Landscape effects and spatial patterns of avian influenza virus in Danish wild birds, 2006–2020

Abstract Avian influenza (AI) is a contagious disease of birds with zoonotic potential. AI virus (AIV) can infect most bird species, but clinical signs and mortality vary. Assessing the distribution and factors affecting AI presence can direct targeted surveillance to areas at risk of disease outbreaks, or help identify disease hotspots or areas with inadequate surveillance. Using virus surveillance data from passive and active AIV wild bird surveillance, 2006−2020, we investigated the association between the presence of AIV and a range of landscape factors and game bird release. Furthermore, we assessed potential bias in the passive AIV surveillance data submitted by the public, via factors related to public accessibility. Lastly, we tested the AIV data for possible hot‐ and cold spots within Denmark. The passive surveillance data was biased regarding accessibility to areas (distance to roads, cities and coast) compared to random locations within Denmark. For both the passive and active AIV surveillance data, we found significant (p < .01) associations with variables related to coast, wetlands and cities, but not game bird release. We used these variables to predict the risk of AIV presence throughout Denmark, and found high‐risk areas concentrated along the coast and fjords. For both passive and active surveillance data, low‐risk clusters were mainly seen in Jutland and northern Zealand, whereas high‐risk clusters were found in Jutland, Zealand, Funen and the southern Isles such as Lolland and Falster. Our results suggest that landscape affects AIV presence, as coastal areas and wetlands attract waterfowl and migrating birds and therefore might increase the potential for AIV transmission. Our findings have enabled us to create risk maps of AIV presence in wild birds and pinpoint high‐risk clusters within Denmark. This will aid targeted surveillance efforts within Denmark and potentially aid in planning the location of future poultry farms.


| INTRODUC TI ON
Avian influenza (AI) is a contagious disease of birds with zoonotic potential. It is caused by Influenza A viruses (AIV), and can be classified as low pathogenic (LPAI) and high pathogenic (HPAI) subtypes based on their pathogenic phenotype. Only AIV of subtype H5 and H7 are known in the HPAI form. LPAI is a persistent problem worldwide. LPAI is found in most bird species, and LPAI subtypes H5 and H7 have the potential to mutate into HPAI, which can cause great economic loss and animal welfare problems when farmed birds are infected (Monne et al., 2014;Rao et al., 2009). Furthermore, some AIV subtypes have zoonotic potential with high case-fatality for humans (Lai et al., 2016); thus, it is crucial to monitor and prevent the geographical spread of AIV in both wild and farmed birds. Control measures to prevent the dispersal of AIV include transport restrictions between areas at risk, contact tracing, hygiene measures and culling exposed animals (Stegeman et al., 2004).
Several countries have implemented surveillance programmes to monitor the distribution of AI and evaluate the spatio-temporal risk, both for wild and farmed birds (Bevins et al., 2014;Buscaglia et al., 2007;Hesterberg et al., 2009;Machalaba et al., 2015). Data obtained from these surveillance programmes can aid in developing statistical spatio-temporal models to identify high-risk areas and critical time periods, which can optimise surveillance for AI. Prediction models for AIV occurrence and risk of HPAI outbreaks have, to a large extent, focused on landscape use, which can indicate the density of specific birds with higher risk of transmitting AIV (Gilbert et al., 2008;Paul et al., 2010;Ward, Maftei, Apostu, & Suru, 2008). Studies have also described continental hotspots for AIV subtypes (Bevins et al., 2014), showing that it is possible to identify risk factors on a large geographical scale. Denmark has dense wild birding areas that intersect with many bird migration routes, including routes coming to and from Europe (Bregnballe et al., 1997), Africa (Tøttrup et al., 2018) and Siberia (Dick et al., 1987). Therefore, there is a high potential for AIV incursions from other regions. In particular, migratory birds from Siberia appear to be a risk factor, as Siberia has previously been identified as a major hub for AIV dispersal (Lai et al., 2016;Li et al., 2014). Additionally, a large number of game birds are released every year for hunting in Denmark (Gamborg et al., 2016;Kanstrup et al., 2009). Some of these game birds originate from other countries (Ministry of Environment & Food of Denmark, 2020; The Danish Hunting Association, 2020), increasing the potential of introducing AIV into Denmark.
Since 2002, the Danish authorities have carried out surveillance for AIV in wild birds. We obtained data from this surveillance system generated between 2006 and 2020 and explored potential patterns of AIV occurrence and spatial risk factors in Denmark. The aim of the study was to identify areas with high or low occurrence of AIV and possible factors associated with these occurrences, in order to optimize future surveillance for AIV. Furthermore, we aimed to assess bias in the Danish passive AIV surveillance data submitted by the public by assessing variables related to human accessibility.
Potentially, our results can be applied to future planning efforts; for example, high-risk areas should be excluded when planning the location of future poultry farms.

| Passive and active AIV surveillance data
We obtained virus detection data from both passive (2006−2020) and active (2007−2019) wild bird AIV surveillance. Passive surveillance data were from the EU mandatory passive surveillance program in Denmark, in which dead and diseased wild birds are tested for AIV and particularly H5/H7 subtypes, whereas the active surveillance data are based on samples from healthy birds, captured for sampling or ringing, submitted by hunters, or from bird dropping samples. Some of the observations in the active AIV surveillance data were pooled samples, whereas others were from individual birds, which had to be taken into account when analysing the data (Section 2.4).
All data were manually checked for entry errors and plotted in ArcMap 10.6.1 (Environmental Systems Research Institute, 2017) to check for any errors in the coordinates (such as coordinates not being located within Denmark). The passive location data all had UTM coordinates for where the birds were found. As the birds from the passive AIV surveillance data were found by the public, we suspected it to be biased due to varying detection probabilities as well as human accessibility to wildlife areas. To assess this, we compared various accessibility variables of the passive AIV surveillance location data to random locations within Denmark (section 2.3 and 2.4). The active surveillance data only had precise UTM-coordinates from 2007 to 2010. From 2011 to 2019 the active surveillance data only registered the postal code of where the sample was collected. To create one single dataset for the active surveillance data, we converted the 2007−2010 coordinates to postal codes instead, and conducted all our analyses on active surveillance data at the postal code level. We also created a single wild bird AIV surveillance dataset by combining the passive and active AIV surveillance data, leaving us with three datasets on which to conduct our analyses-the passive AIV surveillance data, the active AIV surveillance data, and a combined wild bird AIV surveillance dataset. When combining the active and passive AIV surveillance data, we converted all the passive surveillance data coordinates to postal codes, producing a combined dataset based on postal codes alone. We refer to this combined dataset as the wild bird AIV surveillance data throughout this paper.

| Data on game birds
We obtained data from 2018 to 2019 on game birds bred and released for hunting from the Danish.
Environmental Protection Agency. This data had addresses only and no coordinates, thus we used ArcGIS World Geocoding Service (Environmental Systems Research Institute, 2017) to transform all addresses to UTM coordinates. In some cases, only a postal code was reported for the release site, and not a complete address. In those cases, we used the centroid coordinates of the total area of that particular postal code. These centroid coordinates were obtained from a shape file of all Danish postal codes and their areas (Danish Map Supply;Kortforsyningen, 2020). There was no information on the origin of the released birds in the data. To test if game bird releases affected AIV presence/absence in the passive and active AIV surveillance data, we extracted observations from 2018 to 2020 from the surveillance data. We included the year 2020, as we allowed for game bird release to have occurred up to 8 months prior to an observation in the surveillance data. For each observation in the passive AIV surveillance data, we then calculated the nearest game bird release within the last 8 months prior to the observation and identified the species released and the number of birds released.
For the active and wild bird AIV surveillance data, we calculated the number of game bird releases and the total number of birds released up to 8 months prior to the observation within the same postal code as the observation.

| Landscape variables
As wild birds are natural reservoirs of AIV and the dispersal of AIV is thus linked to wild bird movement, we wanted to include landscape variables that could be associated with wild birds, such as breeding sites, feeding sites and overwintering sites. In particular, migrating birds have long been suspected to introduce AIV into naïve populations (Hill et al., 2012;Verhagen et al., 2014), thus we focused on landscape variables (coastal areas and wetlands) where migratory birds are known to gather in high numbers (Belkhiria et al., 2018).
We obtained Corine land cover data as a 100 m 2 resolution raster consisting of 100 × 100 m pixels (European Environment Agency, 2018). For each observation in the passive AIV surveillance data, we extracted the land cover types for the UTM coordinates using the raster package (Hijmans, 2019) in R 3.5.2 (R Development Core Team, 2018). We furthermore calculated distance to coast and distance to wetlands for the passive surveillance data in ArcMap 10.6.1 (Environmental Systems Research Institute, 2017). Distance to wetlands was calculated by selecting only Corine land cover types defined as wetlands (inland marshes, peat bogs, salt marches, salines, intertidal flats). We then calculated the closest distance from our observations to a wetland pixel centroid. To calculate distance to coast line, we obtained a shape file of the Danish coast line (Danish Map Supply; Kortforsyningen, 2020) and added a 1 km buffer. We then calculated the closest distance from our observations to this buffered coastline.
To assess the effect of accessibility on passive AIV surveillance locations, we furthermore calculated distance to roads and distance to cities as well as population density at each location. To calculate distance to roads, we obtained a shape file of all roads in Denmark (Danish Map Supply; Kortforsyningen, 2020) and calculated the closest distance to a road for each location. Population density at a location was extracted from the Gridded Population of the World dataset (raster with 1 km 2 resolution; Socioeconomic Data & Applications Center, NASA, 2015). We also used this raster data to calculate distance to nearest city, defining a city to be a raster grid cell with ≥ 200 inhabitants/km 2 . Distance to nearest city pixel centroid was then calculated for each location in the passive AIV surveillance data. All distance calculations were completed in ArcMap 10.6.1 (Environmental Systems Research Institute, 2017).
As the active and wild bird AIV surveillance data were at the postal code level, instead of distances, we calculated the area of wetlands, coast and city within a postal code. We chose the area of city as a measure of whether the area within a postal code was mostly rural with a low density of people or if it was more densely inhabited. Area of wetland and coast were calculated using the 100 m 2 resolution Corine land cover data (European Environment Agency, 2018), whereas area of city was calculated using the Gridded Population of the World dataset (raster with 1 km 2 resolution; Socioeconomic Data & Applications Center, NASA, 2015).
Similar to the passive AIV surveillance data calculations, a city was defined as having > 200 inhabitants/km 2 . These calculations were completed in R 3.5.2 (R Development Core Team, 2018), using the raster package (Hijmans, 2019).

| Bias in the passive AIV surveillance data
To assess any potential bias in data submitted by the public, we compared our passive AIV surveillance data locations to random locations within Denmark in regard to accessibility. We created random locations and extracted distance to coast, distance to roads, distance to cities, and population density for each of these locations, using the same methods as in section 2.3.

| Statistical analysis
To test for bias in the passive AIV surveillance data, we compared accessibility variables from these locations to the random generated locations using a Kolmogorov-Smirnov test in R 3.5.2 (R Development Core Team, 2018).
We used mixed generalised linear models (GLMs) in the lme4 package (Bates et al., 2015) in R 3.5.2 (R Development Core Team, 2018) to test for associations between landscape and game bird variables and passive, active and wild bird AIV surveillance data.
For the passive AIV surveillance data, we used year and month of the observations as random effects, since we knew that observations varied over the months and years. We could not estimate prevalence due to the nature of the data, and our focus was on whether AIV was present at a location or not. Thus, if multiple birds from the same location were observed on the exact same date (meaning they were probably found together), we aggregated these multiple observations into a single observation with presence of AIV if any of the observations were AIV positive (see section 3.1 regarding the differentiation of subtypes in the data). Exact locations very rarely reoccurred on separate dates (see Section 3.1), and thus location was excluded as a random variable. For the active and wild bird AIV surveillance data, we also used year and month as random variables.
These data were based on postal codes and the same postal codes did reoccur between months and years, thus postal code was also used as a random variable. In the active AIV surveillance data, an observation could be anything from a single bird, to a pooled sample of multiple birds. To avoid any errors or misrepresentations arising from this-and as we were only interested in whether AIV had been confirmed within a postal code in a given month-we summarized observations from the same month and postal code into one observation. If any of the multiple observations within the same month and postal code were AIV positive, the summarized observation was classified as positive (see Section 3.1). This procedure was also used on the wild bird AIV surveillance data.
Effect of game bird release was analysed for the years 2018−2020, and we included the year and month of the observations as random variables. As above, we aggregated multiple observations from the same location or postal code on the exact same date (passive AIV surveillance) or from the same month and year (active and wild bird AIV surveillance) into one single presence/absence observation. For the active and wild bird AIV surveillance data, we then calculated the number of releases and the total number of birds released up to 8 months prior to the summarized data for that month and postal code. For active and wild bird AIV surveillance data, we also included postal code as a random effect. We only used the GLM with variables pertaining to game birds, as we wanted to investigate any possible association.
When needed, for all GLMs, we used backwards stepwise elimination by removing the variable with highest P-value, and re-running the mixed GLM. We also performed an ANOVA between the original and the reduced model to check whether reduction in the residual sum of squares (SS) was statistically significant or not, and compared AIC-values between models. Lastly, we checked the final models for spatial autocorrelation by plotting the residuals.
If the landscape variables were found to be associated with AIV presence, we wanted to use these variables and the GLM models to predict the probability of AIV presence throughout Denmark. To measure predictive power of our GLM models, we reran the models using a leave-one-out cross validation (LOOCV) scheme. This method fits the model as many times as there are observations and each time, withholds one location. We then used the model to predict the withheld location. By withholding all locations, one-by-one, we achieved a measure of predictive power-that is, how well we could predict the AIV status of each location based on the other locations. As the models could not predict using unknown factor levels in the LOOCV (for example unique postal code or unique Corine land cover), we had to exclude observations whose factor level only appeared once in the dataset. We did this because when leaving out an observation with a unique factor level in the LOOCV, the model based on the remaining factor levels does not recognize the one left out, and thus cannot predict using this factor level. We also investigated the predictive power by estimating accuracy, sensitivity and specificity to assess the validity of using the model to predict unknown locations.
For the passive AIV surveillance models, we wanted to predict a map of Denmark in a 1 km 2 resolution. To do so, we created three 1 km 2 raster maps that each covered the entire area of Denmark.
We obtained Corine land cover data in a 1 km 2 raster resolution (European Environment Agency, 2018), and removed land cover types not observed in the location data, as we would not predict to unobserved land covers. For the other two rasters, for each raster pixel centroid within the rasters, we calculated the distance to coast or to wetlands, and thus created two rasters that for each 1 km 2 in Denmark depicted the distance to coast and the distance to wetlands respectively. We used Corine land cover (1 km 2 , European Environment Agency, 2018) to calculate the distances to coast and wetlands. For the active and wild bird AIV surveillance data, we created data on the area of coast, wetlands and city for each postal code in Denmark (based on Corine land cover 100 m 2 resolution raster, thus the units are in 100 m 2 ). All calculations were completed in R 3.5.2 (R Development Core Team, 2018).

| Cluster analysis
To identify potential clusters of AIV within Denmark, we used the program SatScan and the package rsatscan (Kleinman, 2015) in R 3.5.2 (R Development Core Team, 2018). For passive, active and wild bird AIV surveillance data, we performed spatial scan analyses for summarized years and for separate years with an elliptical scanning window, using the Bernoulli probability model and a maximum spatial window size of less than or equal to 50% of the total population at risk. This form of analysis identifies significant spatial clusters where there is a higher (hotspots) or lower (cold spots) number of positive cases within the scanning window than expected based on the Bernoulli probability of the entire study area. SatScan then reports the ODE, which is the ratio of observed number of positive cases within a cluster to the expected number. Interpretation of an ODE of 1 means that there is no difference from the expected number of cases. We used the Gini coefficient (Han et al., 2016) for cluster selection, as it measures the heterogeneity of the cluster collection, aiding us in which clusters to report (multiple smaller clusters versus large joint clusters). All analyses focused on presence or absence of AIV at a specific site or postal code-not the number of cases reported.

| Passive and active AIV surveillance
As only a few wild birds in the passive AIV surveillance data tested positive for AIV, we did not differentiate the positive data by AIV subtype, but rather we categorised the data as AIV detected at a location or not. The different AIV subtypes detected are summarised in Table 1. For the same reason, we did not differentiate the data by bird species. The passive AIV surveillance dataset consisted of 2,089 observation entries and 1,601 unique site locations ( Figure 1). Of these 2,089 entries, 189 were AIV positive (Table 1).
When summarizing same-date and same-location observations for the mixed GLMs, 208 of the 1,601 unique sites had multiple entries ranging from 2−55 birds. The summarized dataset used in the GLM contained 1,614 observations, as 11 locations had multiple entries on different dates within the same year (9 locations with 2 dates, and 2 locations with 3 dates, Figure S1). Of the 1,614 observations, 144 were AIV positive. We found significant differences for all accessibility variables when the 1,601 unique locations were compared to 1,601 random locations (all p < .0001, Figure 2), but as positive and negative AIV locations were equally biased, we proceeded with our analyses described in Section 3.3 and 3.4.
The active AIV surveillance dataset consisted of 8,912 observations within 234 unique postal codes (Figure 3). There were 1,066 observations in this dataset that tested positive for AIV (the AIV subtypes are summarised in Table 1)

| Data on game birds
A total of 2,268 game bird releases were recorded from 2018−2019 at 1,179 unique locations. The total number of birds released was 1,558,302; of these 92.7% were pheasants (Phasianus colchicus), 6.6% were mallards (Anas platyrhynchos) and 0.7% were grey partridges (Perdix perdix).

| Landscape and AIV presence/absence
For the passive AIV surveillance data, distance to coast and distance to wetlands were significant (p < .01, odds ratio (OR) = 0.9994 and 0.9992, respectively), whereas land cover at the location was not. However, we kept the land cover variable in the model, since a comparison of the full and reduced model showed significant differences in the residual SS (p < .0001) and removing land cover increased the AIC and reduced the R 2 (Table 2, Figure S1). The OR indicates that for every meter increase in the distance from the coast, the likelihood of AIV presence decreases by 0.06%. This decrease in likelihood was 0.08% for wetlands (Table 2). Accounting for both fixed and random variables, the R 2 for the full model was 0.86. For the active surveillance data, only city was significant (p < .01, OR = 0.9822, Table 2); the OR indicated that for every increase in the area of city (in units of 100 m 2 ), the likelihood of AIV decreased by 1.78%. We selected the final model that included area of city and area of coast as variables, because this model was not significantly different from the full model (no significant differences in the residual SS, p <.05, same R 2 and a reduction in AIC, Table 2, Figure S1). R 2 was 0.52 for the final model. In the wild bird AIV surveillance data, we found  Figure S1) and we observed a smaller-AIC value and no change in the R 2 . The OR for area of coast indicates that for every unit the area of coast increases (here unit is 100 m 2 ), the likelihood of AIV presence increases by 0.08%. For area of city, the OR indicates that for every increase in a unit area of city (unit is 100 m 2 ), the likelihood of AIV presence decreases by 1.13%. R 2 was 0.43 when both fixed and random variables were included. Detailed results for all mixed GLMs are shown in Table 2, and an overview of the data used and the final GLMs are shown in Figure S1.  Figure S6). Although the spatial autocorrelation was weak, these results indicate that we did not account for all of the spatial variation within the data.
We ran the LOOCV for the passive AIV surveillance data on

Negative
Positive as two land cover types were only found once in the dataset. The LOOCV produced an accuracy of 0.91 when using the default threshold value of 0.5 for classification (probability of AIV presence above 0.5 is classified as a presence, whereas anything below or equal to 0.5 is classified as an absence). However, this accuracy equalled the proportion of AIV negative observations in the data, meaning that the model was not better than predicting all observations to be AIV negative. Hence, the model sensitivity was 0 and the specificity was 1, meaning that none of the positive observations were classified as positive. We could change the threshold to obtain a higher sensitivity (which would then lower the specificity), but we were not able to obtain an accuracy higher than the proportion of absences (0.91). Thus, predictions for this model should be viewed with caution. For the full passive AIV surveillance model, we used the Corine 1 km 2 land cover data and the coast-and wetlandsdistance rasters to predict the probability of AIV throughout Denmark. We set the random effects to zero to predict over all the years and the months. We found high-risk areas along the coast and around the fjords (Figure 5a). We also used the active AIV surveillance model to predict the probability of AIV presence based on postal code level area of coast and area of city.
We selected the active AIV surveillance model-rather than the wild bird surveillance model, as the sensitivity was higher, thus predicted positive postal codes were more likely to be correctly classified in this model than in the wild bird surveillance model.
Again, we set the random variables to zero to predict over all the years, months and postal codes. Here we also found the highest probabilities of AIV presence in postal codes with coastline or along fjords (Figure 5b).
For the game bird release data, we found no significant association with distance to bird release site, bird species released or number of birds released in the passive AIV surveillance data (Table 2).
We also did not find any significant association with number of releases and total number of birds released in the active and wild bird AIV surveillance data (Table 2). , 2006-2020 (red) and random locations in Denmark (blue) in relation to population density, distance to nearest city (≥200 inhabitants/km 2 ), distance to coast and distance to nearest road. All x-axes have been truncated to omit low density observations. As the kernel density calculations replace each observation by a small probability density, negative values around observation zeroes will occur

| Cluster analysis
The SatScan analysis detected several significant clusters for the passive, active and wild bird AIV surveillance data. For all the AIV surveillance data, hotspots were mostly found in the southern parts of Denmark (Southern Zealand, Lolland/Falster and Funen), whereas cold spots were found in northern Zealand and Jutland (see summarised results in Figures 1, 3 and 4). For the individual years, not all years had detectable clusters or the amount of data were insufficient to perform cluster analysis (See Figures S7, S8 and S9 and  Table S1). The summarized presence/absence data used for the cluster analyses are shown in Table S1.  (Bragstad et al., 2007), an outbreak that occurred in several European countries and caused the EU TA B L E 2 Mixed logistic GLM results for passive, active and wild bird AIV surveillance data. The Corine land cover variable is not shown for the full passive model, as this factor variable had over 20 classes, none of which were significant. The ANOVA p-values are from comparing the reduced model to the full model. The R 2 -values depicted are Nakagawa and Schielzeth's R 2 for mixed models from the MuMIn package (Barton, 2009) in R 3.5.2 (R Development Core Team, 2018. These values show the R 2 for fixed variables only as well as the R 2 for fixed and random variables combined. Abbreviations are explained in the footnote  We found that the passive AIV surveillance data were biased regarding the geographical location of sample sites. The majority of recorded locations were within 35 km of a larger city and within 500 m of roads. Public access to Danish beaches might also explain numerous records close to the coast, suggesting that accessibility to wildlife areas biases the Danish passive surveillance data. However, passive surveillance is not easy to control as it depends on the willingness and efforts of the general public. Implementation of information campaigns can be of great assistance to reinforce sampling in areas with sparse information or hotspots, and would be a valuable contribution to the ongoing surveillance programme.

| D ISCUSS I ON
For the passive AIV surveillance data, we found that distance to coast and distance to wetlands were significantly associated with the presence of AIV. For the active AIV surveillance data, we furthermore found an association with the area of coast and the area of city. Other studies have found effects of landscape variables and anthropogenic factors on AI presence in both wild and domestic birds. In Thailand, Paul et al. (2010) found a positive effect of free grazing ducks, high rice-cropping intensity areas, densely populated areas, short distances to a highway junction, and short distances to large cities on AIV presence in poultry. Gilbert et al. (2008) identified duck abundance, human population density, and rice cropping intensity as risk factors in South East Asia. In Romania, Ward et al. (2008Ward et al. ( , 2009 found associations between distance to migratory waterfowl sites, distance to major roads and distance to rivers or streams and HPAI outbreaks. Using a machine learning (ML) approach, Belkhiria et al. (2018) found spatial risk areas for AIV in wild birds in California, where land cover and distance to coast were some of the most im- transmission of AIV to wild birds. Our results could also be explained by a lack of data from other years, as we could only perform our analyses for the years 2018−2020.
Our cluster analyses identified several hot and cold spots for AIV presence within Denmark. We generally found hotspots in the southern parts of Denmark, whereas cold spots were found in northern Zealand and Jutland. The southern parts of Denmark lie on the main migration routes of duck and geese (Bregnballe et al., 2003), and the Wadden Sea along the south western coast of Denmark is also a well-known stop-over for migratory birds (Lotze, 2005). Thus, it is surprising that we found no hotspots in the western part of the country. This could be due to biased sampling, as only few people venture into the Wadden Sea region, and dead birds are quickly washed away. It could potentially also be due to the origin of migrating birds in the different regions of Denmark (for example from Siberia, which is known to be a hot spot for the dispersal of AIV, Lai et al., 2016;Li et al., 2014). However, as Denmark is embedded in the East Atlantic Flyway, with many different migrating birds of different origins (Bregnballe et al., 2003;Lotze, 2005), it can be difficult to determine these origins as there is no precise information on migration routes within the country.
Our predictive maps of AIV in Denmark identified high-risk areas located around the coast and fjords in Denmark. This suggests that any potential risk-based surveillance in wild or domestic birds should be concentrated in these areas, particularly high-risk areas that are not extensively covered in the present Danish AIV surveillance, such as the coast and Fjords in northern Jutland. The cluster analysis found hotspots in the southern parts of Denmark, areas that our predictive maps also highlight as being high-risk.
These areas should also be included in risk-based surveillance.
Knowing which parts of Demark constitute high-risk areas for potential AIV introduction might aid the selection of sites for new poultry facilities. Organic-or free-ranging poultry farms-where the farmed birds can come into contact with wild birds-are of particular concern and any location of such farms in high-risk areas should be avoided. It is important to note that although we did not divide any of our analyses into AIV subtypes, the majority of subtypes in the passive AIV surveillance data belonged to the HPAI types, whereas the majority of subtypes in the active AIV surveillance data belonged to the LPAI types (Table 1) More comprehensive studies and analysis demand more consistent sampling and a stratified sampling scheme for the future surveillance of AIV.

E TH I C S S TATEM ENT
The authors confirm that the ethical policies of the journal, as noted on the journal's author guidelines page, have been adhered to. No ethical approval was required as this article does not use original research data, but data obtained through the Danish authorities.

ACK N OWLED G EM ENTS
This work was carried out as part of the ROFUS project, a veterinary contingency project funded by the Danish Veterinary and Food Administration and the Erasmus Staff Mobility Program (Simulating avian influenza spread in Denmark: An industry-based decision support system).

CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data is subject to confidentiality and is not freely available.