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