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„If we tie a lot of cherry-peppers on a string, they’ll make a pepper-wreath.

However, if we don’t tie them on a string, they won’t make a wreath. Although it’s the same amount of peppers, just as red and just as hot. But still no wreath.

Does it only lie in the string? No, it doesn’t. That string, as we all know, is an incidental, third-rate thing.

Then what?

People capable of brooding over it and taking care not to let their mind wander about, but keep them on the right track may get a scent of eternal verities.”

Örkény István: The meaning of life

Translated by N. Ullrich Katalin

Cattle trade flow analysis

Conventional risk based planning

Methods

Implications for the future

Static and dynamic properties of the network were defined.

Degree: the number of relations or edges of the nodes

In-Degree: incoming connections

Out-Degree: outgoing connections

  • The data needed for the substantiated risk assessment are in many cases not available

  • The problem with the conventional risk based approach is that it doesn't take into account the network flow, the dynamics, just the pure output or production data and other static risk factors.

  • With application of network analysis tools, it is possible to add a new, dynamic, science based and standardized layer of information to the assessment and planning methodology, resulting in different - and presumably more realistic - outcomes

Degree has generally been extended to the sum of weights when analysing weighted networks and labelled as node strength: number of animals moved.

  • contributes to determination of the most vulnerable parts of a cattle holding network;
  • increases effectiveness of the control of the cattle-flow;
  • reveals the interdependencies;
  • helps in working out an optimised strategy of the inspection of herds;
  • increases preparedness against outbreaks and intentional attacks;
  • enhances epidemiological modelling simulations;
  • provides information on the source of possible infections so that preventive and control measures can be applied;
  • serves the food chain safety and network science community with analysable data and helpful descriptions of the methodology to enhance cross-border cooperation.

To understand the relative importance of different hubs in cattle flow, we had to apply the Centrality concepts of network analysis.

Results

Centrality measures

Structural properties

Based on the degree distribution the Hungarian cattle holdings network shows scale-free network characteristics: it is a network whose degree distribution follows a power law.

The most vulnerable points of a network are not necessarily their largest hubs. To extract information on the nodes playing central role in the network, different centrality measures were calculated: betweenness, closeness, authority and hub centralities were defined.

The degree exponent for the Hungarian cattle holdings network is 2,24.

There is a big difference between those centralities: in case of the authority and hub centrality a central node can be any node in the network, but in the case of betweenness and closeness centralities (as the names indicate) the central nodes can not be source-vertex or sink-vertex. The vertices of high betweenness centrality value are usually logistic centres, transloading places or major livestock farms.

Therefore it shows small world properties, meaning that only few steps are needed to get from a random point to another random point, having large implications in case of spreading of different diseases.

These nodes have important role in epidemiological investigations, because of the high risk of cross-infections.

Outputs: control plans

Dynamic properties

The results presented above all contribute to network analysis based risk based control plans.

The simplest approach is to observe the number of animals moving per month. The results indicated, that the trade becomes very active in June-July with a peak of activity at the end of the year.

The trend of increasing activity in the second half of the year seems to be stable. This should have an impact on the control time schedules, assigning increased control frequencies to those periods.

(1) Control plan aiming to control the epidemiological, hygiene, animal welfare, etc. properties (‘food chain safety control’), as defined by the Regulation 882/2004/EC

(2) Control plan aiming to control the requirements posed by Regulation 1760/2000/EC on animal identification (‘animal identification control’)

(3) Control plan for ‘critical infrastructures’ (critical from food security perspectives).

Analysing dynamic properties of different network measures

It can be derived from the results, that apart from small differences, the network characteristics are quite stable over time, allowing for predictions at the whole network level.

The analysis of the dynamic patterns is valuable especially in case of single holding analysis: performing time-dependent assessments, the results could be used for effective targeting of the control, or for prediction purposes as well.

We have selected top 5 nodes on the betweenness centrality rank list, and plotted the monthly betweenness centrality values, to see, when the nodes played central role in the network in the past 3 years.

The results let us see a very volatile nature of the holdings in relation to betweenness centrality values.

FOOD

CHAIN

Leonhard Euler

(1707-1793)

1967

Stanley

MILGRAM

In case of the letters reaching the addressee

the averagre path length was...

In 1967 asked 300 people from USA to send a letter to an unknown person via their friends.

... 5.5 steps!

Frigyes

KARINTHY

In 1929 he published a volume of short stories titled Everything is Different. One of these pieces was titled "Chains," or "Chain-Links." The story investigated in abstract, conceptual, and fictional terms many of the problems that would captivate future generations of mathematicians, sociologists, and physicists within the field of network theory.

Due to technological advances in communications and travel, friendship networks could grow larger and span greater distances. In particular, Karinthy believed that the modern world was 'shrinking' due to this ever-increasing connectedness of human beings. He posited that despite great physical distances between the globe's individuals, the growing density of human networks made the actual social distance far smaller.

As a result of this hypothesis, Karinthy's characters believed that any two individuals could be connected through at most five acquaintances. In his story, the characters create a game out of this notion.

Face

book

2016

Published on 4th February, 2016

Three and a half degrees of separation

Network of animal farms

Network of countries

Each person in the world (at least among the 1.59 billion people active on Facebook) is connected to every other person by an average of three and a half other people.

The average distance we observe is 4.57, corresponding to 3.57 intermediaries or "degrees of separation."

Within the US, people are connected to each other by an average of 3.46 degrees.

https://research.facebook.com/blog/three-and-a-half-degrees-of-separation/

Fight against terrorism –

Protecting the critical infrastructures

RASFF notifications

Which farm is the most probable place being a hub in future epidemies?

Network with consumers

New tool for

food chain safety

risk analysis

Network of businesses

  • Market basket analysis
  • Investigation of food-chain incidents
  • Loyalty cards?

From the aspect of food-chain control: global network

Risk analysis from critical infrastructure point of view

Usual food network

risk analysis

Which network science measures fit reality the most?

Traceability

Embedded networks!

'one step back –

one step forward'

No obligation for internal traceability nor for electronic databases, however there is potentially enormous amount of data to explore

Non-food epidemic outbreaks:

people-to-people

Food incidents:

food-to-people

unintentional

intentional

Our aim is to protect this network

but on the other hand, if we choose few major hubs and take them out of the network, it simply falls apart and is turned into a set of rather isolated graphs.

Scale-free networks: allows for a fault tolerant behaviour

The epidemiologic investigation has the most significant importance till the moment of the identification of the food causing the incident (e.g. German EHEC).

Export-import (trade)

Same food network

different outcomes for risk assessment

Are we able to draw the network at the point when we do not know exactly the nodes and links (i.e. source food)?

Is network science a helpful tool in those situations?

Which trade routes are the most risky?

Ercsey-Ravasz, M., Toroczkai, Z., Lakner, Z., & Baranyi, J. (2012). Complexity of the International Agro-Food Trade Network and Its Impact on Food Safety. (V. Colizza, Ed.)PLoS ONE, 7(5), e37810. doi:10.1371/journal.pone.0037810

Elaboration of spreading models

different vulnerable points

  • Find patterns
  • Observe sudden changes
  • Choose higher risk nodes or link

ARE NOT RANDOM

NETWORK

SCIENCE

YOURTITLE

Ákos Jóźwiak, NÉBIH

Albert-László

BARABÁSI

1999

Research on links of homepages: Internet constists of few large hubs and a lot of small periferic sites.

Modeling complex networks as random graphs

Party: random connections

JOZWIAKA@NEBIH.GOV.HU

The degree distribution of this network can be described with power law.

It is called a 'scale free' network.

GIANT Component

Every point can be reached from any point

Physicists: phase transition (e.g. water freezes)

Sociologists: a community is formed

Random graphs

  • Every new node is connecting to an old one with the same probability
  • Distrubution of the number of edges connecting to a node:

The scale-free name captures the lack of an internal scale, a consequence of the fact that nodes with widely different degrees coexist in the same network.

This feature distinguishes scale-free networks from lattices, in which all nodes have exactly the same degree, or from random networks, whose degrees vary in a narrow range.

This divergence is the origin of some of the most intriguing properties of scale-free networks, from their robustness to random failures to the anomalous spread of viruses.

  • In a random network most nodes have comparable degrees and hence hubs are forbidden.
  • Hubs are not only tolerated, but are expected in scale-free networks.
  • Furthermore, the more nodes a scale-free network has, the larger are its hubs. The size of the hubs grows polynomially with network size, hence they can grow quite large in scale-free networks.
  • In contrast in a random network the size of the largest node grows logarithmically or slower with N, implying that hubs will be tiny even in a very large random network.

NATURALNETWORKS

All follow power law distribution, and are very similar to each other

  • social networks
  • Internet
  • neural networks
  • epidemiological spreading routes
  • cellular biochemical reactions
  • ...

SCALE-FREEPROPERTIES

Scale-free networks have small world properties.

The low-degree nodes form dense subgraphs, inteconnected by large hubs.

Scale-free networks are very fault-tolerant towards random failures (randomly removing a node will have no significant effect on the network structure).

However, they are very sensitive to targeted attacks: removing the hubs will result in quick collapsing.

RANDOM

SCALE-FREE vs

Source: Albert-László Barabási: Network Science

THE KÖNIGSBERG

BRIDGE PROBLEM

The city of Königsberg in Prussia (now Kaliningrad, Russia) was set on both sides of the Pregel River, and included two large islands which were connected to each other and the mainland by seven bridges.

REAL NETWORKS

ARE DIFFERENT

Actually, yes:

Wilking H. et al.: Identifying Risk Factors for Shiga Toxin-producing Escherichia coli by Payment Information (2012)

SOLUTION

Euler proved that the problem has no solution.

  • The difficulty was the development of a technique of analysis and of subsequent tests that established this assertion with mathematical rigor.
  • The solution laid the foundations of graph theory.

evolve?

How graphs

GOAL

To devise a walk through the city that would cross each bridge once and only once, with the provisos that:

  • the islands could only be reached by the bridges
  • every bridge once accessed must be crossed to its other end
  • the starting and ending points of the walk need not be the same.

More and more pairs formed

optimization & planning at world level would be needed

Network mapping of food businesses

USA: small world

Pál Erdős

Alfréd Rényi

Inputs of the research

  • National Cattle Identification System and Database (ENAR)
  • able to follow the animals along the whole life cycle, from birth to slaughterhouse or from entering to the territory of Hungary to exporting
  • each movement record reports the unique identifier of the animal, the codes of the holdings of origin and destination, and the date of the movements

  • Cattle holding network: consists of a set of vertices (nodes) and a set of edges.

Nodes:

(1) cattle exporter countries

(2) the importers, buying living cattle from Hungary

(3) the various economic organizations (e.g. farms, slaughterhouses, logistic/distribution centers, markets, etc...)

Edges:

the cattle-flows between vertices

When the number of connections equals the number of people at the party, something special happens

"

Key players: FBOs

Poisson distribution

1929

but no world level, even not EU level optimization exists, just MS level

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