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Chapter 22 Qualitative GIS-based risk assessment of avian influenza introduction and spread in domesticated poultry in EU-27 regions B.J. Grabkowsky, P.L. Geenen, H.W. Saatkamp, H.-W. Windhorst Abstract Avian influenza (AI) risk maps may provide support to decision makers in the development of region specific strategies for AI control. In this study, a first approach to classify regions of the 27 European Union member states (EU-27) based on expert knowledge and the spatial distribution of AI introduction and spread risk factors is presented. Many challenges were faced particularly with respect to data quality and data availability. It was found that large differences with respect to AI risk factors in the various regions of the EU-27 exist and that with basic and comprehensible GIS methods these data can be combined into risk maps. Based on the resulting risk maps, careful conclusions can be made for some regions, whereas for others classification is not yet possible as essential data is lacking. In order to improve the results of future GIS-based risk assessment studies in the EU-27, more attention should be given to the collection of specific AI related risk factors, to harmonization of data collection, to disclosure of data for research purposes and to adapted sensitivity analysis. 1. Introduction Risk assessment is an essential decision-support tool in animal disease policy making in the European Union (EU). In the case of avian influenza (AI), several risk assessments studies were initiated due to the pressing situation in Asia, e.g. EFSA (2005) and Sabiroviz et al. (2007). AI risk assessment is not an easy task as the epidemiology of AI is highly complex and there are many uncertainties (Sabiroviz et al., 2007). Moreover, poultry production differs considerably in the EU-27 (Van Horne, this report). Within the EU-27, EU legislation sets the minimum measures for AI control. Additional measures adopted by a member state should be based on risk assessment of the local epidemiological situation (Pittman and Laddomada, 2008). A prerequisite to develop region specific strategies is to classify regions based on their (relative) risk. Distribution of AI risk factors can be presented in single risk factor maps using a geographical information system (GIS). However, for decision support the data underlying these maps should be integrated into so-called risk maps. To produce risk maps, data of various sources should be combined using algorithms and weights that link the different types of input data. In practice, these weights are often not available; instead quantitative or qualitative information from the literature or estimates provided by experts can be used (Pfeiffer, 2004). In this chapter, a first approach of a GIS-based qualitative regional risk assessment of AI introduction and spread in domesticated poultry of the EU is described. In this study, we aim to distinguish EU regions with a relatively high risk of AI introduction and spread from regions with a relatively low risk. The risk assessment builds on an explorative study on AI risk factors as obtained from the literature and expert opinion (Geenen et al., this report). The AI risk factors identified were used to focus data collection, and ratings given by the experts were used as weights. The following steps were taken: 1) Selection of risk factors, 2) Data collection and preparation, 3) Visualization of the data in single risk factor maps, 4) Construction of algorithms to combine single risk maps into AI risk maps, 5) Construction of

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an AI overall risk map. We will end this paper with a discussion on data availability, methods used and the use of the resulting risk maps for decision support. 2. Materials and methods 2.1. Study design The general design of the study was as follows. During a preceding three-stage Delphi study, risk factors for AI introduction and spread at regional level were rated by an international expert panel (see Geenen et al., this report). Numeric and coordinate based regional data on the risk factors was obtained from various sources in the EU-27 and, based on the rates given by the experts and the availability of data, a final subset of 13 main risk factors was selected. The data collected for each risk factor were standardized into scores between 0-1 based on the maximum and minimum values found in the EU-27 regions. Using these scores, 13 single risk factor maps of the EU-27 were produced. All single risk factor maps were converted to raster maps using a grid size of 100x100 m. A weighted sum algorithm with weights obtained from the Delphi study was applied to the grids to combine the single risk factor maps into final risk maps for introduction and spread. Finally, an overall AI risk map was produced. A more detailed description of the several steps can be found in subsections 2.2-2.6. 2.2. Selection of risk factors In the Delphi study, an international expert panel rated the importance of several risk factors related to AI introduction and secondary spread by dividing 100 points over sets of risk factors. The risk factors were organized in classes related to: 1) Routes of introduction, 2) Routes of spread, and 3) Poultry demographics. For the current study, the third class was split up in 3) poultry demographics related to introduction and 4) poultry demographics related to spread. The classes of Introduction-Routes and Spread-Routes were subdivided in eight and five categories, respectively. The four classes contained 89 risk factors in total. Given this high number of factors, a selection of the main risk factors was deemed necessary to focus data collection. Main risk factors were selected by ranking the factors from high to low points and next by summing the points until 66 points or more per category were reached. These factors were kept, which resulted in a total of 41 risk factors. However, for many of these main factors, no data were found (e.g. illegal trade, poultry workers). Based on data availability, 13 main factors remained, which represented 31.5%, 65.0%, 30.3% and 69.8% of the points originally given by the expert panel for the classes Introduction-Routes, Introduction-Demographics, Spread-Routes and Spread-Demographics respectively, see Table 1. As can be seen from Table 1, for many risk factors proxy factors were used. In some cases no data could be found on the original Delphi study risk factor, but an acceptable proxy was available (e.g. wintering birds). Moreover, bird and farm numbers were changed in bird and farm densities as regions of varying sizes have to be compared and densities are assumed to be better risk factors. In the case of Introduction-Demographics risk factors were even changed to bird densities, as each bird in a region has a certain (very low) risk to become infected with AI, and thus the risk of introduction is better reflected by bird densities than farm densities. However, the relation between bird density and risk of introduction is most likely not linear; adding extra birds to a region with a low number of birds will increase the risk on AI introduction more than adding birds to a region of the same size which already has a higher number of birds. Therefore we assumed this relation to be logarithmic (see Fig. 1a). A comparable assumption was used by Snow et al. (2007) on the risk of AI introduction in

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relation to holding size. Farm density is believed to be an important risk factor for AI outbreaks (Boender et al., 2007), therefore all Spread-Demographics risk factors in this study are farm and not bird related. The relation between poultry farm densities and the risk on major AI outbreaks is unknown, but we assumed it to follow an S-shape curve. As the parameters of this curve are unknown, in this study we therefore assumed a linear relation between farm density and the risk of a major outbreak (see Fig 1b). Table 1. Overview risk factors included in the maps and their weights obtained from the Delphi study

1) Weights are given on scale 0-1. ‘Total points’ (highlighted in grey) are the sums of the risk factor weights multiplied by the category weights.

2) The two risk factors related to free-range housing were replaced by the single risk factor ‘Number of freerange poultry (layers)’. The weights originally given to the two risk factors were summed together. 3) Introduction and Transmission Demographics both consist of one single category; hence no category weights were needed. Figure 1. Assumed relation between bird density and risk AI introduction (a) and farm density and risk major AI outbreak (b).

Class Category Weight category 1)

Risk factor Delphi Proxy factor Weight risk factor 1)

Introduction -Routes Wild birds 0.410 Presence of IBA - 0.215Wild birds 0.410 Number of wild birds in proximity

to domestic poultry farms Number of wintering birds/km2 in IBAs0.242

Wild birds 0.410 Presence of open water bodies - 0.220Legal trade EU-27 0.056 Number of imported live poultry

from EU-27Log-transformed number of imported live poultry from EU-27

0.173

Legal import 3rd countries

0.107 Number of imported live poultry from outside EU-27

Log-transformed number of imported live poultry from outside EU-27

0.255

Total points 0.315

Introduction - Demographics - - Number of commercial poultry farms Number of poultry/km2 0.096

- - Number of commercial duck farms Number of ducks/km2 0.126

- - Percentage of commercial poultry farms with free-range housing + Number of poultry kept in freerange

Number of freerange birds (layers)/km2

2)

0.428

Total points 0.650Transmission - Routes Wild birds 0.106 Presence of IBA - 0.196

Wild birds 0.106 Number of wild birds in proximity to domestic poultry farms Number of wintering birds/km2 in IBAs

0.253

Neighbourhood spread 0.255 Local poultry farm density

Number of commercial poultry farms (>50

LSU)/km2

1.000

Total points 0.303

Transmission - Demographics

- - Number of commercial poultry farms Number of commercial poultry farms (>50

LSU)/km2

0.236

- - Number of large-scale poultry farms (>100,000 birds)

Number of large-scale poultry farms (>500

LSU)/km2

0.123

- - Number of commercial duck farms Number of duck farms/km2 0.106

- - Number of commercial turkey farms Number of turkey farms/km2 0.108

- - Percentage of commercial poultry farms with free-range housing

Number of farms with freerange poultry

(layers)/km2

0.125

Total points 0.698

3)

3)

Bird density

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Farm density

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Besides the four classes mentioned above, also the regional biosecurity level is an important determinant in the risk of AI introduction and spread, but was nevertheless excluded from this study. In section 4 we will discuss this issue in more detail. 2.3. Data 2.3.1 Data collection and sources Data were collected over a period of one year and was obtained from numerous sources. Data on poultry numbers, turkey and duck farm numbers were obtained from agricultural census data made available by the official statistical offices in the EU-27 memberstates. Number of freerange birds and freerange farms was obtained from various institutes and statistical offices within the memberstates and was partly reconstructed from information made available by the International Egg Commission. Trade data (live poultry numbers imported) were obtained from FAOSTAT (http://faostat.fao.org). The location and characteristics of the important bird areas (IBAs) and number of wintering birds in these areas were downloaded from BirdLife International (www.birdlife.org). For Germany, polygon data for all IBAs were received from the German Society for Nature Conservation (NABU) (http://bergenhusen.nabu.de). The coordinates of important wintering sites and the number of wintering birds in these areas were delivered by the Italian project partners of the Italian Wildlife Institute (INFS). Geospatial data on administrative and political boundaries as well as wetlands were bought from GfK Macon, Germany. For the Netherlands, more detailed wetland data (canals, small rivers, estuaries) from the Dutch Centre for Field Ornithology (SOVON) (www.sovon.nl) was implemented to the dataset. In addition, a more precise German wetland dataset (inundation areas, floodplains, estuaries, etc.) was derived from the European Environment Agency, EEA (http://dataservice.eea.europa.eu). During data collection, it became clear that for counting the number of poultry farms, different definitions on the minimum flock size are used in the agricultural census of the member states. Since for many countries farm size information was not available from the agricultural census, Eurostat data that distinguishes poultry farms based on livestock units was used (http://epp.eurostat.ec.europa.eu). The definition of commercial poultry farm was set at a size of >50 LSU which corresponds to > 7,143 broilers, > 3,571 layers and > 1667 other poultry (or less birds in case of mixed farming). The definition of large scale poultry farm was set at a size of >500 LSU, the class with the largest farms distinguished by Eurostat, which corresponds to > 71,429 broilers > 35,714 layers and > 16,667 other poultry. The farm definition issue will be further discussed in section 4. 2.3.2 Data preparation Data on number of birds or farms obtained from the various data sources differed in year of collection (range 1999-2007) as well as the regional level at which data were available. Regional levels within the EU-27 are expressed in so called NUTS levels (N1-3). The NUTS-system is a hierarchical classification system of regions in a country; an overview can be found on http://ec.europa.eu/eurostat/ramon/nuts/home_regions_en.html. For reasons of comparison, data on only one particular NUTS level was used (either N2 or N3) for each bird or farm related risk factor; the choice of the level was based on data availability. Moreover, only the most recent data available for each particular country were considered. Even though a single NUTS level was used, the sizes of regions differed considerably (for example, N3 range 24.1 - 106,011.5 km2). The number of freerange birds (mainly layers) and freerange farm data were scarce and were at first only obtained for (part of) Germany, Denmark, Slovakia, England, The Netherlands and France. Since this would seriously reduce the number of countries in the final maps, other

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data sources were consulted. According to IEC (2007) the number of freerange layers in Finland, Greece, Hungary, Italy, Spain and Sweden were estimated to be 3% or less. Assuming that in these countries the freerange layers follow a similar distribution over the regions as the total number of layers for which data was available, the freerange layer numbers were calculated using the estimated percentages. No additional information on freerange farms could be found. Again the bird and farm numbers were converted into bird and farm densities and entered in a Microsoft Excel spreadsheet. Bird densities were subsequently log-transformed, because of the assumed logarithmic relation with risk of introduction. Trade related risk factors comprised the total number of live poultry imported from either the EU or third countries (i.e. non-EU countries) per year. These data were only available on country level and were not converted into densities, but were log-transformed using a base-10 log. With the exception of Germany, for all IBAs only a pair of x and y coordinates of each IBA centre point was available. The coordinates were transformed into decimal degrees and imported in the GIS. To get polygon data for further conversion purposes, the radius of each area was calculated and used to buffer each IBA site. The same procedure was applied for the 124 IBA wintering sites in Europe. In addition, the bird numbers wintering at these sites were entered in Microsoft Excel spreadsheets, converted into densities and subsequently log transformed. Afterwards, the dataset was joined to the geospatial dataset in the GIS. The wetland data consisted of small and large rivers per country, lakes, inundation areas as well as mudflats. Since rivers are represented as line features in the geospatial dataset, they were buffered by a radius of 50 and 100 meters. Accordingly, all wetland shapefiles were merged together in ArcGIS 9.2. From a spatial point of view, two types of data were collected: 1) numeric data (bird and farm numbers, trade data) that needed further processing before it could be visualized in polygon maps (see 2.3.3) and 2) geospatial data (wetlands and IBAs) that could almost directly be processed into gridmaps and could only adopt two values: present (1) or absent (0). 2.3.3 Standardization and classification In order to reach a qualitative risk classification of the regions, combining the single risk factor maps into final maps is a crucial step. The data of the various risk factors are expressed in different measurement units and have entirely different ranges. To avoid arbitrary classification of each risk factor, all bird, farm and trade related data were therefore standardized to scores between 0-1 using the following well-known standardization method used in multicriteria analyses (e.g. see Voogd, 1982):

jii

jii

jii

ji

ji ss

ssss

minmax

min

−

−= , in which sji is the ‘raw’ value of risk factor j in region i, ji

ismin and

jii

smax are the minimum and maximum values of the risk factor j over all regions, and ssji is

the resulting standardized score. Note that if the minimum value in the dataset is 0, which is the case for most of the risk factors in the dataset, this formula is simplified to:

jii

jiji s

sss

max= .

Note that for all bird density and trade data this standardization method was applied to the log-transformed data. The standardized scores were calculated in Microsoft Excel spreadsheets.

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2.4. Visualising the data: single risk factor maps and raster maps The EU-27 shape files from the GfK Macon map set were implemented into ArcGIS 9.2 and projected using the cylindrical map projection Mercator. Subsequently, the standardized score datasets were imported to the GIS and joined with the geospatial data. The scores were classified in 5 equal intervals (0-0.2, 0.2-0.4, etc.), missing values were excluded. Due to different scales and feature types, the vector data were converted into raster maps to receive comparable units, which can be easily set in relation to each other. Within the raster process each grid cell was allocated to the corresponding shape file value. To cover even the buffered river polygons, a cell size of 100x100 m was defined. 2.5. Combined risk factor maps 2.5.1 Algorithms To combine the 13 single risk maps into the ‘introduction-routes’, ‘introduction-demographics’, ‘spread-routes’ and ‘spread-demographics’ maps, four weighted sum algorithms were constructed using the average weights resulting from the Delphi study (Geenen et al., this report). All weights were rescaled to ascertain that the scores of the combined risk factor maps obtained values between 0 and 1 (rescaled weights see Figs. 2 and 3). For example, to calculate the resulting value of a grid cell on the ‘introduction-demographics’ map, the values of the corresponding grid cells on the poultry density, the duck density and the free-range density map were multiplied with 0.148, 0.194 and 0.658 respectively, and summed together. Subsequently, ‘introduction-routes’ and ‘introduction-demographics’ were combined into the ‘introduction-final’ map and the ‘spread-routes’ and ‘spread-demographics’ into the ‘spread-final’ map by simply summing the grids. Equal weights were assumed, since ‘routes’ and ‘demographics’ were not rated against each other by the experts. Due to dominance of the wild bird data and missing data, alternative ‘introduction-final’ and ‘spread-final’ maps were produced (see results section). The algorithms were applied to the grids using the weighted sum procedure in the Spatial Analyst in ArcMAP 9.2. The resulting scores (range of y-values) were divided in three equal intervals (‘low’, ‘medium’ and ‘high’ and visualized on the final maps. Missing values in any of the single risk factors resulted in a missing value in the final map.

Figure 2. Flow chart of the AI introduction risk maps with rescaled weights

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Figure 3. Flow chart of the AI spread risk maps with rescaled weights 2.5.2 Overall risk map To identify regions with a relatively higher risk for both AI introduction and spread, an overall risk map was produced. It was not intended to produce a very detailed map, but only to give a rough indication of areas that from the current (limited) data could be identified as having a higher risk. Therefore N2 regions on an alternative AI introduction final risk map (routes:demographics as 1:2, see section 3.2) and on the AI spread final risk map were evaluated visually as either low, medium or high risk areas based on their colours in the respective maps. Regions with a substantial high risk area on the introduction final map, i.e. excluding red ‘dots’ (which resulted from IBA locations with high weights), were evaluated as being high risk regions independent of the other colours present. Regions with at least 1/3 part yellow and apart from that only green, were classified as medium risk region. For the regions on the introduction final map that were unknown due to one or more missing risk factors, a closer look at the underlying available data was taken. All missing duck density data were replaced by best (0) and worst case (1) scores. Similarly, all missing freerange poultry density data were replaced by best (0) and worst case scores. For the worst case scores, all poultry in the region was assumed to be freerange. The results of the two final risk maps for each N2 region were combined using the colour scheme shown in Fig. 4. When both best and worst case scores resulted in a similar final classification, the region was evaluated as such. Regions with an inconclusive final classification remained grey.

Figure 4. Colour scheme to combine the final-introduction and final-spread map into the overall risk map

Fin

al-i

ntro

duct

ion

Low

Med

ium

H

igh

Final-spread

High Medium Low

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3. Results 3.1. Single risk factor maps The 13 single risk factor maps are shown in Appendix A. For clarity, the threshold values of the five score classes (0-0.2; 0.2-0.4, etc.) have been (re)converted into the original units. As a consequence the threshold values differ for each risk factor depending on the maximum and minimum value within the EU-27 regions. Note that the farm density classes follow a linear scale whereas bird density and live poultry imported classes follow a logarithmic scale. The duck farm density and turkey farm density maps (Figs. 11 and 12) should be interpreted with care as farm sizes were not available and as a result for some countries the data includes backyard flocks and for others not. For example, given the current dataset considered, turkey farm densities in Italy are highest in the Abruzzo and Marche region. However, when backyard turkey would be excluded, the density would be highest in the Veneto and Lombardy region (M. Toson, personal communication). 3.2. Combined risk factor maps In appendix B, the combined risk factor maps are shown. The scales of these maps have three classes, high, medium, and low, that refer to the relative risk of the regions. This means that, based on the combination of the values of the risk factors at the particular area and their respective weights, a red area has a relatively higher risk than a yellow area, whereas a yellow area has a relatively higher risk than a green area. Note that the routes maps (Figs. 1 and 5) have a higher level of detail than the demographics maps (Figs. 2 and 6) due to the detailed wetland, IBA and wintering bird data. Many regions in the demographics maps are not classified mainly as a result of missing duck (farm) and freerange (farm) data. In the AI introduction final map (Fig. 3), strong emphasis on the wild bird introduction routes is laid. As a result only small red spots show up, which represent IBAs in areas with a relatively high (free-range) poultry density. To put more emphasis on demographics, an alternative AI introduction final map was produced in which ‘demographics’ was given twice the weight of ‘routes’ (Fig. 4). Since more than 85% of the N3 regions on the ‘spread-demographics’ map (Fig. 6) could not be classified due to missing data and the turkey and duck farm density data was known to be inconsistent, the final risk map for AI spread was constructed based on the weighted sum of commercial and large-scale farming data only (Fig. 7). The rescaled weights used for commercial and large-scale farming were 0.657 and 0.343, respectively, and were based on the original spread-demographics weights. Finally in Fig. 8 the overall AI risk map is shown. Areas that are coloured red or yellow on this map represent areas with a relatively higher risk for both AI introduction and spread compared to the green areas. The result of the remaining grey areas is inconclusive as a result of accumulated missing data. 4. Discussion This paper describes a first approach to identify regions with a relatively higher and lower risk for AI introduction and extensive spread in domesticated poultry within the EU-27. Based on expert opinions (see Geenen et al., this report), main risk factors for demographics and routes for AI introduction and spread were selected and accordingly data were collected from various sources within the EU-27. The results of this study were strongly affected by data availability and data quality. This is partly inherent of EU wide studies as many different data sources need to be consulted; moreover data quality is identified as a general concern for GIS

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users (Pfeiffer and Hugh-Jones, 2002). Data quality problems that were encountered were: demographical data collected at different NUTS levels and in different years, incomplete datasets (e.g. wild bird information), lack of geospatial data (e.g. for important bird areas) and the use of different definitions across member states. Examples of the latter were freerange poultry, for which the definitions differed from ‘freerange layers only’ to ‘all poultry including backyard flocks’, and the missing definition of a poultry farm (Grabkowsky & Windhorst, this report). In Poland e.g. each farm with 30 chickens or more is counted as a poultry farm, whereas in The Netherlands only farms with more than 3 NGE (Dutch unit of measurement valued at 1400 euro) are counted, which corresponds to 2300 broilers or 1150 layers (or less birds in case of mixed farming). The farm size problem was solved by using poultry farm data from Eurostat which is expressed in so-called livestock units (LSU). Note that LSU are not only based on the number of poultry on a farm, but also on the other livestock present; for the purposes of this study poultry farm size classes based on the actual number of poultry present would have been preferred. The farm sizes were particularly important as the number of commercial poultry farms (as opposed to backyard farms) and large-scale farms were selected as main risk factors for AI spread; the role of backyard farms is generally thought to be small (EFSA, 2005; Otte et al., 2006; Otte et al., 2007). In appendix C, the effect of choosing a certain farm size level is illustrated by maps made of the poultry farm counts from Eurostat at different LSU thresholds. From map 1 (all farms) to map 4 (only the largest farms) a notable shift can be seen from high densities in Romania and Poland to high densities in The Netherlands and France. Besides risk factors related to demographics and routes, risk factors related to biosecurity are generally considered important (Capua and Marangon, 2007). Biosecurity is usually regarded at farm level and not at regional level, therefore the expert panel was asked for advice (Geenen et al., this report). The panel suggested either to classify the regional biosecurity level by using the poultry production sector classification system of the FAO (FAO, 2004), or to compose a measure from a combination of aggregated risk factors at farm level. When looking into the first suggestion, difficulties were met. The FAO system is mainly based on the situation in developing countries and data was not readily available for the EU. Regions with backyard would be classified as equally low and regions with integrated farms as equally high in biosecurity, which is not very informative from a risk assessment perspective, which brings us to option 2. Option 2 however, was deemed infeasible to perform in a short time span for all EU member states; an example approach for Germany, Austria and The Netherlands can be found in the paper by Grabkowsky et al., (this report). Hence biosecurity was excluded from this study, but remains an important topic for further research. Although data on many factors were unavailable, e.g. data on illegal trade, 30-70% of the points given by the experts were represented by the data collected. Despite the data availability and inconsistency problems, the single risk factor maps clearly point out that there are large differences with respect to AI risk factors in the various regions of the EU-27. After data collection, a second challenge was faced; combining the single risk factor maps into risk maps that show in which regions risk factors would accumulate to relatively high risk of AI introduction and spread. For this first approach it was chosen not to use a biological model, but merely use a weighted sum algorithm with weights given by the experts. The weighted sum algorithm is helpful for a first approach as it is very transparent, but unfortunately it cannot represent interactions among the risk factors, which makes it less realistic. The authors would like to stress that the resulting maps shown in this paper should be interpreted with care; the maps present the relative (i.e. not absolute) risk of regions based on currently available data and may change when better or more data and improved models will

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be available, when other standardization methods will be used, or insight in the AI epidemiology changes. Moreover, uncertainties in e.g. weights are sometimes high, but this could not be shown in the maps, and last but not least, inconsistencies in the underlying data will also accumulate and strongly affect the results of the combined maps. For future research it is therefore recommended to investigate sensitivity analysis methods for this type of GIS risk assessments and to reevaluate the weights used in the algorithm. For example, the weight for the freerange poultry was relatively high as it was the sum of two freerange risk factors in the Delphi study and in the introduction-routes map the wild bird introduction routes seem to be overrepresented. A trial to replace missing data on other introduction routes (returning livestock trucks, poultry service/professionals, legal interregional trade within EU poultry supply chains) with poultry density as a ‘rough’ proxy did not lift the dominance of the wild bird data, since too much other data with relatively high weights (illegal trade, live bird markets, imported birds other than poultry) were missing and because of the relatively large, but uncertain average weight estimated by the expert panel. Another factor that needs to be considered is the regional level used. The regional level chosen should be suitable for taking decisions on animal disease control, should be suitable for data collection and should be sufficiently detailed for distinguishing between regions. For spread for example, local farm density, e.g. NUTS 5, would have been more representative than farm density at NUTS 2, but NUTS 5 level data was only available for a few member states. On the overall risk map, many regions turn up green as they consistently have low scores on the single risk factor maps (e.g. Scandinavian and Baltic countries). These green regions include the northern part of Italy which has experienced several AI outbreaks (Capua et al., 2003) and hence was expected to show up in yellow/red in the overall risk map. The cause for this is not clear and should be looked into. Of course not all risk factors that may have played a role in the Italian outbreaks have been included, the amount of freerange poultry may have been underestimated, and also the relatively low farm densities at NUTS 2 level may not have been representative of the situation in Northern Italy. Nevertheless, in the overall risk map we were able to identify various regions that consistently have high scores in the various underlying risk maps (e.g. parts of The Netherlands, Germany and France). Germany and The Netherlands have adjacent medium/high risk areas, and on the spread maps a medium/high risk zone across Germany, The Netherlands, Belgium and Northern France shows up. These cross-border risk zones may have serious implications for AI control. In this study, we have shown that large differences with respect to AI risk factors in the various regions of the EU-27 exist and demonstrated that with basic and comprehensible GIS methods these data can be combined into risk maps. For some regions careful conclusions can be made on their relative risk for AI, whereas for others classification is not yet possible as essential data is missing. In the member states, more attention should be given to the collection of wild bird data, freerange poultry (farm) data and data related to trade in live birds. Harmonization of data collection within the EU as well as data without disclosure for such research purposes would increase data quality and together with adapted methodology such as sensitivity analysis, the results of comparative and risk assessment studies will greatly improve. Although the risk maps presented in this study cannot be readily used by decision makers, this first approach shows the great potential of GIS-based risk assessment for decision support in animal disease control. Acknowledgements The authors would like to express their sincere thanks to all the people of the statistical offices and institutes in the EU-27 member states that assisted us in obtaining the data. Special thanks to Mara Scremin, who prepared and delivered the data on wintering bird sites. Financial support was given by EUGrant SSPE-CT-2004-513737 ‘Healthy Poultry’.

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References Boender GJ, Hagenaars TJ, Bouma A, Nodelijk G, Elbers ARW, De Jong MCM, Van Boven M. 2007. Risk maps for the spread of highly pathogenic avian influenza in poultry. PLoS Computational Biology 3: 704-712. Capua I, Marangon S, Dalla Pozza M, Terregino C, Cattoli G. 2003. Avian influenza in Italy 1997-2001. Avian Diseases 47: 839-843. Capua I, Marangon, S. 2007. Control and prevention of avian influenza in an evolving scenario. Vaccine 25: 5645-5652. EFSA, 2005. Scientific Opinion on Animal health and welfare aspects of Avian Influenza. The EFSA Journal (2005) 266, 1-21. FAO, 2004. FAO Recommendations on the Prevention, Control and Eradication of Highly Pathogenic Avian Influenza (HPAI) in Asia. Downloadable from: www.fao.org/docs/eims/upload/165186/FAOrecommendationsonHPAI.pdf IEC, 2007. International egg market, annual review 2007. International Egg Commission. Otte J, Pfeiffer D, Tiensin T, Price L, Silbergeld E. 2006. Evidence-based policy for controlling HPAI in poultry: biosecurity revisited. PPLPI Research Report. Downloadable from: http://www.fao.org/AG/againfo/projects/en/pplpi/docarc/rep-hpai_biosecurity.pdf Otte J, Pfeiffer D, Tiensin T, Price L, Silbergeld E. 2007. Highly pathogenic avian influenza risk, biosecurity and smallholder adversity. Livestock Research for Rural Development 19: Article #102. Pfeiffer DU, Hugh-Jones M. 2002. Geographical information systems as a tool in epidemiological assessment and wildlife disease management Rev. sci. tech. Off. int. Epiz. 21: 91-102. Pfeiffer DU. 2004. Geographical information science and spatial analysis in animal health. In: P.A. Durr and A.C. Gatrell (eds.) GIS and Spatial Analysis in Veterinary Science. CAB International, Wallingford, Oxfordshire, England. 119-144. Pittman M, Laddomada A. 2008. Legislation for the control of avian influenza in the European union Zoonoses and Public Health 55: 29-36. Sabirovic M, Hall S, Wilesmith J, Grimley P, Coulson N, and Landeg F. 2007. Assessment of the Risk of Introduction of H5N1 HPAI Virus from Affected Countries to the UK. Avian Diseases 51: 340-343. Snow LC, Newson SE, Musgrove AJ, Cranswick PA, Crick HQP, Wilesmith JW. 2007. Risk-based surveillance for H5N1 avian influenza virus in wild birds in Great Britain. Vet Rec 161: 775-781. Voogd JH. 1982. Multicriteria evaluation for urban and regional planning. PhD Thesis. TU Eindhoven. Downloadable from: http://alexandria.tue.nl/extra1/PRF4A/8203510.pdf

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Appendix A: Single risk factor maps Fig.1 Important bird areas in EU-27 Fig.2. Wintering birds density in EU-27 (based on IBA data)

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Fig.3 Presence of open water bodies in EU-27 Fig.4 Number of live poultry imported from EU-27

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Fig.5 Number of live poultry imported from outside EU-27 Fig.6 Poultry density in EU-27

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Fig. 7 Duck density (commercial production) in EU-27 Fig. 8 Freerange poultry (mainly layers) in EU-27

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Fig. 9 Commercial poultry farm (>50 LSU) density in EU-27 Fig. 10 Large scale poultry farm (>500 LSU) density in EU-27

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Fig. 11 Duck farm density in EU-27 (note data consistency Fig. 12 Turkey farm density in EU-27 (note data consistency problem, see 3.1) problem, see 3.1)

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Fig. 13 Freerange farm density (mainly layer farms) in EU-27

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Appendix B: Combined risk factor maps Fig. 1 AI introduction routes risk map Fig. 2 AI introduction demographics risk map

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Fig. 3 AI introduction final risk map (routes:demographics is 1:1) Fig. 4 AI introduction final risk map (routes:demographics is 1:2)

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Fig. 5 AI spread routes risk map Fig. 6 AI spread demographics risk map

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Fig. 7 AI spread final risk map (commercial and large-scale Fig. 8 AI overall risk map (introduction and spread combined) farming only)

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Appendix C: Illustration of differences in EU-27 poultry farm sizes. Classification of N2 regions based on farm densities of 1) all farms, 2) farms >5 LSU, 3) farms >50 LSU and 4) farms > 500 LSU. Fig.1 Farm density (all farms) in EU-27 Fig.2 Farm density (farms >5 LSU) in EU-27

Fig.3 Farm density (farms >50 LSU) in EU-27 Fig.4 Farm density (farms >500 LSU) EU-27