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Received: 9 November 2021 Revised: 21 February 2022 Accepted: 1 March 2022
DOI: 10.1111/tbed.14504
ORIGINAL ARTICLE
Targeting the search of African swine fever-infected wild boar
carcasses: A tool for early detection
Alberto Allepuz1Mark Hovari2Marius Masiulis3,4,5Giovanna Ciaravino1
Daniel Beltrán-Alcrudo2
1Department of Animal Health and Anatomy,
Universitat Autònoma de Barcelona (UAB),
Barcelona, Spain
2Food and Agriculture Organization (FAO),
Regional Office for Europe and Central Asia,
Budapest, Hungary
3Emergency Response Division, State Food
and Veterinary Service, Vilnius, Lithuania
4National Food and VeterinaryRisk
Assessment Institute, Vilnius, Lithuania
5Dr. L. Kriauceliunas Small Animal Clinic,
Veterinary Academy, Lithuanian Universityof
Health Sciences, Kaunas, Lithuania
Correspondence
Alberto Allepuz, Department of Animal Health
and Anatomy, UniversitatAutònoma de
Barcelona (UAB), Barcelona, Spain.
Email: alberto.allepuz@uab.cat
The views expressed in this information
product are those of the author and do not
necessarily reflect the views or policies of FAO.
(Mark Hovari and Daniel Beltrán-Alcrudo)
Abstract
This study analyses the temporal and spatial distribution of found dead African swine
fever (ASF)-positive wild boar carcasses from 2017 to January 2021 in affected
European countries: Bulgaria, Estonia, Germany, Hungary, Latvia, Lithuania, Romania,
Poland, Serbia and Slovakia. During this period, a total of 21,785 cases were confirmed
in 19,071 unique locations. The temporal analysis of aggregated cases per month evi-
denced that most countries located in southern latitudes showed a higher number of
cases between January and April, whereas in northern latitudes there was no clear
temporal pattern. The space–time K-function evidenced a space–time clustering in
the ASF-positive wild boar carcasses, which was most prominent within distances of
2 km and within 1 week. A Bayesian hierarchical spatial model was calibrated to eval-
uate the association between the probability of finding ASF-positive wild boar car-
casses and landscape factors (i.e. the presence of a path and paved road), land use and
wild boar abundance. Results showed the highest likelihood of finding ASF-positive
wild boar carcasses in areas of transition between woodland and shrub, green urban
areas and mixed forests. The presence of a path and a higher abundance of wild boar
also increased slightly the odds of finding an ASF-positive dead wild boar. In summary,
this paper aims to provide recommendations to design a search strategy to find ASF-
infected wild boar carcasses, which is a crucial activity in the management of the dis-
ease, not just for surveillance purposes (i.e. the early detection of an introduction and
the regular monitoring to understand the epidemiology and dynamics), but also for
control, namely the disposal of infected carcasses as a virus source.
KEYWORDS
African swine fever, early detection, epidemiology, surveillance, wild boar
1INTRODUCTION
African swine fever (ASF) is a disease that affects all members of the
Suidae family. The disease was first identified in 1921 and since then
it has been circulating mainly in Sub-Saharan Africa. In 1957 and 1960,
ASF virus of genotype I arrived to Europe (Spain and Portugal) and then
spread to other European countries (Sánchez-Vizcaino et al., 2013).
The disease was eradicated from Europe in 1995 with the exception
of the Italian island of Sardinia, which remained endemic (Sauter-Louis
et al., 2021). In 2007, ASF genotype II was introduced into Georgia
from where it spread gradually westwards until it reached the Euro-
pean Union (EU) in early 2014, namely Lithuania and Poland (Maˇ
ciul-
skis et al., 2020). Since then, multiple countries in Europe, but also Asia
and America have been affected by genotype II with outbreaks in wild
TransboundEmerg Dis. 2022;1–11. © 2022 Wiley-VCHGmbH 1wileyonlinelibrary.com/journal/tbed
2ALLEPUZ ET AL.
boar, domestic pigs or both. The persistence of the disease in wild boar,
the lack of an effective vaccine or treatment, and the high case-fatality
rate represent a serious challenge for the global pig sector. At present,
biosecurity, movement control and the stamping out of animals are the
only tools to fight the disease in domestic pig farms (Sánchez-Cordón
et al., 2018; EFSA et al., 2021).
Finding ASF-positive wild boar carcasses is a crucial activity in the
management of the disease, not just for surveillance purposes, but also
for control, namely the disposal of infected carcasses as a source of
virus. When it comes to ASF surveillance and early detection in wild
boar, it has been repeatedly proven that sampling and testing found
dead wild boar is much more efficient than testing hunted wild boar
or road kills, even when the later may intuitively seem more conve-
nient. This is explained because the vast majority of wild boar that get
infected with the ASF virus will die within days, leaving a very short
time window of opportunity to detect the virus in a healthy-looking ani-
mal, i.e. whether incubating animals or the few that survive the infec-
tion. Moreover, as soon as wild boars start presenting clinical signs,
they tend to hide and rest, which largely prevents them from being
hunted. This has very important implications when trying to find the
disease in wild boar, both in already infected countries that try to
understand the epidemiology and evolution of the disease, but partic-
ularly in newly infected countries, where early detection is critical for
having a chance at successful control. The active search of carcasses in
countries or regions at high risk of ASF, for example, across the border
from infected areas, is the most efficient way to early detect the intro-
duction of the disease into ASF-free wild boar populations.
Wild boar that have died of ASF represent a continuous source of
infection for other animals, as the virus might remain infectious in the
carcass for an extended period of time, depending on the environmen-
tal conditions. It has been reported that a frozen carcass can maintain
infectious ASF virus for several months enabling the virus to overwin-
ter and to initiate a new outbreak when the defrosted carcass is vis-
ited the following spring by a susceptible wild boar or free-ranging pig.
Therefore, the safe removal of carcasses from the environment and
their disposal is an important measure to avoid ASF spread by reduc-
ing the local maintenance of the virus (FAO, 2019). The EU developed
an ASF strategic approach to prevent and control the spread of the dis-
ease and eventually to eradicate ASF from the EU. One of the compo-
nents of this strategy is finding, testing and disposal of ASF-infected
carcasses (Anonymous, 2020). Optimizing the search (and disposal) of
ASF-infected carcasses should contribute to the eradication of the dis-
ease.
However, there are few studies that have attempted to identify in
which areas it is more likely to find ASF-infected carcasses. Similarly,
there are currently no instructions or recommendations on where to
look for the dead wild boar. The objective of this study was to describe
the temporal and space–time distribution of ASF-positive wild boar
carcasses reported from 2017 to January 2021 in Europe and to iden-
tify those landscape factors that increase the likelihood of finding these
carcasses. This will in turn enable optimization ASF surveillance efforts
and strategies.
2MATERIAL AND METHODS
2.1 Study area and origin of data
The area of study included the following European countries: Bulgaria,
Estonia, Germany, Hungary, Latvia, Lithuania, Romania, Poland, Ser-
bia and Slovakia. Data were provided by the national competent vet-
erinary authorities and covered all ASF-positive wild boar carcasses
found dead, excluding hunted animals and road kills. To ensure that
data would be comparable in terms of their quality, spatial resolution
and level of detail, only countries reporting to the EU’s Animal Dis-
eases Information System (ADIS) were chosen. In fact, all eligible coun-
tries under such criteria (i.e. reporting to ADIS and with ASF outbreaks
in wild boar) were selected, with the exception of Belgium and the
Czech Republic, where, due to the small areas initially affected by ASF,
surveillance was particularly intense as compared to other countries.
In fact, both countries managed to contain the disease and eventually
eradicate it and regain freedom. The Italian island of Sardinia was also
excluded because of the different genotype involved.
Data included all eligible events in the target countries. Many coun-
tries revised their data collection and only since 2017 they started to
provide precise geo-coordinates for each found dead wild boar. There-
fore, the data analysed for countries already affected at the time starts
in 2017.
2.2 Covariates included in the model
OpenStreetMap data from the area of study was downloaded from
Geofabrik (https://www.geofabrik.de/). Paths correspond to those cat-
egorized as paths or bridleways in OpenStreetMap. Paved roads
included secondary, tertiary and unclassified roads, as well as those
with an agricultural use. Water included both water lines (i.e. rivers and
streams) and water bodies (i.e. reservoirs or wetlands). Land use was
extracted from the Corine Land Cover map, created by the European
Environment Agency under the European Union’s Earth Observation
Programme, named Copernicus [©European Union, Copernicus Land
Monitoring Service, 2018, European Environment Agency (EEA)]. This
map has a resolution of 100 ×100 m grid and was last updated in 2018.
For our study, we used the 44 classes included on level 3 that corre-
sponded to five main land use groups: artificial surfaces, agriculture,
forests and semi-natural areas, wetlands and water bodies. Wild boar
abundance was retrieved from the ENETWILD consortium (2020)ata
resolution of 2000 ×2000 m grid.
2.3 Data management
Data were projected into ETRS89-extended/LAEA Europe. For each
point in which a wild boar had been found dead, the distance to the
nearest path, paved road, water line or water body, was calculated by
creating a SpatiaLite database for each of these layers. A structured
ALLEPUZ ET AL.3
query language (SQL) query was created among them to extract dis-
tances. For spatial modelling purposes, a buffer of 2000 m was created
around each location in which a wild boar was found dead. The area
covered by the buffer was divided into a grid of 500 ×500 m. The loca-
tions of ASF-positive wild boar carcasses were superimposed on this
grid and cells that intersected with those points were classified as pos-
itive and otherwise, negative. Similarly, paths and paved roads were
superimposed on this grid to identify if they were present in each of
the grid cells. Water lines and water bodies from OpenStreetMap were
not superimposed on this grid, as this land use was already present in
the Corine Land Cover map data (i.e. level 3 classes: water courses and
water bodies). The zonal statistic plugin was used to obtain the maxi-
mum and most frequent values of wild boar abundance and land use,
respectively, in that 500 ×500 m grid. All these analyses were done
with Quantum GIS 3.18 (QGIS Development Team, 2021).
2.4 Temporal analysis and space–time analysis
The forecast library (Hyndman & Khandakar, 2008; Hyndman et al.,
2021) in R was used to describe the temporal trend of the number of
ASF-positive wild boar carcasses between January 2017 and January
2021. To construct the time-series dataset, we used the date when car-
casses were confirmed to be infected by ASF by the national reference
laboratories. Dates were aggregated by month. The number of cases
per month along the different years, and the number of cases found
each month in the whole study period were described.
The space–time K-function, as described by Diggle et al. (2015) was
used to describe the excess of risk that could be attributed to an ASF-
positive wild boar carcass as a function of distance and time. In case of
no space–time clustering (i.e. when cases occur independently in space
and time) the K-function at each distance and each temporal increase
is equal to the product of the K-function in space and the one in time.
The multiplication of the difference between the observed K-function
in space and time by the product of the space and time K-functions is
called the proportional increase in risk or excess of risk due to the pres-
ence of space–time interaction. Using the splancs R package, we calcu-
lated this value over a space–time grid of 5 km times 2 months using
intervals of 500 m and 1 week, respectively. To illustrate any elevated
disease risk attributable to space–time interaction this value was plot-
ted as a surface over a space–time grid.
2.5 Spatial model
A Bernoulli distribution was used to model the probability of find-
ing ASF-positive wild boar carcass in each grid cell. The logit trans-
formation was used to link such probability with specific explanatory
variables. A backward and forward stepwise procedure based on the
Akaike information criterion (AIC) was used to select the best model.
Once the best model was selected, it was extended by adding ran-
dom spatially structured and unstructured components. The spatially
structured random effect was defined by a stochastic partial differ-
TAB LE 1 Distribution by country and year of the data used in the
study, i.e. African swine fever-positive wild boar found dead in target
countries between January 2017 and January 2021
Country 2017 2018 2019 2020 2021 Total
Bulgaria 6 68 980 15 1069
Estonia 185 12 1 6 204
Germany 372 130 502
Hungary 155 2038 4444 57 6694
Latvia 774 269 31 83 6 1163
Lithuania 1065 1035 197 57 2354
Poland 738 2415 2470 2692 8315
Romania 125 385 682 50 1242
Serbia 27 84 111
Slovakia 21 110 131
Total 2762 4017 5211 9453 342 21,785
ential (SPDE; Lindgren et al., 2011) and calculated from a matrix of
Euclidean distances between grid centroids using Delaunay triangu-
lation (Cameletti et al., 2013; Simpson et al., 2011). This model was
solved by using the R-INLA package (Schrödle & Held, 2011). To assess
the association of the variables included in the model with the prob-
ability of finding ASF dead wild boar in a grid, 95% credible intervals
(CR) were obtained from the exponential of the mean, 2.5% and 97.5%
percentiles of the posterior probability distribution of the regression
coefficients. We considered a variable to be associated if the proba-
bility was over 95%, that is, if the 95% CR was greater or lower than
1. If greater, the variable increased such probability, and if lower, it
decreased it.
To validate the ability of the model to discriminate between grids in
which it was more likely to find wild boars dead due to ASF, the sta-
tus (i.e. the classification of a grid as positive or negative) was removed
from 30% of randomly selected grid cells. In those cells, the status
was predicted by the model and compared with their original value by
means of a receiver operating characteristic (ROC) curve constructed
using the pROC package (Robin et al., 2011) in R. The area under that
curve (AUC) is related to the performance of the model. AUC values
greater than .8 and between .7 and .8 are indicative of good and mod-
erate discriminate capacities respectively.
3RESULTS
3.1 Descriptive, temporal and space–time
analysis results
Tabl e 1andFigure 1show the number and location of ASF-positive wild
boars found dead in target countries between January 2017 and Jan-
uary 2021.
During this period, a total of 21,758 cases of ASF-positive in wild
boar carcasses were confirmed in 19,071 unique locations (i.e. in some
4ALLEPUZ ET AL.
FIGURE 1 Location of the data used in the study, i.e. African swine fever-positive wild boar found dead in target countries between January
2017 and January 2021
cases, several animals were reported in the same coordinates). The
year with more detected cases was 2020. Poland, followed by Hungary,
detected the most positives.
The number of ASF-positive wild boar carcasses per month between
2017 and 2021 in the target countries included in this study can be
found in Figure 2.
The temporal pattern of each country was heterogeneous. Despite
this apparent heterogeneity, when plotting cumulative cases per
month in the whole period of study (Figure 3), there was a pattern
whereby countries in southern latitudes (i.e. Bulgaria, Hungary, Roma-
nia and Serbia) showed a higher number of cases from January to April,
with the exception of Romania, where there was also a high number of
cases in November–December. On the other hand, there was no clear
temporal pattern for countries in northern latitudes (i.e. Estonia, Latvia,
Lithuania or Poland), with the exception of Poland, where the numbers
of cases were slightly higher in winter.
Figure 4shows the plot of the proportional increase in risk from
the space–time K-function. This plot evidences the existence of space–
time clustering in the data, which translates in an increase in risk, which
is most prominent within 2000 m and within 1 week.
3.2 Spatial model results
Tabl e 2shows the distance between ASF-positive wild boar carcasses
to the nearest path, paved road, water line or water body.
Twenty five per cent of carcasses were found within 755, 161 and
298 m of a path, paved road or water line/body, respectively. Tables 3
and 4show the most frequent land use in each grid cell together with
the presence of a path or paved road and the abundance of wild boar
according to the presence or not of ASF-positive wild boar carcasses in
them.
Green urban areas, transitional woodland-shrub areas, mixed and
broad-leaved forests and sport/leisure areas were the land uses with
a higher proportion of positive grid cells. Wild boars were only slightly
more abundant in those grids in which dead ASF-positive wild boar had
been found (versus those without).
Odds ratio and their 95% credible intervals (CI) for each of the risk
factors from the hierarchical Bayesian model together with the random
effects are presented in Table 5.
Among land use categories, considering non-irrigated arable lands
as the baseline, the model showed the highest likelihood of finding
ASF-positive wild boar carcasses in areas of transition between wood-
land and shrub, green urban areas and mixed forests, with odds ratios
around 3 times higher. The presence of a path and a higher abundance
of wild boar also increased slightly the odds of finding an ASF-positive
dead wild boar. On the other hand, the presence of paved roads was
not retained in the model as it did not influence the likelihood of find-
ing ASF-positive carcasses.
For model validation, we randomly selected 30% of cells in which
we removed the ASF status and estimated the area under the ROC
(receiver operating characteristic) curve (i.e. AUC). Results showed
ALLEPUZ ET AL.5
FIGURE 2 African swine fever-positive wild boar found dead in target countries per month (January 2017 to January 2021)
FIGURE 3 Number of African swine fever-positive wild boars found dead in target countries per month. Germany and Slovakia are not shown
due to the low temporal frame for which cases have been found
6ALLEPUZ ET AL.
FIGURE 4 Proportional increase in risk due to space–time clustering with the K-function. The elevated surface illustrates the excess in risk for
finding African swine fever-positive dead wild boar
TAB LE 2 Distance (in meters) from each African swine
fever-positive wild boar carcass to the nearest path, paved road, water
line or water body
Min Q1 Median Q3 Max
Path 0 755 2136 4376 22,634
Wate r*0298 887 1759 17,374
Paved 0 161 394 800 8049
Min: minimum; Q1: first quartile; Q3: third quartile; Max: maximum.
*The shortest distance to a water line or water body.
77% (IC95%: 69.4%–84.2%), indicative of a model with a moderate
capacity to discriminate between ASF-positive and ASF-negative grid
cells (Figure 5).
4DISCUSSION
Early detection is of paramount importance to contain any outbreak. It
applies to all transboundary diseases and to both livestock and wildlife.
Two key control measures recommended for ASF in wild boar are the
active search of dead wild boar and the subsequent disposal of infected
carcasses. Results from our study might contribute to increase the effi-
ciency of the search of infected carcasses by allowing to target those
areas in which it is more likely to find ASF-positive dead wild boars.
Results from this study showed that some landscape factors (and wild
boar abundance to a lesser degree) increased the likelihood of find-
ing ASF-positive wild boar carcasses and could therefore be used to
map those areas that should be prioritized to search for them. In the
Czech Republic, Cukor et al. (2020) also attempted to identify those
factors linked to the location in which ASF-positive wild boar carcasses
were found. In their study, they determined that most ASF-infected
carcasses were found in forest and especially in young forest areas.
These results were explained by the fact that wild boars may choose
such areas to die, since they offer silence, cover and lower densities
of other animal species. Similarly, our model also showed higher odds
of finding ASF-infected carcasses in certain forests (i.e. mixed, broad-
leaved or coniferous) and areas of transition between woodlands and
shrub, which consist of young plants. Moreover, studies conducted in
Poland have also identified woodlands as areas with a risk of ASF occur-
rence (Podgórski et al., 2020). Therefore, these types of land use should
be targeted in the search of ASF-infected carcasses.
ALLEPUZ ET AL.7
TAB LE 3 Most frequent land use and presence of a paved road or path in grid cells where African swine fever-positive wild boar carcasses
were found (i.e. positive), versus neighbouring cells in which they were not found (i.e. negative)
Variable Category Pos Neg Proportion (%)
Land use Green urban areas 16 128 11.1
Transitionalwoodland-shrub 1107 14,224 7.2
Mixed forest 2197 30,485 6.7
Broad-leaved forest 3677 54,526 6.3
Sport and leisure facilities 37 566 6.1
Water courses 52 1012 4.9
Coniferous forest 1790 35,995 4.7
Inland marshes 82 1827 4.3
Land principally occupied by agriculture, with
significant areas of natural vegetation
626 14,608 4.1
Vineyards 66 1567 4
Discontinuous urban fabric 450 11,063 3.9
Pastures 1133 30,659 3.6
Mineral extraction sites 11 312 3.4
Industrial or commercial units 45 1295 3.4
Fruit trees and berry plantations 101 2991 3.3
Complex cultivation patterns 325 11,752 2.7
Natural grasslands 62 2262 2.7
Non-irrigated arable land 2868 114,139 2.5
Freq_lowa15 842 2.3
Water bodies 79 3817 2
Rice fields 9 463 1.9
Peat bogs 12 851 1.4
Paved Yes 6322 144,437 4.19
No 8439 190,946 4.23
Path Yes 1726 21,207 7.53
No 13,035 314,176 3.98
aLand uses with less than 200 observations in the dataset were grouped in this category.
TAB LE 4 Descriptive statistics on wild boar abundance in grid cells with and without African swine fever-positive wild boar carcasses
NMean SD Min Q1 Median Q3 Max
Positive (with) 14,841 54.8 24.7 3.7 36.2 47.2 67.8 181.3
Negative (without) 339,186 49.3 22.9 2.7 33.8 41.9 58.8 181.3
Searching near water courses or water bodies has also been rec-
ommended, as infected wild boar, when developing clinical signs such
as fever and dehydration, search for humid environments and water
(Podgórski et al., 2020). Indeed, Cukor et al. (2020) described that
around 60% of ASF-infected carcasses were found up to 100 m from
water sources. However, we did not find such a clear association, as
only 25% of the ASF-positive carcasses were found within 298 m from
water sources. The association with distance to water might be influ-
enced by other factors, such as temperature. The probability of find-
ing an ASF-infected carcass near water might be higher during the hot-
ter periods of the year, when animals need more drinking water and
cooler resting places, often associated to water sources. Perhaps also
the abundance of water (streams and rivers) is important and animals in
more arid areas may tend to remain closer to the water. Consequently,
the recommendation for searching near water sources might depend
on the period of the year and on the land uses present in the target area.
Other landscapes such as green urban areas (OR of 3.0) and sport
and leisure facilities (OR of 1.5), or the presence of a path in the grid
8ALLEPUZ ET AL.
TAB LE 5 Fixed and random effects included in the hierarchical Bayesian model, odds ratio (OR), standard deviations (SD) and their 95%
credible intervals (CI)
OR SD
Credible
2.5%
Credible
97.5%
Land useaTransitionalwoodland-shrub 3.1 0.0492 2.8 3.4
Green urban areas 3.0 0.3670 1.4 6
Mixed forest 2.9 0.0395 2.7 3.2
Broad-leaved forest 2.5 0.0377 2.3 2.7
Inland marshes 2.4 0.1449 1.8 3.2
Coniferous forest 2 0.0444 1.8 2.2
Land principally occupied by
agriculture, with significant
areas of natural areas
2.0 0.0567 1.8 2.2
Water courses 1.7 0.1877 1.2 2.5
Natural grasslands 1.6 0.1604 1.1 2.1
Pastures 1.5 0.0456 1.4 1.7
Rice fields 1.5 0.3857 0.7 3.1
Sport and leisure facilities 1.5 0.2503 0.9 2.4
Mineral extraction sites 1.5 0.3725 0.7 2.9
Industrial or commercial units 1.4 0.1865 1 2
Vineyards 1.4 0.1718 1 1.9
Complex cultivation patterns 1.2 0.0746 1.1 1.4
Discontinuous urban fabric 1.2 0.0702 1.1 1.4
Fruit trees and berry plantation 1.0 0.1441 0.8 1.4
Water bodies 0.9 0.1466 0.7 1.3
Freq_lowb0.8 0.3072 0.4 1.4
Peat bogs 0.8 0.3890 0.3 1.5
PathcPath presence 1.1 0.0414 11.2
WBq2e1.1 0.0377 1.1 1.2
Wild boars abundancedWBq3e1.3 0.0390 1.2 1.4
WBq4e1.3 0.0440 1.2 1.4
Coefficients SD
Random effects Spatial-structured random
effect
720.9 0.956
Non-spatial structured random
effect
0.0106 0.007
aReference category was ‘non-irrigated arable land’.
bLand uses with less than 200 observations in the dataset were grouped in this category.
cReference category was ‘path absence’.
dReference category was the first quartile.
eWBq2, WBq3 and WBq4 refer to the second, third and fourth quartile of the wild boar abundance distribution.
(OR of 1.1), were also highlighted by the model as areas where it is more
likely to find ASF-infected carcasses. Probably these results respond
to the higher human activity, which implies that any wild boar carcass
will most likely be quickly found, rather than a predilection of wild
boar for those areas. An association between human population den-
sity and the number of reports of ASF-positive carcasses has indeed
been reported elsewhere (Lim et al., 2021). Therefore, and despite ASF-
positive wild boar carcasses might be found in these areas, these land-
scapes should probably not be targeted to search for ASF-infected car-
casses, since they are already indirectly found by people passing by, but
rather increase the incentives of the public to report found dead wild
boar. Citizen science and mobile application easing such public report-
ing can assist detection efforts. Accordingly, a participatory workshop
with different experts in the field (Jori et al., 2020) highlighted that
good communication and transparent information directed to the pub-
lic was a powerful tool for improving passive surveillance against ASF.
ALLEPUZ ET AL.9
FIGURE 5 Receiver operating characteristic (ROC) curve to test the ability of the model to discriminate between positive and negative African
swine fever grid cells in the 30% of randomly selected cells in which their status against ASF was removed. AUC: area under the curve
The size of the infected area is another important factor that influ-
ences the search of wild boar carcasses once ASFV has been confirmed
in an area. The size of the infected area may vary greatly, which makes
the targeted search for dead wild boar very demanding in terms of time
and human resources. The minimum size of the infected area should
be defined based on the geographical distribution of the disease, the
wild boar population in the area and the presence of major natural or
artificial obstacles to the movements of wild boars. Therefore, it can
vary from a few square kilometres to even an entire country. Since
the search is time and resource consuming, it is critical to define the
area and period of time in which such search should be performed, to
optimize the most likely time and location. The space–time analysis
evidenced that after the first detection of an ASF-infected wild boar
in an area, the probability of finding ASF-positive carcasses was higher
up to 2 km and over the following week. This combination will offer
the best effort-success ratio. Indeed, many ASF-affected countries
have guidelines for the search of newly infected areas that recommend
searching for at least 30 days and focusing on the wild boar feeding
and resting places or water sources. The reasoning behind the 1 week
temporal pattern might be explained by the fact that wild boar are
social animals who live in groups. Most times, several members of the
same group will become infected by ASF at about the same time. This
implies they will all be dying clustered at approximately the same time
(i.e. 1 week) and around the same area (2 km radius). Combining the
search in this spatial and temporal frame (focusing on the landscapes
identified by the model) with other methods such as the use of hunting
dogs (Jori et al., 2020) or drones might also maximize the probability
of carcass detection.
As highlighted by the human density factor mentioned above, it is
important to stress that this model does not always point to the areas
with more ASF-positive wild boar carcasses, but rather at the places
where such carcasses are more easily found, for example, close to
paths, in areas often visited by people, and where vegetation is lower
and/or thinner, thus allowing for a better visibility. While efforts were
done to utilize only data of high quality, by targeting countries that
all collect and report data with precise geo coordinates and the same
10 ALLEPUZ ET AL.
reporting standards/requirements (AIDS), there are a number of biases
that are difficult or often impossible to avoid. Perhaps the most impor-
tant bias relates to the nature of wild boar as a wildlife species, that
is, the fact that they live freely, in unknown numbers and densities
and without movement restrictions. This implies that finding their car-
casses when they die of ASF or any other diseases is a challenging pro-
cess that translates in a high (but variable) degree of under-reporting,
which will depend on the search effort (whether active or passive), but
also on the type of land (e.g. how accessible it is or how thick is the veg-
etation). These will vary greatly between and even within countries.
Efforts were taken when selecting the targeted countries, by avoid-
ing countries with very intensive search effort like the Czech Repub-
lic or Belgium. The limited fenced infected area in these two countries
allowed a clear shot at eradication (as it indeed happened), which trans-
lated in an active search of carcasses that probably lead to the detec-
tion of the majority of existing ASF-positive carcasses in the area. On
the other hand, countries with less resources and no economic incen-
tives for the reporting of carcasses were also excluded from the study
(i.e. most countries outside the EU, except for Serbia), as the under-
reporting is considered to be more severe than in study countries.
Wild boar management is another important factor, for example, the
type of hunting (driven or not), the ban of supplementary feeding, the
level of hunting biosecurity,the awareness and cooperation of hunters,
the magnitude of (economic) incentives to report, etc. All these differ
between and even within countries and affect the way ASF spreads
in wild boar population and the chances of finding wild boar. Finally,
ecological and climatic factors will also affect the wild boar popula-
tions, not just in their abundance (which was accounted through the
use of wild boar abundance variable), but also their movement pat-
terns, behaviour and interactions. Factors related to the disease also
need to be accounted for. Although all countries are affected by the
same genotype (II), there are various strains circulating (Nurmoja et al.,
2017), and different levels of endemicity, which translate in different
clinical presentations, lethality and other epidemiological parameters.
Also, the ASF status in domestic pigs (which may allow the disease
to jump back and forward between domestic and wild populations)
and other epidemiological factors cannot be excluded as potential
biases.
5CONCLUSION
Finding ASF-positive wild boar carcasses is a crucial activity in the man-
agement of the disease, not just for surveillance purposes (i.e. the early
detection of an introduction and the regular monitoring to understand
the epidemiology and dynamics), but also for control, namely the dis-
posal of infected carcasses as a source of virus. This study, based on
thousands of observations, can be translated into very practical appli-
cations in the early detection of ASF in wild boar populations. This is
key to havea chance at the control and eradication of the disease in wild
boar populations, which is otherwise extremely difficult and resource-
consuming. Results pointed that efforts to find (and remove) additional
ASF-positive wild boar carcasses after a confirmed case should be
devoted up to 2 km and over the following week. In addition, the model
allows to generate search maps or strategies for wild boar carcasses,
which focus on the areas with a higher likelihood to find an ASF-positive
wild boar carcass. Rather than covering whole territories, both the gen-
eration of maps and the subsequent search efforts should be based on
risk assessment approach. Results also helps emergency preparedness
to make better simulation exercises for ASF in wild boar, by aiding to
better determine where dead wild boar might be found.
For free countries, the mapped areas should be those at a higher
risk for ASF introduction, for example, border areas or specific hunt-
ing grounds. For infected countries, the rapid finding and subsequent
disposal of ASF-positive wild boar carcasses is one of the key recom-
mended measures to reduce the viral load in the ecosystem, which will
eventually translate in less spread of the disease and even its control
and eradication.
Easier than generating risk maps is the standardization of search
parameters. Already described within the paper, just providing the key
risk factors to hunting ground managers is a simple, yet powerful tool to
focus search efforts where there are more chances of success, that is,
finding an ASF-positive wild boar carcass. The most important factors
identified by the model are (in order of importance):
1. Transitional woodland-shrub
2. Mixed forest
3. Broad-leaved forest
4. Inland marshes
5. Coniferous forest
6. Land principally occupied by agriculture
7. Water courses
8. Natural grasslands
9. Pastures
10. Rice fields
11. Vineyards
12. Areas with the highest wild boar density
When trying to find carcasses around an already confirmed ASF-
infected wild boar, active searches should take place within 1 week
after the event and in a 2 km radius, focusing in those areas in which
is more likely to find them.
ACKNOWLEDGEMENTS
The authors acknowledge the veterinary services from the different
countries for providing outbreak data. Acknowledgements also to the
Food and Agriculture Organization of the United Nations (FAO), which
financed the study through a Technical Cooperation Project facility
(TCPf) titled “Services related to the risk assessment and cost-benefit
analysis of African Swine fever in Europe and beyond Ukraine”. The
views expressed in this information product are those of the authors
and do not necessarily reflect the views or policies of the Food and Agri-
culture Organization.
ETHICS STATEMENT
No animals were used in this project.
ALLEPUZ ET AL.11
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request
from the corresponding author. The data are not publicly available due
to privacy or ethical restrictions.
ORCID
Alberto Allepuz https://orcid.org/0000-0003- 3518-1991
Marius Masiulis https://orcid.org/0000-0003- 2779-5803
Giovanna Ciaravino https://orcid.org/0000-0002-5796-8093
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How to cite this article: Allepuz, A., Hovari, M., Masiulis, M.,
Ciaravino, G., & Beltrán-Alcrudo, D. (2022).Targeting the
search of African swine fever-infected wild boar carcasses: A
tool for early detection. Transboundary and Emerging Diseases,
1–11. https://doi.org/10.1111/tbed.14504
