - (1-p)nResult

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Risk Triplet Risk Triplet

1. What can go wrong that?2. How likely is that to happen?

3. If it does happen, what are the consequences?

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Pictorial representation of probability: Risk Pathway Tree-Pictorial representation of probability: Risk Pathway Tree-with Mitigationswith Mitigations

NIs the Herd the boar is picked from Infected ?

Y

Infection Detected during Inspection of Herd ? Y

N

Is the Boar Infected ?

Y

N

Y

NInfection Detected during pre-semen Collection Inspection of Boar ?

Is the Semen Infected ?

Y

N

Infected Semen Exported to the USA

Boars Per Year from which Sperm is collected for Export

Initiating Event:Decision to collect Sperm from boars for Export

No Risk

As Planned - No Risk

No Risk

As Planned - No Risk

No Risk

F

P1

P2

P3

P4

P5

Prob. Imported semen from a boar is infected: p = P1*P2*P3*P4*P5Prob. Imported Semen from at least 1 boar is infected: q = 1 - [1 - p]n

Frequency of importing infected semen: f = F*p

Prob. Imported semen from a boar is infected: p = P1*P2*P3*P4*P5Prob. Imported Semen from at least 1 boar is infected: q = 1 - [1 - p]n

Frequency of importing infected semen: f = F*p

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Consequences: Risk Pathway TreeConsequences: Risk Pathway Tree

Infection Caused in Sows

Infected units of semen Per Year

Initiating Event:Use of infected imported sperm

No Risk

F1

Frequency of Infection caused in sows, or in newborns: k = F2*P6 + F3*P7Frequency of Infection caused in sows, or in newborns: k = F2*P6 + F3*P7

NDoes Sow get infected ?

Y

Infection Caused in Newborns

Infected sows giving birth Per Year

Initiating Event:Infected Sows give birth

No Risk

F3

NDoes newborn get infected ?

Y P7

P6

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ConclusionConclusionRisk Assessment Model:

– allow the quantification of risk and uncertainty– help to identify gaps in knowledge, thereby defining

data needs– help to standardize approaches

Risk Assessment: should be transparent, flexible, documented, and consistent

The assessment should effectively communicate the insights that it reveals

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ConclusionConclusionRisk assessment models:

– allow the quantification of risk and uncertainty.– help to identify gaps in knowledge, thereby defining

data needs.– help to standardize approaches.

At a minimum: The Assessment should be transparent, flexible, documented, and consistent.

The assessment should effectively communicate the insights that it reveals.