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National Institutefor Public Healthand the Environment ViTAL WP5 Data analysis
Progress reportCentre for Infectious Disease ControlLaboratory for Zoonoses and Environmental Microbiology Ana Maria de Roda Husman, Martijn Bouwknegt,
Saskia Rutjes, Katharina Verhaelen and Froukje Lodder
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
Human and Animal viruses
• Human sources of pathogenic viruses
- HAdV
- NoV, HEV, HAV
> Feed back into QVRA
• Animal sources of pathogenic viruses
- PAdV, BPyV
> To trace sources of contamination and to target interventions
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
Virus contamination and reduction in food production chain
ProcessingPrimary production Retail
hAdV
pAdV
bPyV
HAV
NoV
HEV
Indicator virus
Pathogenic virus
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
GANTT diagram (excl. possible extension) T5.1 Data Gathering Workshop
T5.2 Analysis of gathered data
T5.3 Modular Process Risk Model (MPRM) development
T5.4 Prioritization of risk assessment criteria
T5.5 Assessment of foodborne virus risks along the developed MPRM
T5.6 Quantitative Viral Risk Assessment on intervention measures
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
T5.1 and T5.2 Workshop and Analysis of gathered data
• Type of data
- Virus concentrations in sources of contamination, during processing and at point-of-sale
- Consumption of shellfish, fresh produce, soft fruits and pork meat products
- Dose-respons relationships for HAV, HEV, NoV
• Quality of data
• Quantification of viruses
• Tool to collect data
• Data analysis tool
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
T5.3 Development of Modular Process Risk Models (MPRM)Steps
• Definition of the statement of purpose, the (microbial) hazard and the food product
• Description of the food pathway
• Building the MPRM model structure by splitting up the food pathway into the modules
• Collection of the available data and expert opinions according to the model structure developed
• Selection of the model to be used for each module
• Plug the available data into the model
• Exposure assessment
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
T5.4 Prioritization of risk assessment criteria
• Virus concentration in contamination source • Methodology of virus detection
- Volume tested- Recovery- Detection limit
• Natural inactivation pre-harvest- Sunlight- Temperature etc.
• Treatment post-harvest- Disinfection - High pressure- Radiation
• Consumption • Dose-response
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
Deliverables (excl. possible extension)
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
D5.1 Guidance document on data collection and analysis
• Different food chains• Different phases
1. Identification of sampling points2. Sample sizes3. Virus detection by (RT-)PCR
Changed to Individual Guidance documentsSubject to further discussion by Martijn
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
D5.2 Tool for data analysis
Go to:Quantification of PDU for water samplesQuantification of PDU for fertilizer, faeces, serum, liver and meat
This tool can be used to estimate by approximation the number of PCR detectable units (PDUs) per volume of a sample. The estimation is based onthe assumption that the PDUs are distributed homogeneously within samples. If this assumption cannot be made, then this tool cannot be used for estimation of the concentration. For more accurate estimates, other software tools, such as Mathematica (Wolfram Reasearch, Inc.) can be used.Nevertheless, the results obtained with this tool will provide a good indication of the order of magnitude of virus concentrations in the samples.
This tool works with drop-down menu's as well as blank cells that require insertion of volumes or weights. Values that need to be inserted or adjustedare presented in blue, whereas automatically calculated parameters are presented in red. When entering volumes or weights, please use the numericalpad of the keyboard to assure the correctness of decimal points.
This tools is created for individual samples. Hence, for each sample the spreadsheet has to be completed. The output should be copied into an externalfile (e.g. Word document or Excel sheet) and the values for the second sample should be inserted. Of course, if the parameter values and results of the molecular analyses are identical between samples, then identical estimators for the concentration will be obtained.
How to complete the speadsheet?First confirm (using the pull-down menu) that the assumption of homogeneous mixing is appropriate for the respective virus. Next, complete theparameter-section by entering the appropriate values using the labjournal. Then select the number of (RT-)PCR analyses that were done per sample (i.e. in triplicate, then select '3' from the pull-down menu). Subsequently select the number of serial 10-fold dilutions that were examinedper repeat, and select from the pull-down menu per dilution the presence ('1') or absence ('0') of target genomes as detected by molecular analyses.The estimated concentration and 95% interval are subsequently provided. Considering the amount of data that has to be processed, this estimationmay take some time on slower computers.
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
D5.3 Document on available models
• Literature review prepared in the first year
• On Vital website
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
D5.4 Risk assessment model
• Models needed for selected production chains
- Soft fruits
- Salad vegetables
- Pork meat
- Shellfish
• Target viruses
- NoV
- HAV
- HEV
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
D5.4 Possible RA model outcome
5 10 15 200.0
0.2
0.4
0.6
0.8
1.0
Number of ingested oysters
Prob
.ofinfectionProb.of
illness
Boxplot of the probability of NoV infection based on oyster monitoring data and household outbreak
data (Rodriguez manuscript in preparation)
Distribution of norovirus PDU concentration per oyster in outbreak case
Risk of NoV infection and risk of illness per number of oysters consumed for
screening data
f ( D; α, β) = 1 – 1F1(α, α + β; −D)
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
Manure
Irrigation water
Washing
Harvesters’ hands
Freezing
Fresh soft fruits
Surface / Ground waters
Natural Inactivation
Latrines
Production Processing Point-of-Sale
Chlorination
Soft fruit products
Door handles
Packaging
MODULE
Frozen soft fruits
D5.5 Modular Process Risk Model
Integrated Monitoring and Control of Foodborne Viruses
in European Food Supply Chains
National Institutefor Public Healthand the Environment
WP5 Data Analysis
End of project deliverables
D5.6 Comparison of assessed risks
• Data needed from data gathering labs
• Data collection sheet
D5.7 Data gap analysis
• Produced from D5.3 and D5.6
D5.8 Intervention strategies and their efficiency
• In conjunction with T6.2 and in addition to D5.6
Recommended