Modeling for Quantitative Microbial Risk Assessment Thomas P. Oscar, PhD USDA, ARS Princess Anne,...

Modeling for Quantitative Microbial Risk Modeling for Quantitative Microbial Risk AssessmentAssessment

Thomas P. Oscar, PhDThomas P. Oscar, PhD

USDA, ARSUSDA, ARS

Princess Anne, MD, USAPrincess Anne, MD, USA

Risk AssessmentRisk Assessment

1.1. Hazard IdentificationHazard Identification 2.2. Hazard CharacterizationHazard Characterization

3.3. Exposure AssessmentExposure Assessment 4.4. Risk CharacterizationRisk Characterization

PredictiveMicrobiology

Food SafetyInformation

HazardsHazards

ChemicalChemical PhysicalPhysical

MicrobialMicrobial

Pathogen EventsPathogen Events(growth, death, survival, removal,

cross-contamination)

RareRare RandomRandom

VariableVariable UncertainUncertain

Rare Events’ ModelingRare Events’ Modeling

Iteration

1

2

3

:

100

Discrete

1

0

0

:

0

Pert (0,1,4)

1.8

1.2

0.2

:

2.2

Power

63.1

0

0

:

0

Round

63

0

0

:

0

=RiskDiscrete({90,10},{0,1})

=RiskPert(0,1,4)

=Power(10,Pert)

=Round(IF(Discrete=0,0,Pert),0)

Discrete()

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

90.0%

0.0000 1.0000

Pert(0.021, 0.057, 0.24)

0

2

4

6

8

10

12

0.0

0

0.0

5

0.1

0

0.1

5

0.2

0

0.2

5

5.0%90.0%

0.0314 0.1511

Risk PathwayRisk Pathway(Unit operations and pathogen events)(Unit operations and pathogen events)

Packaging(Contamination)

Distribution(Growth)

Cooking(Death)

Serving(Cross-contamination)

Consumption(Dose-response)

J. Food Safety (1998) 18:371-381

1 10 100 1000

1

10

100

1000

10000

100000

Raw Chicken, Salmonella/bird

Tem

pera

ture

Abu

se,

Salmonella

/bir

d

20%

J. Food Safety (1998) 18:371-381

Unit OperationUnit Operation Pathogen EventPathogen Event IncidenceIncidence ExtentExtent

PackagingPackaging Initial ContaminationInitial Contamination 20%20% 1 (0 – 3) log/bird1 (0 – 3) log/bird

DistributionDistribution GrowthGrowth 20%20% 0.5 (0.1-3.0) logs0.5 (0.1-3.0) logs

RARE

EVENTS

MODELING

RISK

ASSESSMENT

1 10 100 1000

0

2

4

6

305070

300400500600


Coo

king

,Salmonella

/bir

d0.9%

J. Food Safety (1998) 18:371-381


CookingCooking SurvivalSurvival 20%20% -1.5 (-2 to -1) logs-1.5 (-2 to -1) logs

1 10 100 1000

0

2

4

6

8

10100250400


Tot

al D

ose

Con

sum

ed,

Salmonella

7.0%

J. Food Safety (1998) 18:371-381


ServingServing Cross-contaminationCross-contamination 25%25% 2 (1 to 5)% transfer2 (1 to 5)% transfer

1 10 100 1000

0

250

500

750

1000


Infe

ctio

us D

ose,Salmonella

J. Food Safety (1998) 18:371-381

Normal Risk

High Risk


ConsumptionConsumption Normal RiskNormal Risk 80%80% 750 (500-1000) cells750 (500-1000) cells

High RiskHigh Risk 20%20% 200 (50 to 350) cells200 (50 to 350) cells

Relative risk of infection =Relative risk of infection =(Dose Consumed (Dose Consumed ÷ ÷ Infection Dose) * 100Infection Dose) * 100

1 10 100 1000

0

2

4

6

8

1015253545


Pro

babi

lity

of S

alm

onel

losi

s, %

J. Food Safety (1998) 18:371-381

Higher risk!

Hazard IdentificationHazard Identification

CornerstoneCornerstone ExpensiveExpensive

Number and SubtypeNumber and Subtype PackagingPackaging

Microbial EcologyMicrobial Ecology

MinorityMinority UnattachedUnattached

AttachedAttached EntrappedEntrapped

Standard incubation conditions

Predictive ModelPredictive Model(Initial Contamination(Initial Contamination))

Detection limit = 102 cells/ml

Target pathogen (< 1/ml)

DetectionTime

J. Food Prot. (2004) 67(6):1201-1208

0 1 2 3 4 5 6 70

5

10

15

20

25

Y = 1 + 4.89X - 0.31X2

R2 = 0.9611Most likely

Maximum

Minimum

Salmonella spp. (log number/25 g)

PC

R d

etec

tion

time

scor

e

Final Standard CurveFinal Standard Curve95% Prediction Interval95% Prediction Interval

Pert(1.4, 2.1, 2.9)X <= 1.6578

5.0%X <= 2.5890

95.0%

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

J. Food Prot. (2004) 67(6):1201-1208

Frequency PCR Score Min. Mode Max Random #90 0 NA NA NA 0 0 per 25 g1 1 to 9 0.0 NA 1.1 12 27 per 25 g2 10 0.0 0.2 1.7 2 0 per 25 g1 11 0.0 0.9 2.3 3 0 per 25 g2 12 0.0 1.5 2.9 27 27 per 100 g3 13 0.6 2.1 3.5 1161 14 1.3 2.7 4.2 1960 15 1.9 3.4 4.8 9340 16 2.6 4.0 5.5 4,2560 17 3.2 4.6 6.1 16,2740 18 3.8 5.2 6.0 234,5680 19 4.4 5.8 6.0 259,1730 20 5.0 NA 6.0 102,3240 21 5.6 NA 6.0 491,282

Iterations Incidence Min. Mode Max.10,000 34 0.00 1.52 3.96

Salmonella Load

Predictive ModelPredictive Model

J. Food Prot. (2004) 67(6):1201-1208

RareEventsModel

Exposure AssessmentExposure Assessment

Develop predictive models for hazard events from Develop predictive models for hazard events from hazard identification to consumption hazard identification to consumption

GrowthGrowth Survival Survival

Cross-contamination Cross-contamination Physical Removal Physical Removal

General Regression Neural Network (GRNN) ModelGeneral Regression Neural Network (GRNN) Model

-1 0 1 2 3 4 50

1

2

3

4A) RiskPearson5(4.4594,1.5797,RiskShift(-0.26825))

Output dataDistribution fit

Log change

Fre

quency

RareEventsModel

J. Food Prot. (2009) 72(10):2078-2087

Hazard CharacterizationHazard Characterization

Severity of Illness

InfectedMild

Illness Illness

Doctor

SevereIllness

Hospital

ChronicDisability Death


UniformUniform

Pathogen Food HostPathogen Food Host Human feeding trials are no

longer ethical!

J. Infect. Dis. (1951) 88:278-289; Risk Anal. (2004) 24(1):41-49.

RareEventsModel

Scenario C

4 5 6 7 8 9 100

20

40

60

80

100

Dose (log10)

Salm

onel

losi

s (%

)

Risk Anal. (2004) 24(1):41-49.

Disease Triangle ModelingDisease Triangle Modeling

Pathogen Host

Food

-1 log -2 log

-0.5 log

Very youngVery oldCancer

DiabetesHIV

Pregnant:

Top clinical isolateAcid resistant

:

High fatAnti-acid

:

Oscar, book chapter, in press

High Risk

Disease Triangle Model

RareEventsModel

Relative versus Absolute RiskRelative versus Absolute Risk

0%Absolute

100%Absolute

There will always be data

gaps!

100%Uncertainty

0%Uncertainty

Scenario AnalysisScenario Analysis

Plant APlant A Plant BPlant B


What if ?

Risk PathwayRisk PathwayPackaging

(Contamination)


Washing(Removal)

Cooking(Survival)

Serving(Contamination)


I see only one risk pathway

Plant A Plant B

Module A

90%90% 10%10% Plant B


RareEventsModel

Module B Oscar, book chapter, in press

RareEventsModel

Risk Assessment ResultsRisk Assessment Results

0 5 10 150.0

0.1

0.2

0.3

0.4Plant A

Plant B

Response Rate (per 100,000)

Fre

qu

ency

n = 200 replicate simulations per scenario

I see two risk pathwaysI see data

gaps!Hazard strainTime & TempPredictive

ModelsConsumerSurveys

Packaging(Contamination)


Washing(Removal)

Cooking(Survival)

Serving(Contamination)


Plant A Plant B

Research ResultsResearch Results

Plant APlant A Plant BPlant B

Initial ContaminationInitial Contamination 25%25% 10%10%

Temperature AbuseTemperature Abuse 20%20% 40%40%

WashingWashing 15%15% 30%30%

Proper CookingProper Cooking 90%90% 90%90%

Cross-contaminationCross-contamination 15%15% 30%30%

High Risk FoodHigh Risk Food 10%10% 10%10%

High Risk PathogenHigh Risk Pathogen 20%20% 60%60%

High Risk HostHigh Risk Host 20%20% 30%30%

Filtered Results

Exposure AssessmentExposure Assessment

0

10

20

30

Pa

ck

ag

ing

Dis

trib

uti

on

Wa

sh

ing

Co

ok

ing

Se

rvin

g

Plant APlant B

Ha

zard

Inc

ide

nc

e (

%)

2

3

4

5

6

7

8

Pa

ck

ag

ing

Dis

trib

uti

on

Wa

sh

ing

Co

ok

ing

Se

rvin

g

Plant APlant B

Ha

zard

Nu

mb

er

(lo

g p

er

10

0,0

00

un

its

)


0 1 2 3 4 5 6 7 8

0

20

40

60

80

100

Plant BRD50 = 4.9

Plant ARD50 = 5.6

Hazard Dose (log)

Res

po

nse

(%

)



Risk CharacterizationRisk Characterization

0 5 10 150.0

0.1

0.2

0.3

0.4Plant A

Plant B


Fre

qu

ency

Single Risk PathwaySingle Risk Pathway Multiple Risk PathwaysMultiple Risk Pathways

0 5 10 150.00

0.05

0.10

0.15

0.20

0.25Plant APlant B


Fre

qu

ency

Unsafe

Safe

SingleRisk

Pathway

MultipleRisk

Pathways

Un

safe

Safe

Packaging

Consumption

Distribution Channel

CookingSafe

Unsafe

To maximize the public health benefit of food by ensuring its safety & consumption

Thank you for your attention!

Modeling for Quantitative Microbial Risk Assessment Thomas P. Oscar, PhD USDA, ARS Princess Anne,...

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