modeling for quantitative microbial risk assessment thomas p. oscar, phd usda, ars princess anne,...
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![Page 1: Modeling for Quantitative Microbial Risk Assessment Thomas P. Oscar, PhD USDA, ARS Princess Anne, MD, USA](https://reader035.vdocuments.net/reader035/viewer/2022062722/56649f295503460f94c4289e/html5/thumbnails/1.jpg)
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
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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
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HazardsHazards
ChemicalChemical PhysicalPhysical
MicrobialMicrobial
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Pathogen EventsPathogen Events(growth, death, survival, removal,
cross-contamination)
RareRare RandomRandom
VariableVariable UncertainUncertain
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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
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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
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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
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1 10 100 1000
0
2
4
6
305070
300400500600
Raw Chicken, Salmonella/bird
Coo
king
,Salmonella
/bir
d0.9%
J. Food Safety (1998) 18:371-381
Unit OperationUnit Operation Pathogen EventPathogen Event IncidenceIncidence ExtentExtent
CookingCooking SurvivalSurvival 20%20% -1.5 (-2 to -1) logs-1.5 (-2 to -1) logs
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1 10 100 1000
0
2
4
6
8
10100250400
Raw Chicken, Salmonella/bird
Tot
al D
ose
Con
sum
ed,
Salmonella
7.0%
J. Food Safety (1998) 18:371-381
Unit OperationUnit Operation Pathogen EventPathogen Event IncidenceIncidence ExtentExtent
ServingServing Cross-contaminationCross-contamination 25%25% 2 (1 to 5)% transfer2 (1 to 5)% transfer
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1 10 100 1000
0
250
500
750
1000
Raw Chicken, Salmonella/bird
Infe
ctio
us D
ose,Salmonella
J. Food Safety (1998) 18:371-381
Normal Risk
High Risk
Unit OperationUnit Operation Pathogen EventPathogen Event IncidenceIncidence ExtentExtent
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
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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
Raw Chicken, Salmonella/bird
Pro
babi
lity
of S
alm
onel
losi
s, %
J. Food Safety (1998) 18:371-381
Higher risk!
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Hazard IdentificationHazard Identification
CornerstoneCornerstone ExpensiveExpensive
Number and SubtypeNumber and Subtype PackagingPackaging
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Microbial EcologyMicrobial Ecology
MinorityMinority UnattachedUnattached
AttachedAttached EntrappedEntrapped
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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
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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
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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
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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
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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
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Hazard CharacterizationHazard Characterization
Severity of Illness
InfectedMild
Illness Illness
Doctor
SevereIllness
Hospital
ChronicDisability Death
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Hazard CharacterizationHazard Characterization
UniformUniform
Pathogen Food HostPathogen Food Host Human feeding trials are no
longer ethical!
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J. Infect. Dis. (1951) 88:278-289; Risk Anal. (2004) 24(1):41-49.
RareEventsModel
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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.
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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
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Disease Triangle Model
RareEventsModel
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Relative versus Absolute RiskRelative versus Absolute Risk
0%Absolute
100%Absolute
There will always be data
gaps!
100%Uncertainty
0%Uncertainty
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Scenario AnalysisScenario Analysis
Plant APlant A Plant BPlant B
Oscar, book chapter, in press
What if ?
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Risk PathwayRisk PathwayPackaging
(Contamination)
Distribution(Growth)
Washing(Removal)
Cooking(Survival)
Serving(Contamination)
Consumption(Dose-response)
I see only one risk pathway
Plant A Plant B
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Module A
90%90% 10%10% Plant B
Oscar, book chapter, in press
RareEventsModel
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Module B Oscar, book chapter, in press
RareEventsModel
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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
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I see two risk pathwaysI see data
gaps!Hazard strainTime & TempPredictive
ModelsConsumerSurveys
Packaging(Contamination)
Distribution(Growth)
Washing(Removal)
Cooking(Survival)
Serving(Contamination)
Consumption(Dose-response)
Plant A Plant B
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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%
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Filtered Results
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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
)
Oscar, book chapter, in press
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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
(%
)
Hazard CharacterizationHazard Characterization
Oscar, book chapter, in press
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Risk CharacterizationRisk Characterization
0 5 10 150.0
0.1
0.2
0.3
0.4Plant A
Plant B
Response Rate (per 100,000)
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
Response Rate (per 100,000)
Fre
qu
ency
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Unsafe
Safe
SingleRisk
Pathway
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MultipleRisk
Pathways
Un
safe
Safe
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Packaging
Consumption
Distribution Channel
CookingSafe
Unsafe
To maximize the public health benefit of food by ensuring its safety & consumption
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Thank you for your attention!