Joint AESAN/EFSA Workshop ‘Science Supporting Risk Surveillance of Imports’ - 10 February 2010, Seville (Spain)

Tools for analysing data from alert systems

Nepusz, Tamas

Petroczi, Andrea

Taylor, Glenn

Naughton, Declan

School of Life Sciences, Kingston University, London (United Kingdom)

D.Naughton@kingston.ac.uk

Trend analysis of the Rapid Alert System for Food and Feed (RASFF) database over the past

decade has revealed patterns demonstrating the improvement (or otherwise) of food safety

measures across the EU. This approach may also provide forecasting data to inform on smart

testing regimens. The aim of this work is to explore the use of network analysis in food safety to

provide a method of capturing the complexity of reporting and reported countries along with

simultaneous visualizations and performance indices.

We have developed a user-friendly analytical tool based on network approaches for instant

customized analysis of food notifications from the RASFF. Data taken from notifications

recorded in the RASFF between January 2003 – August 2008 were processed using network

analysis to i) capture complexity, ii) analyze trends, and iii) predict possible effects of

interventions by identifying patterns of reporting activities between countries. The detector and

transgressor relationships are readily identifiable between countries which are ranked using i)

Google's PageRank algorithm and ii) the HITS algorithm of Kleinberg. The tool facilitates

consideration of total notifications and performance indices or for those based on metal or

mycotoxin contamination. The program identifies Iran, China and Turkey as the transgressors

with the largest number of alerts and also readily identifies the countries most affected. The

major detector countries are Italy, Germany and the UK. Additional applications and further

developments will be discussed.

References Network analytical tool for monitoring global food safety highlights China. Nepusz, T., Petroczi, A., and Naughton, D.P. (2009) PLoS ONE 4(8): e6680.

Food recall patterns for metal contamination analyses in seafoods: longitudinal and geographical perspectives. Nepusz, T., Petroczi, A., and Naughton, D.P. (2009) Food Environment International 35, 1030-1033.

Mercury, cadmium and lead contamination in seafood: a comparative study to evaluate the usefulness of Target Hazard Quotients. Naughton, D.P., Petroczi, A. (2009) Food and Chemical Toxicology 47, 298-302.

Worldwide food recall patterns over an eleven month period: a country perspective Nepusz, T., Petroczi, A., Naughton, D.P. (2008) BMC Public Health 8, 308.

Url for tool: http://staffnet.kingston.ac.uk/~ku36087/foodalert/

Tools for analysing data from alertTools for analysing data from alert systems

Tamas Nepusz, Andrea Petroczi, Glenn Taylor Declan Naughton

School of Life Sciences

Kingston University, Londong y,

AIMSAIMS

1. To analyse trends in global food alerts

2. To provide a user‐friendly tool to inform2. To provide a user friendly tool to inform “emerging” nations of patterns in global alertsalerts

3. In the longer term, to develop a tool that analyses data in ‘real’ time to warn of emerging incidents on a weekly basisemerging incidents on a weekly basis

RASFF tifi tiRASFF notifications (9,276; 61%)

Microorganism(70 7%)

Metal (10.9%)

Chemical  (15.5%) %

) )(70.7%) (10.9%) (15.5%)

1 9%)

ol(1.6%

s(0.4%)

Mycotoxins(50.4%)

Bacteria (20.3%)Arsen

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RASFF – all notifications: May ‘03 – Aug ’08

Trends in RASFF notificationsnotifications

Trends allow ‘forecasting’ to a degree

Ratio of metal notifications in seafood vs. all metal notifications

80% of metal notifications arise from seafood80% of metal notifications arise from seafood

(2008 = 8 months)

Heavy metals in foodstuffs

RASFF 2003 January – 2008 August

Metals in foodstuff (≥ 10 reports)( p )

More complex analyses ?More complex analyses ?

Transgressors [>100 reports against]

Detectors ‐ all

RASFF notifications by country between 2003‐2008.

Networks: Advantages.....?gNetworks

B d l i hiNetwork Analysis

• Based on some relationship (edge) between two nodes (e.g. countries) with edges having

• Capture complexity: takes simultaneously countries), with edges having 

‘weights’ (i.e. number of reports)the number of reports and the number of countries involvedcountries involved into consideration

• Good visualisation• Mathematical 

expression of network propertiesproperties

• Can be tailored to type of notificationyp

Focus on China Snapshot for July 2003 & 2004

Increase in alerts against food from China from ‘03 – ‘08

Network analyses tool for identifying the role and impact of country involvement

Classes of contaminant in food from Spain

Metals Mycotoxins

Total

Countries identified by transgressor impact

Impact  ≠ Number of reports 

Summary network studySummary – network study

• Demonstrate which countries are the major transgressors and detectorsg

• Demonstrate the impact of these countries

U f i dl l l h ib i• User‐friendly tool to analyse the contribution of each country and relation to others

TargetsTargets

• Assist countries ‘new’ to food testing

• Inform testing to avoid redundancyInform testing to avoid redundancy

• Prepare for malicious (terrorist) activities

• Identify major culprit nations

• Identify major detector nationsIdentify major detector nations

• Highlight trends in a nations’ contribution

Future aspirationsFuture aspirations• To allow selection by reasons for alert (taxonomy)To allow selection by reasons for alert (taxonomy)

• Include levels of contamination found (where li bl )applicable)

• To extend tool to international alert systems beyond RASFF 

• To promote release of international alerts in ‘liveTo promote release of international alerts in  live feed’ format  for immediate processing

T d l ‘li f d’ i l ti t• To develop an ‘live feed’ response in real time to warn about emerging patterns

» “prediction tool”

‘prediction tool’prediction tool

• Automatic feeds each day/week

• Flag systemFlag system– Red increased quantities and numbers of key alerts or key country of originalerts – or key country of origin

– Orange increased quantity or numbers of key l k f i ialerts ‐ or key country of origin

– No flag – usual pattern of alerts from usual countries

References• Nepusz, T., Petroczi, A., and Naughton, D.P. (2009)

Network analytical tool for monitoring global food safety highlights China. PLoS ONE 4(8): e6680.PLoS ONE 4(8): e6680.

• Nepusz, T., Petroczi, A., and Naughton, D.P. (2009) Food recall patterns for metal contamination analyses in seafoods: longitudinal and geographical perspectivesgeographical perspectives. Environment International 35, 1030-1033.

• Naughton, D.P., Petroczi, A. (2009) Mercury, cadmium and lead contamination in seafood: a comparative study to evaluate the usefulness of Target Hazard Quotients Food and Chemical Toxicology 47, 298-302.

• Nepusz, T., Petroczi, A., Naughton, D.P. (2008) Worldwide food recall patterns over an eleven month period: a country perspective BMC Public Health 8, 308.

• http://staffnet.kingston.ac.uk/~ku36087/foodalert/