Avis 10-2016 du Comité scientifique de l'AFSCA...AVIS 10‐2016 Monitoring d’indicateurs de risque d’émergence des maladies animales 8/60 9 Increase in interactions between animal

AVIS 10‐2016 Monitoring d’indicateurs de risque d’émergence des maladies animales

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Appendix 1: Diagram describing the position over time of the different types of forecasting or early warning system of emergence of animal diseases Emergence is represented by the red line (increase in incidence). Early warning is the detection of new cases of disease in the early stages of emergence. The generic and specific forecasting is a monitoring of risk indicators before actual emergence. The specific forecasting already target diseases, unlike the generic forecasting that target a priori any disease.

Disease outbreak


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Appendix 2: Diagram describing four alert levels (generic or specific) decided by expert opinion based on the measurement of risk indicators


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Appendix 3: Initial list of risk indicators The initial list of risk indicators for this self‐task dossier is presented below. This list correspond to the final list of risk indicators in the advice 06‐2013 of the Scientific Committee.

Risk indicators

Indicators linked to the infectious agent

1 Genetic variability (mutation, recombination, etc.)

2 Lack of knowledge of pathogenesis

3 Change in pathogenesis (= change in development of the disease in the host) (e.g., increase in virulence, prolonged incubation period, variation in interaction host‐pathogen, new transmission pathway)

4 Difficulty to control disease by vaccination

5 Possibility to change the host spectrum from one animal species to another animal species (species barrier)

6 Possibility to cross the species barrier and to change the host spectrum from animals to humans (zoonotic potential)

7 Extension in geographical distribution of the agent

8 Increase in incidence (new cases) in another country(ies)

9 Persistence of the agent in the environment

Indicators linked to humans (human activity)

10 Legislation/sanitary policy

11 Change in technological or industrial processes

12 Problem of detection of emergence (e.g. difficult declaration of the disease by farmers, weak performance of diagnostic tests)

13 Increase in interactions between animal compartments (populations)

14 Increase in interactions between animal and human populations

15 Human demographic growth

16 Growth of the animal population concerned by the disease

17 Globalisation: increase in travel (humans)

18 Globalisation: increase in tourism (humans)

19 Globalisation: increase in trade (animals)

20 Globalisation: increase in transport (animals)

21 Globalisation: increase in terrorism

22 Intensive production system

23 Extensive production system

Indicators linked to animals

24 Carrier without clinical signs (asymptomatic)

25 Animal reservoir

26 Long incubation period in animals

27 Contact between domestic animals and wildlife

28 Epidemiological role of wildlife

29 Increase in demography and/or distribution of wildlife

Indicators linked to environmental changes

30 Changes in climate and meteorology

31 Changes in the ecosystems produced by man

32 Urbanisation

33 Vector


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Appendix 4: Inclusion criteria of risk indicators for a monitoring

Criteria Explanation

Weight of evidence The importance of the cause/effect link between the indicator and the emergence must be sufficient.

Anteriority The indicator must be prior to the real emergence of the disease or close after the real emergence, but before the identification and the confirmation of the emergence, i.e. until the limit of the early warning, such as for example an ''increase in mortality''.

Quantitative measurement

The indicator must be measurable in order to be analysed and placed in a graphic in order to see its evolution in time. The variability of the measurement must be known with a confidence interval of 95%, which allows to fix alert thresholds and to build an alert if the measurement leaves the 95% interval.

Availability of data The data allowing to analyse the indicator must be easily, for free or low‐cost available and easily measurable by the user.

Reliability of data The data must be correct (low uncertainty), recent and if possible available in real time. A real time measure allows to give the alert before a potential emergence, which increases the efficiency of prevention measures.

Aggregation The indicator can be combined with other indicators in order to better describe the risk of emergence in the framework of a multivariate analysis.

Space granularity The level of spatial detail of the data (e.g. data available at country, municipality, farm level) must allow it to detect the apparition and/or increase of incidence of the indicator.

Temporal granularity The level of temporal detail of data, i.e. the availability period of data (e.g. 1 / week, 1 / month) must allow it to detect the apparition and/or increase in incidence of the indicator.

Surrogate (Proxy) In case of lack of data or impossibility of measurement, having other data with the same influence on the emergence can add value.


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Appendix 5: List of risk indicators adapted to the purpose of the study An extended list of risk indicators is presented below. Some indicators have been added, modified combined or divided to respond to the objectives of the study.


1 Genetic variability (mutation, recombination, etc.)

2 Lack of knowledge of pathogenesis and/or epidemiology of the disease2

3 Change in pathogenesis (= change in development of the disease in the host, e.g.

increase in virulence, prolonged incubation period) or in epidemiology of the

disease (e.g. new transmission pathway) 2

4 Difficulty(ies) to control the disease by vaccination

5 Possibility to cross the species barrier (from one animal species to another animal

species)

6 Possibility to cross the species barrier (from one animal species to humans) or

zoonotic potential

7 Extension in geographical distribution of the infectious agent in animals and/or

humans2

8 Increase in incidence (new cases in animals and/or humans) in another country(ies)2

9 Persistence of the infectious agent in the environment


10 Legislation/sanitary policy

11 Change in technological or industrial processes

12 Change in the number of notifications2

13 Change in the number of analyses in routine vet laboratories1, 3

14 Increase in interactions between animal compartments (populations)

15 Increase in interactions between animal and human populations

16 Human demographic growth or change2

17 Animal demographic growth or change (ex. distribution, density)2

18 Increase or change in human movements2

19 Increase or change in animal movements2

20 Change in the geo‐political situation2,4

21 Economical crisis1

22 Changes in the (intensive/extensive) production systems2

23 Changes in the animal markets (prices)1

24 Increase in consumption of drugs in a foreign country1, 5


25 Increase in animal mortality rate1

26 Asymptomatic carriage

27 Animal reservoir

28 Long incubation period in animals

29 Modifications of the contact between domestic animals and wildlife2

30 Epidemiological role of wildlife

31 Increase or change in demography and/or distribution of wildlife2



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32 Changes in climate and meteorology

33 Changes in the ecosystems produced by man

34 Urbanisation

35 Changes in the vector populations (presence / number)2

1 Added indicators 2 Modified/combined/divided indicators 3 In case of increase in demands of analyses, there can be suspicion of emergence 4The indicator « change in the geo‐political situation » replaces the indicator « increase in terrorism ». This

indicator is linked to the effectiveness of the veterinary services : unstability in a country leads to a decrease in

the sanitary precaution. There exist data on geo‐political profiles of countries. 5 An increase in consumption of drugs in a foreign country can reveal a risk of emergence for Belgium (ex. Tamiflu).


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Appendix 6: List of risk indicators excluded and justification The list of excluded risk indicators is presented below with the main justifications: 1 Not quantitatively measurable indicator 2 The preliminary knowledge of the disease at risk of emergence is necessary 3 Not an operational indicator (indicators of which the monitoring will not allow it to identify in a pragmatic or operational way an increasing risk of

emergence) 4 Insufficient weight of evidence 5 Insufficient data 6 Ambiguity (indicator which can, according to the interpretation, increase or decrease the risk of emergence) 7 Stability of the indicator with as consequence a weak sensitivity to variations 8 Indicator linked to another indicator included in the list Excluded risk indicators Main justifications


1 Lack of knowledge of pathogenesis and/or epidemiologyof the

disease

1, 2, 3, 4

2 Difficulty(ies) to control the disease by vaccination 2

3 Possibility to cross the species barrier (from one animal species

to another animal species)

1, 2

4 Possibility to cross the species barrier (from one animal species

to humans) or zoonotic potential

1, 2

5 Persistence of the infectious agent in the environment 1, 2, 3


6 Legislation/sanitary policy 1

7 Change in technological or industrial processes 1

3 6 according to the process, a change can increase or decrease the risk of

emergence

8 Change in the number of notifications 6 an increase in the number of notifications can be the signal of two opposed

things : an increase in vigilance or an alert of emergence. A decrease in the

number of declaration can also signify two opposed things : a better sanitary

situation, or an underreporting, i.e. a late detection of emergences


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9 Increase in interactions between animal compartments

(populations)

3

5 EMPRES can be a source of data on animal layers at the herd level but the

analysis of these data is of the research domain 7 These interactions are very stable in Belgium

10 Increase in interactions between animal and human populations 1,3,4,5

7 These interactions are very stable in Belgium

11 Human demographic growth or change 3,4

7 Eurostat en Statbel give annual data from more than one year before. It

concerns large trends in the time. The evolutions are very slow (10 years). The

relevance on the detection of emergences on the short term is low.

12 Animal demographic growth or change (ex. distribution, density) 3,4

7 Sanitel gives continuous data and the GIS system gives distribution maps of

herds but the evolutions are slow. The sensitivity of the measure is weak.

13 Change in the geo‐political situation

3

7 When geo‐political changes occur in a country, the European commission

sufficiently manage it at european level to guarantee the stability of the

sanitary measures. The FVO guarantees an adequate veterinary sevice. This is

communicated to all the european MS via the SCOFCAH

14 Economical crisis

3

7 idem than for the indicator « change in the geo‐political situation »

15 Changes in the (intensive/extensive) production systems 1, 3, 6 for ex., intensive production system scan decrease the risk of introduction of

diseases into the herds but can also favor the dispersion of contagious diseases

(high density) 7 Theses changes occur very slowly in Belgium. Their impact on the emergence

of animal diseases has to be considered on the (very) long term. These changes

have the time to be taken into consideration at the FASFC via adaptations of

the legislation. This system is stable.

16 Changes in the animal markets (prices) 8 These changes have an impact on the importations and exportations, which

are included in the indicator « increase or change in animal movements »

17 Increase of sale/consumption of veterinary drugs in Belgium and 5 no real time data (only data from annual reports)


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abroad


18 Asymptomatic carriage 1, 2, 3

19 Animal reservoir 1, 2, 3

20 Long incubation period in animals 1, 2, 3

21 Modifications of the contact between domestic animals and

wildlife

1, 3

22 Epidemiological role of wildlife 1, 2, 3


23 Changes in the ecosystems produced by man 1, 3, 6, 7

24 Urbanisation 1, 3, 6, 7


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Appendix 7: Indicators to be included in the monitoring system The remaining risk indicators are shown in column A. The cause‐effect relationship between the indicator and the emergence of animal diseases is described in column B. Column C describes the data sources to operate a monitoring.

Indicators Cause‐effect relationship between the indicator and the emergence of animal diseases

Data sources


1 Changes in pathogenic agents related to their molecular biology (e.g. genetic variability), their pathogenesis (e.g. increase of virulence, acquisition of antibiotic resistance), their epidemiology (e.g. new transmission pathway)

A mutation can lead to the emergence of new pathogen agents; an increase in virulence may favor the transmission of diseases and promote their dispersion ; etc.

Scientific littérature (for example, PubMED)

2 Extension of the geographical distribution of the infectious agent or of the disease in animals and/or humans

The geographical spread of a disease in a neighboring country may allow it to enter the Belgian territory. The expansion can also occur in Belgium (see definition of emergence)

ProMED, ADNS and WAHID (OIE) report the cases of disease that occurred abroad (and also in Belgium) and also the geographical distribution of these cases.

3 Increase of the incidence (new cases in animals and/or humans) in (an)other country(ies)

The increase in incidence of disease in other countries increase the risk of introduction of these diseases in Belgium

ProMED, ADNS, WAHID (OIE) and the health alerts of the OIE report the cases of disease that occurred abroad (and also in Belgium)


4 Change of numbers of generic analyses carried out by labs

An increase in the number of analyzes is a signal of an ongoing emergence (early warning).

Data are available and usable via the LIMS system of the FASFC. The data available in universities (University of Ghent and Liege) are. The private data from laboratories are not available

5 Increase or modification of international movements of persons

An increase in movement of people from one country can increase the risk of introducing zoonotic diseases, disease vectors , infected

Eurostat provides all data on tourism (eg. number of trips, by purpose , by means of transport, ...), but on the basis of retrospective


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animal products. The most at‐risk movements are illegal movements, impossible to count .

annual reports, with the risk of being late relative to an emergence Air traffic data

6 Increase or modification of animal movements (e.g. business exchanges)

Increased trade/imports of live animals to Belgium increases the risk of disease introduction into Belgium from some countries. European legislation concerning imports is such that 90 % of imports are determined by trade agreements and authorizations and that only 10% are variable depending on price, needs, alerts. There are already many systems set up by the EC to reduce import risks and protect against the emergence (introduction) of diseases. Imports are authorized only from regions / countries free from certain diseases. The EC has the ability to block imports in case of disease in a country. Despite this, a monitoring of trade/import to Belgium remains relevant for the following reasons : ‐ Cases of trade/imports from a country/region with a not detected or not yet known disease, doing therefore not subject to trade restrictions; ‐ Case of insidious disease that are not always detected rapidly (eg BSE ). ‐ Cases of trade/import from a country/region close to a country where there are cases of disease (contact proxi).

The TRACES database available at the FASFC provides all information related to trade and international trade in live animals and animal products (number of imported animals by species, year, month, countries of origin, etc. ) to and from Belgium


7 Increase of mortality rate of animals An increase in the number of deaths is a signal of Rendac (Belgian data for all species of rent


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an ongoing emergence (early warning ) . animals)

8 Increase or change of demography and/or distribution of wildlife

An increase in the population of wildlife may favor the spread of diseases in the territory

Data exist at regional level (drones and IR camera, shooting plans) but they are few precise and only available on annual basis


9 Historical climatic and meteorological changes on medium (previous months) and long (previous years) term

The long‐term climate changes (several years) influence the behavior and survival of vectors and pathogens. The weather changes in the medium term (several months) condition the survival of vectors (eg, a warm winter favors the survival of vectors with vectors explosion during the summer and risk of emergence of vector‐borne diseases )

Royal Meteorological Institute (temperature and precipitation data, monthly, seasonal, annual)

10

Changes in vector populations (presence/number)

The introduction of a new vector species at a port/airport can be the starting point of an emergence of a vector‐borne disease if the carrier establish in Belgium. An increase of vector population may favor the emergence of vector‐borne diseases

Until 2012, a monitoring of Culicoides was made in Belgium by ITG based on 20 traps established in Belgium Since 2012, ITG performs a risk‐based monitoring of all kinds of vectors in three ports or airports (Antwerp , Ostend, Liege). Not relevant for detecting a population increase, but makes it possible to detect the introduction of a new vector species in Belgium The ITG and the Belgian army monitor for several years ticks and tick infections by parasites and bacteria


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Appendix 8: The 4 risk indicators selected for the feasibility and efficiency analysis and associated data sources Indicator for the generic

forecasting Data sources Type

1 Increase or modification of commercial exchanges of animals

Traces Risk indicator of emergence

Indicators for the specific forecasting

2 Extension of the geographical distribution of the infectious agent or of the disease in animals and/or humans

ProMed ADNS WAHID

Risk indicator of emergence

3 Increase of the incidence (new cases in animals and/or humans) in (an)other country(ies)

ProMed ADNS WAHID and health alerts of the OIE

Risk indicator of emergence

Early warning indicator

4 Increase of mortality rate of animals

Rendac Indicator of emergence


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Appendix 9: Retrospective analysis of trade and import data to Belgium in live animals and animal products For the retrospective analysis, the following data were used: ‐ January 2010 until 20 April 2015 (not before 2010 because there have been changes in customs codes), ‐ Per month, ‐ By country, ‐ Number of live animals and number of animal products o Live animals

‐ 0101 horses (total number of animals) ‐ 0102 cattle (total number of animals) ‐ 0103 pigs (total number of animals ‐ not lots) ‐ 0104 10 sheep (total number of animals) ‐ 0104 20 goats (total number of animals) ‐ 0105 domestic poultry (total number of animals ‐ not lots) ‐ 010 614 rabbits (total number of animals) ‐ 01061900 others (dogs, cats, ferrets) (total number of animals)

o Animal products: trade and imports ‐ 051110 bovine semen (straws) ‐ 05119986 sperm (straws) from horses, pigs and goats, embryos (units) and eggs (units) from horses, goats, pigs, cattle

‐ Exchanged from Member States and imported from third countries towards Belgium. Postal codes from the countries of origin and of destination in Belgium, and data about the "Local Veterinary Unit" (provinces, departments, Länder) are included in the BO request so that they are available if needed for the risk assessment, but are not included in the Excel reports not to complicate them. Below, some examples are given of import and trade of animal species to Belgium.


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Appendix 10: BO request in Tralink


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Appendix 11: Retrospective quantitative monitoring of trade/import data Below the results are presented obtained with a model combining different abnormalities detection

algorithms (Holtwinters, EWMA ...). These examples concern imports of horses into Belgium and

exchanges into Belgium of several animal species. Retrospective data from TRACES between 2010

and 2015 have been used to create a baseline that defines a normal import threshold (red dotted

line). The black line represents the actual values of imports/exchanges. Exceeding the baseline with

the actual data in the context of a monitoring can create the emission of warning signals represented

by boxes (red boxes – Holtwinters algorithm / green boxes ‐ EWMA algorithm / blue boxes ‐

Shewhart algorithm) depending on the considered algorithm.

Horses


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Cattle


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Sheep/goats


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Pigs


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Poultry


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Pets


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Appendix 12: Prospective quantitative monitoring of trade/import data Below are presented the results obtained with a model using the HoltWinters and the Predict

algorithms. These examples concern imports of horses into Belgium and exchanges into Belgium of

horses and cattle. Retrospective data from TRACES between 2010 and 2015 have been used to create

a baseline that defines a normal import threshold (red baseline). Predictions of the baseline for 2016

(red line) were made with a 95% confidence interval (blue lines: upper and lower limits of the

confidence interval). The black line represents the actual values of imports/exchanges. Current

values of import/exchanges can be plotted on the prediction curves. Exceeding the upper threshold

by the actual data in the context of a monitoring can create the emission of warning signals.

EU exchanges of cattle


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Importations et EU exchanges of horses


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Appendix 13: Retrospective analysis of mortality data of different farm animal species in Belgium The following data were used for the retrospective analysis: ‐ for 2013 and 2014, ‐ number of dead cattle (calves and cattle of different weight classes, runts), horses (foals, adults), swine (piglets, breeding pigs, fattening pigs, barrels of 100 kg of runts or "sow waste"), small ruminants (lambs, adults), poultry (barrels of 100 kg) collected by the rendering firm. For cattle, data for each individual dead animal are available. For the other species, these individual data are not available, but well data concerning the holdings and the number of dead animals collected daily in the holding; ‐ daily (with date of collection), weekly, monthly or yearly, ‐ per holding, with the zipcode and the commune of the holding. The categorization of these animals is based on the visual assessment of the weight by the driver (no weighing). In the barrels, there may be other things than runts or poultry. Based on the data, mortality graphs were realized by species and weight category. Below are shown some examples of mortality graphs.


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Appendix 14: Quantitative analysis of mortality data Below are presented the results obtained with a model combining different abnormalities detection

algorithms (Holtwinters (Burkom et al., 2007; Elbert and Burkom, 2009), EWMA (Woodall, 2006), ...).

This example shows mortality rate in sheep/goats during the Bluetongue outbreak in 2007. The red

dotted line represents the normal mortality baseline. This baseline was set up based on retrospective

mortality data of sheep/goats from Rendac in 2013 and 2014. The black line represents the actual

values of mortality in 2007. The graph shows that it was possible to detect mortality threshold

crossings (crossing of the red dotted line by the black line) and to detect a signal of abnormal

mortality (red boxes – Holtwinters algorithm / green boxes ‐ EWMA algorithm / blue boxes ‐

Shewhart algorithm) in april and in august/september 2007. A detection of abnormal mortality at

that time could have helped to early detect (early warning) the presence of a disease in sheep/goats.


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Appendix 15: Prospective quantitative monitoring of mortality data Below are presented the results obtained with a model using the HoltWinters and the Predict

algorithms. These examples concern mortality of cattle/pigs by age categories (Bovine 150 kg, Bovine

300 kg, Bovine 550 kg, Bovine 800 kg, or in total (Holt‐Winters on Bovine, Holt‐Winters on Suidae).

Retrospective data from Rendac have been used to create a baseline that defines a normal import

threshold (red baseline) (De Vries and Reneau, 2010; Perrin et al., 2012). Predictions of the beginning

of 2015 (red line) were made with a 95% confidence interval (blue lines: upper and lower limits of the

confidence interval). The black line represents the actual values of mortality. Current actual values of

mortality can be plotted on the prediction curves (see Holt‐Winters on Bovine, Holt‐Winters on

Suidae). Exceeding the upper threshold by the actual data in the context of a monitoring can create

the emission of warning signals of increased risk. On these two graphs (Holt‐Winters on Bovine, Holt‐

Winters on Suidae), the crossings of the lower thresholds correspond to a punctual decrease in the

number of harvested animals by the rendering company due to circumstances (Christmas and New

Year).

Holt‐Winters and prediction algorithms on separate bovine rendering data


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Holt‐Winters and prediction algorithm on total amount of rendered Bovine and Suidae


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Appendix 16: Retrospective analysis of incidence and geographical expansion data of diseases in the world and in Europe

Data on incidence and on geographical expansion of animal diseases in the world and in Europe are found in WAHID (World Animal Health Information

Database) and in the ADNS (Animal Disease Notification System) of the EC

1. WAHID

For the risk indicator "Increased incidence in other countries", monthly analysis are shown based on the data given in the immediate notifications to the OIE

and in the OIE reports: either total cases (from January 1, 2014 to April 17, 2015) or number of outbreaks (from January 1, 2014 to April 17, 2015). The

following diseases were analysed : Equine Infectious Anemia, Highly pathogenic avian influenza, Bluetongue, African horse sickness, African swine fever,

Bovine tuberculosis and Bovine Brucellosis. Only the example of the highly pathogenic avian influenza is shown here.


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Total number of cases per month and per country, with totals

Numbre of cases of highly pathogenic avian influenza

janv. févr. mars avr. mai juin juil. août sept. oct. nov. déc. janv. févr. mars avr.

2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2015 2015 2015 2015

Allemagne 0 0 0 0 0 0 0 0 0 0 5.001 4.091 9 0 0 0 9.101

Bhoutan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 16

Bulgarie 0 0 0 0 0 0 0 0 0 0 0 0 23 2 21 0 46

Burkina Faso 0 0 0 0 0 0 0 0 0 0 0 0 0 1.366 114.028 0 115.394

Cambodge 0 4.959 301 0 0 0 0 0 0 0 0 0 0 0 0 0 5.260

Canada 0 0 0 0 0 0 0 0 0 0 8.400 694 0 94 0 9.000 18.188Chine (Rép. pop.

de)6.705 3.629 29.600 1 0 0 0 20.550 61 0 0 0 10.665 3.402 260 0 74.873

Corée (Rép. de) 6.917 11.174 112 0 0 0 25 0 1.200 6 128 0 30 0 0 0 19.592Corée (Rép. pop.

dém. de)0 … 169.771 501 0 0 0 0 0 0 0 0 0 0 0 0 170.272

Etats-Unis d'Amérique

0 0 0 0 0 0 0 0 0 0 0 65 100 9 0 0 174

Hong Kong (RAS - RPC)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

Hongrie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Inde 2 0 0 0 0 0 0 0 0 0 18.554 22 1.628 0 190 0 20.396

Israël 0 0 0 0 0 0 0 0 0 0 0 0 51.401 1.000 0 0 52.401

Italie 0 0 0 0 0 0 0 0 0 0 0 1.982 0 0 0 0 1.982

Japon 0 0 0 1.100 0 0 0 0 0 0 4 81 25 1 0 0 1.211

Laos 0 0 457 0 0 0 0 0 0 0 0 0 0 0 0 0 457

Libye 0 0 19 0 0 0 0 0 0 0 0 0 0 11 0 0 30

Mexique 0 0 0 0 0 0 0 0 0 0 0 0 0 0 139 0 139

Myanmar 0 0 0 0 0 0 0 0 0 0 0 0 0 350 0 0 350

Népal 0 570 0 0 0 0 0 0 0 0 0 0 0 0 0 0 570

Nigeria 0 0 0 0 0 0 0 0 0 0 0 3.300 24.496 6.739 0 0 34.535

Pays-Bas 0 0 0 0 0 0 0 0 0 0 1.725 0 0 0 0 0 1.725

Roumanie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 64 0 64

Royaume-Uni 0 0 0 0 0 0 0 0 0 0 803 0 0 0 0 0 803

Russie 0 0 0 0 0 0 0 0 441 0 0 0 0 0 0 0 441

Suède 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2

Taipei chinois 0 0 0 2 0 0 0 0 0 0 0 0 647.328 319.444 44.066 590 1.011.430Territoires auto.

palestiniens0 0 0 0 0 0 0 0 0 0 0 0 22.400 4.200 1.225 90 27.915

Vietnam 3.895 37.006 23.136 40 0 950 0 1.048 3.000 0 160 3.792 260 100 1.953 650 75.990

Totaux 17.519 57.338 223.396 1.644 0 950 25 21.598 4.702 6 34.775 14.027 758.365 336.720 161.946 10.347 1.643.358

Pays Totaux


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Total number of new outbreaks per month and per country, with totals

Numbre of outbreaks of highly pathogenic avian influenzajanv. févr. mars avr. mai juin juil. août sept. oct. nov. déc. janv. févr. mars avr.

2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2015 2015 2015 2015

Allemagne 0 0 0 0 0 0 0 0 0 0 2 3 3 0 0 0 8

Bhoutan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

Bulgarie 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 0 4

Burkina Faso 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 4

Cambodge 0 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 5

Canada 0 0 0 0 0 0 0 0 0 0 2 10 0 1 0 1 14

Chine (Rép. pop. de)

2 1 1 1 0 0 0 1 51 0 0 0 5 2 1 0 65

Corée (Rép. de) 11 14 21 4 4 3 1 0 1 2 2 1 5 0 0 0 69

Corée (Rép. pop. dém. de)

0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 3

Etats-Unis d'Amérique

0 0 0 0 0 0 0 0 0 0 0 6 20 17 20 14 77

Hong Kong (RAS - RPC)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

Hongrie 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1

Inde 1 0 0 0 0 0 0 0 0 0 4 1 1 0 3 0 10

Israël 0 0 0 0 0 0 0 0 0 0 0 0 7 1 0 0 8

Italie 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1

Japon 0 0 0 1 0 0 0 0 0 0 4 7 4 1 0 0 17

Laos 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1

Libye 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 2

Mexique 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2

Myanmar 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 3

Népal 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

Nigeria 0 0 0 0 0 0 0 0 0 0 0 1 35 11 0 0 47

Pays-Bas 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 5

Roumanie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1

Royaume-Uni 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1

Russie 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 3

Suède 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2

Taipei chinois 0 0 0 1 0 0 0 0 0 0 0 0 631 136 15 1 784

Territoires auto. palestiniens

0 0 0 0 0 0 0 0 0 0 0 0 2 1 2 1 6

Vietnam 4 24 10 1 0 1 0 3 2 0 1 3 1 1 2 2 55

Totaux 18 44 37 9 4 4 1 4 57 2 21 33 716 181 49 21 1.201

Pays Totaux


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For the risk indicator " Geographical expansion ", monthly disease outbreaks and disease distribution maps are shown for highly pathogenic avian influenza

(from June 1, 2014 to June 1, 2015).

Below are presented disease outbreaks maps and disease distribution maps for highly pathogenic avian influenza from January 2014 to april 2015. On the

disease outbreaks maps, the resolved outbreaks are indicated in blue and the ongoing outbreaks are indicated in red. On the disease distribution maps, the

countries where cases of disease are reported, indicated in red.

Disease outbreaks maps Disease distribution maps

January 2014


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February 2014

March 2014


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April 2014

May 2014


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June 2014

July 2014


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August 2014

September 2014


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October 2014

November 2014


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December 2014

January 2015


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February 2015

March 2015


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April 2015 (until 17 april)


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2. ADNS

For the risk indicator "Increased incidence in other countries", daily data based on notifications to

the ADNS can be extracted. The example below concern African swine fever.

Table. Number of cases of disease per country per day in European eastern countries from

01/08/2014 to 31/01/2015.

Country Confirmation dateFattening pigs Piglets Breeding Pigs29 LATVIA 01/08/2014 21 1 2029 LATVIA 01/08/2014 1 1 029 LATVIA 01/08/2014 3 3 229 LATVIA 01/08/2014 2 2 029 LATVIA 01/08/2014 2 2 129 LATVIA 02/08/2014 11 4 1029 LATVIA 03/07/2014 2 2 229 LATVIA 05/08/2014 196 9 18129 LATVIA 06/08/2014 2 1 130 LITHUANIA 06/08/2014 2 1 125 POLAND 08/08/2014 1 1 129 LATVIA 11/07/2014 3 2 329 LATVIA 11/08/2014 2 2 029 LATVIA 12/07/2014 2 2 229 LATVIA 12/08/2014 23 1 2229 LATVIA 13/07/2014 41 130 LITHUANIA 13/08/2014 2 2 130 LITHUANIA 13/08/2014 1 1 029 LATVIA 16/07/2014 7 1 629 LATVIA 17/07/2014 48 1 4729 LATVIA 17/07/2014 2 1 229 LATVIA 17/09/2014 43 6 4329 LATVIA 18/07/2014 56 4 029 LATVIA 19/07/2014 4 2 329 LATVIA 19/07/2014 17 1 1629 LATVIA 22/07/2014 2 2 229 LATVIA 22/07/2014 2 2 130 LITHUANIA 22/08/2014 2 1 025 POLAND 23/07/2014 8 5 330 LITHUANIA 24/07/2014 19411 1941129 LATVIA 25/07/2014 9 1 029 LATVIA 25/08/2014 1 1 029 LATVIA 25/08/2014 38 1 3729 LATVIA 26/06/2014 3 3 329 LATVIA 29/07/2014 15 1 029 LATVIA 29/07/2014 2 2 229 LATVIA 29/07/2014 4 1 329 LATVIA 29/07/2014 2 1 129 LATVIA 29/08/2014 19 5 1730 LITHUANIA 31/08/2014 4 4 225 POLAND 31/01/2015 7 5 7


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Étiquettes de lignes Somme de Fattening pigs25 POLAND 16

08/08/2014 123/07/2014 831/01/2015 7

29 LATVIA 58501/08/2014 2902/08/2014 1103/07/2014 205/08/2014 19606/08/2014 211/07/2014 311/08/2014 212/07/2014 212/08/2014 2313/07/2014 4116/07/2014 717/07/2014 5017/09/2014 4318/07/2014 5619/07/2014 2122/07/2014 425/07/2014 925/08/2014 3926/06/2014 329/07/2014 2329/08/2014 19

30 LITHUANIA 1942206/08/2014 213/08/2014 322/08/2014 224/07/2014 1941131/08/2014 4

Total général 20023

0

100

200

300

400

500

600

700

800

900

1000

08/08/2014

01/08/2014

05/08/2014

11/08/2014

13/07/2014

17/09/2014

22/07/2014

26/06/2014

06/08/2014

24/07/2014

25 POLAND 29 LATVIA 30 LITHUANIA

Total

Total

Étiquettes de lignes Nombre de Breeding Pigs25 POLAND 3

08/08/2014 123/07/2014 131/01/2015 1

29 LATVIA 3101/08/2014 502/08/2014 103/07/2014 105/08/2014 106/08/2014 111/07/2014 111/08/2014 112/07/2014 112/08/2014 113/07/201416/07/2014 117/07/2014 217/09/2014 118/07/2014 119/07/2014 222/07/2014 225/07/2014 125/08/2014 226/06/2014 129/07/2014 429/08/2014 1

30 LITHUANIA 506/08/2014 113/08/2014 222/08/2014 124/07/201431/08/2014 1

Total général 39

0

1

2

3

4

5

6

08/08/2014

01/08/2014

05/08/2014

11/08/2014

13/07/2014

17/09/2014

22/07/2014

26/06/2014

06/08/2014

24/07/2014

25 POLAND 29 LATVIA 30 LITHUANIA

Total

Total


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For the risk indicator " Geographical expansion ", disease distribution maps based on notifications at

European level can be extracted. Below are presented disease distribution maps for African swine

fever between June 2014 to may 2015.

June 2014

July 2014

August 2014

September 2014


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October 2014

November 2014

December 2014

January 2015


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Februari 2015

March 2015

April 2015

May 2015


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Appendix 17: Logigrams In the examples below, three flowcharts are presented according to their point of entry, which represents the data source being the start of monitoring (TRACES – logigram 1; Rendac – logigram 2; WAHID, ProMED, ADNS – logigram 3). The combination of flowcharts shows how the flowcharts can be logically combined.

Logigram 1. A complete analysis of import and exchanges trends towards Belgium via the TRACES database must allow to detect thresholds crossings of imports/exchanges of certain animal species towards Belgium and to identify the countries of origin. If the threshold is exceeded for a species and a country, a pre‐alert signal is issued and a risk assessment should be performed. As the monitoring of TRACES is generic, i.e. the disease at risk of emergence is not yet known, this risk assessment should include a secondary analysis of the other indicators to gradually target the disease at risk of emergence. For example, the health situation in the species concerned by the increasing exchange/import can be assessed in the country(ies) of origin via the analysis of the indicators "incidence in another country and geographic expansion" in the data sources WAHID, ADNS and ProMED. An evaluation of the risk of introduction in Belgium of diseases present in that country of origin may lead to the issue of an alert. A strengthening of the surveillance and controls at importation can be recommended. If the health situation in the country of origin is favorable, the risk assessment stops and starts again the next month in the periodic monitoring. In parallel, an analysis of mortality data can be conducted to detect an increase in mortality in the concerned species, which may also lead to an alert.


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Logigram 2. An as “real‐time” as possible analysis of the mortality data in the various animal species should allow to early detect an increased mortality in a particular species. If the threshold is crossed in an animal species, a pre‐ alert should be issued and a risk assessment should be performed. As the monitoring of the mortality is generic, i.e. the disease causing the increased mortality is not yet known, this risk assessment should include a secondary analysis of the other indicators to gradually identify the emerging disease. For example, an analysis of TRACES must determine whether an increase in trade/import of the animal species concerned by the increased mortality has occurred, and if so, from which country. If this is the case, it is possible that the cause of the increased mortality is situated in this country of origin. To find it out, an analysis of the health situation in the country(ies) of origin of the diseases of the concerned species has to be performed via WAHID, ProMED and ADNS.


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Logigram 3. A complete and systematic analysis of the risk indicators "increase in incidence " and "geographical expansion " for all the diseases (or of a predetermined list of diseases) in all countries of the world (or in European countries) with WAHID, ADNS or ProMED should allow to detect outbreaks/emergences in the world. If the threshold is crossed for a disease in a species in a particular country, a pre‐alert signal should be issued and a risk assessment must be performed. As this is specific monitoring, the disease at risk of emergence risk is already known. However, this risk assessment can still include the analysis of the other indicators. The analysis of TRACES must allow to detect if there has been an increase in trade / import from the concerned country, of the animal species concerned by the disease. If so, an alert must be issued and recommendations for the risk manager should be formulated. In parallel, the analysis of mortality data should allow to early detect a potential increase in mortality in the concerned animal species. If so, an alert should be issued. ProMED should not be neglected because it has the advantage of providing information on the non‐notifiable diseases.


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Combination of the 3 logigrams

This scheme shows how the monitoring of the different indicators can be combined and interconnected. In blue: logigram with the analysis of TRACES data as point of entry (logigram 1). In orange: logigram with the analysis of Rendac data as point of entry (logigram 2). In green: logigram with the analysis of the data of WAHID/ProMED/ADNS as point of entry (logigram 3).


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Appendix 18: Practical example of flowchart combining 3 risk indicators This example illustrates the logigram 1.

Legend table WAHID x: disease present in 2014/2015, 0: disease not reported in 2014/2015, never: disease never reported in the country, /: no information

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