<caption>**Table 4**</caption>

Descriptive statistics of the pig trade reported by 66 Corsican pig farmers in 2013 for the 5 years preceding the interview.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Purchase (# of farmers)</th><th colspan="2">Sale (# of farmers)</th></tr><tr><th>Sows</th><th>Boars</th><th>Fattening pigs</th><th>Sows and boars</th><th>Fattening pigs</th></tr></thead><tbody><tr><td>Trade of pigs</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>18</td><td>56</td><td>16</td><td>10</td><td>9</td></tr><tr><td>No</td><td>48</td><td>10</td><td>50</td><td>56</td><td>57</td></tr><tr><td>Location of pig source/destination<sup>a</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Neighbours(&lt;=10 km)</td><td>0</td><td>5</td><td>2</td><td>1</td><td>4</td></tr><tr><td>Corsica</td><td>17</td><td>45</td><td>2</td><td>9</td><td>5</td></tr><tr><td>Continental France</td><td>1</td><td>6</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Abroad</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>No answer</td><td>0</td><td>0</td><td>12</td><td>0</td><td>0</td></tr><tr><td>Source of pigs/destination<sup>b</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Farm</td><td>18</td><td>56</td><td>15</td><td>8</td><td>9</td></tr><tr><td>Trader holding</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td></tr><tr><td>Market</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Animal breeding centre</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>Number of sources/destination</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1</td><td>6</td><td>24</td><td>12</td><td>6</td><td>5</td></tr><tr><td>Between 2 and 6</td><td>12</td><td>32</td><td>4</td><td>4</td><td>4</td></tr></tbody></table>

### 3.1.6. Farm contacts through people and vehicles

The people that had most contacts with pig herds were, in order of decreasing importance, veterinarians and animal health workers, other pig farmers, castrators, tourists and students from agricultural schools (Table 3). Veterinarians and animal health workers visited the farms between one and eight times a year (median: 2, IQR: 1–2), while castrators, tourists and students visited the farms only once a year. None of the farmers interviewed reported visits by butchers or dealers.

The equipment most shared between farmers were trucks to transport the pigs to the slaughterhouse (21.2%), health equipment (1.5%) and trucks to transport feed (1.5%). None of the farmers interviewed reported sharing a truck for the transport of slurry or tagging equipment.

### 3.1.7. Characteristics of the two farms with pigs raised indoors

The management practices of the two farms (one backyard, one industrial), on which pigs were reared indoors, were quite different from those of farms on which pigs were reared outdoors. The backyard farmer bought fattening pigs from a farmer located in continental France in spring, did not sell any pigs, fed the pigs with purchased industrial food and slaughtered the pigs himself for home-consumption in winter. The industrial-scale farmer purchased reproductive pigs from an animal breeding centre located in continental France and produced Duroc piglets which he sold all year round to about 50 Corsican farmers. Being a breeder, he rarely sent pigs to slaughterhouses himself. The industrial food was delivered at least once a month to his farm, but the delivery truck never entered the pig pens. Both farmers called veterinarians or animal health workers to take care of the pigs, did not spay pigs kept for fattening, and did not share any equipment with other farmers.

### 3.2. Multiple factor analysis

The backyard and the industrial farms were included as supplementary individuals as they were assumed to have outlier characteristics, so they did not influence the results of MFA. Seven variables were excluded from the analysis: pork processing (97.1% of positive answers), keeping the pigs outdoors (97.1%), feeding pigs with leftover food (swill) (1.5%), visits by castrators (4.4%), visits by dealers (0.0%), visits by butchers (0.0%), and type of fences used (because 21% of the farmers did not

answer the question). Twenty-nine variables remained in the dataset, of which nine were introduced as supplementary variables.

Three factors were chosen from the scree plot which explained 34.6% of the variance of the dataset. Ranked in decreasing order of importance, the management practices which differentiated the farms were: stock management, reproduction management, slaughter, animal waste and carcass management, pasture and food management, and to a lesser extent, contacts with people and vehicles (Fig. 3).

Factor 1 was mainly influenced by factors related to stock management (purchase of reproductive pigs and sale of fattening pigs, $p \le 10^{-4}$), reproduction management (location of mating, spaying sows, farrowing period, $p \le 10^{-4}$), and to the structure of the farm (the breed reared, involvement in a quality label, $p \le 10^{-4}$). Factor 2 was mainly related to the practice of on-farm slaughtering ($p \le 10^{-4}$), what was done with animal waste and carcasses ($p \le 10^{-4}$), the season feed was delivered ($p \le 10^{-4}$), and the geographical area ($p = 0.001$). Factor 3 was mainly associated with pasture management (sharing pastures, type of enclosure, use of summer pastures, $p \le 10^{-4}$; pasture area, $p = 0.0004$), and contact with people and vehicles (visits from other farmers, sharing equipment, $p \le 10^{-4}$; visit by health workers, $p = 0.005$).

### 3.3. Hierarchical cluster analysis

Four clusters were identified (Table 1–3, Fig. 4). The clusters are presented below in order of increasing level of risk of the introduction and transmission of infectious diseases.

Cluster 1 mainly comprised farmers who reared pigs in completely fenced pastures which were not shared with other farmers, and who did not use summer pastures. Supplementary feed was provided all year round, and mating occurred in fenced areas. These farmers rarely or never slaughtered their own pigs on their farm, they deposited animal waste and carcasses in specific areas, and did not receive visits from other farmers.

Cluster 2 mainly comprised farmers involved in a quality label, who used the “Nustrale” breed, and who purchased or exchanged boars. Their pigs were kept in shared pastures, some of which were only partially fenced or were unfenced, and whose fattening sows were castrated. Like the farmers in cluster 1, they rarely or never slaughtered their animals on the farm, they deposited animal waste

<footnote><sup>a</sup> Most remote source/destination.</footnote>

<footnote><sup>b</sup> A farmer could have more than 1 type of supplier/buyer.</footnote>
<figure><img src="image_58.png" /><figcaption>**Fig. 3.** The 6 groups of variables projected on the three first factors of the MFA performed on 66 Corsican pig farms (DEAD: management of slaughter, animal waste and carcasses; FOMI: contact with people and vehicles; PAST: pasture and feeding management; REPRO: reproduction management; STOC: stock management; STRU: structure of the farm; the percentage in brackets gives the amount of variance captured by each factor).</figcaption></figure>

<figure><img src="image_59.png" /><figcaption>**Fig. 4.** The pig farms and their clusters identified by HCA on the three first factors of the MFA (points represent pig farms, and the distance between them is an indication of their similarities in terms of management practices).</figcaption></figure>

and carcasses in specific areas, but were visited by animal health workers and by other pig farmers with whom they shared equipment.

Cluster 3 mainly comprised farmers who were not involved in a quality label, who mainly reared crossbred pigs in partially fenced or unfenced areas, and supplied them with feed only in summer. Farmers in this group frequently slaughtered their own animals on their farm and left animal waste and carcasses in the field. They did not receive visits from animal health workers and their feed was not delivered to the farm. These farms were mainly located in Castagniccia, and pig rearing was their primary source of income.

Cluster 4 comprised farmers who reared “Corsican-type” pigs in large pastures and used summer pastures. They were not involved in a quality label. They did not purchase or exchange boars, mating occurred in unfenced areas, births occurred all year round, and fat-

tening sows were not castrated. No other pig farmers visited their farm; pig feed was delivered to their farm.

## 4. Discussion

Identifying ways to prevent and control the spread of infectious diseases requires a broad understanding of farmers' practices. While several authors have investigated biosecurity and management practices in industrial pig farms (Hurnik et al., 1994; Boklund et al., 2004; Casal et al., 2007; Ribbens et al., 2008), few data are available on traditional production systems, and they mainly come from non-European countries (Costard et al., 2009; Alawneh et al., 2014) or are based on the observation of a limited sample of farms (Albarella et al., 2007; Hadjikoumis, 2012). The data collected for the present study came from almost a quarter of Corsican registered
pig farms, most of whose owners practice extensive pig produc-
tion. Therefore, it provides new insights into the management of pig
farms in a traditional Mediterranean extensive system, the Corsican
sylvo-pastoral system, including detailed data on practices which
could help to better understand and predict the potential spread of
infectious pathogens.

As the inclusion of farms was based on the voluntary partic-
ipation of farmers, a selection bias was probably introduced. An
"informal" economic activity based on pig products exists in Cor-
sica, characterized by the slaughtering of pigs on the farm and
not in official slaughterhouses, thereby preventing routine san-
itary inspection. Richomme et al. (2007) and Richomme (2009)
estimated that more than 50% of pigs were slaughtered on the
farm in Corsica. Therefore, farmers whose practices are "illegal"
may have refused to take part in the survey, or may even not be
registered as pig farmers. The sampling strategy was designed to
include 20% of active farmers in each production area. However, due
to the practical difficulties we encountered organizing interviews
during the time allocated to the study, the number of farms sam-
pled was particularly low in two production areas, Alta-Rocca and
Plaine Orientale. More interviews in these areas would be useful to
enhance our results. Finally, the farms were not drawn randomly
so the practices described in this paper cannot be assumed to be
representative, but rather as a good overview of the practices of
registered pig farmers in Corsica, given the relatively large number
of farmers interviewed and the fact that only few farmers refused
to take part to the study.

A surprisingly large number of irregular practices were reported
by farmers, such as dumping of carcasses in the field or on-farm
slaughtering. This highlights the value of face-to-face interviews
with semi-structured questions, which allow farmers to talk more
freely, and gives them time to express their own points of view.
On the other hand, the face to face method increased the time
needed to visit the farmers, thus limiting the number of farmers
who could be interviewed. The interviewers may also have unin-
tentionally influenced the responses, and interviewer bias cannot
be completely excluded.

The abundant and detailed data collected on management prac-
tices revealed some interesting patterns for the risk of introduction
and spread of diseases. Contrary to more intensive types of pig pro-
duction, the main potential sources of interaction between Corsican
pig farms who practice extensive production might not be trading
animals (Fèvre et al., 2006; Bigras-Poulin et al., 2007), which is lim-
ited in Corsica, or transmission through fomites (Elber et al., 1999;
Ribbens et al., 2004). Corsican pig farmers seldom reported that
their pigs came into contact with visitors or vehicles, compared to
data available concerning industrial pig production (Nielen et al.,
1996; Ribbens et al., 2009). Feed delivery vehicles never entered
pig pens, which should limit the spread of infectious diseases.

Instead, there is a risk of local spread of infectious diseases in
the pastures through, interactions with wild boars, the exchange
of domestic boars and inappropriate management of carcasses and
animal waste. Wild boars are extremely common and widely dis-
tributed in Corsica (Richomme et al., 2010), and have been shown
to play an important role in the maintenance and dissemination
of major infectious diseases like African swine fever and bovine
tuberculosis (Richomme et al., 2010; Khomenko et al., 2013). In
Corsica, pigs reared in extensive pastures tend to form bands with
a sow and her family and have their own territory (Casabianca and
Maestrini, 2001). The extent of interactions between domestic pigs
and wild boars and between domestic pigs from different herds
in pastures is not really known and further research is needed to
better characterise these interactions.

Fencing the entire territory used by a pig herd is one possible
way to limit these interactions, but would be very difficult to imple-
ment for technical and legal reasons. Indeed, most areas used to

feed the pigs in autumn and winter are very large, the land is located
in a mountainous environment, it provides food for other domestic
and wild animals, and usually belongs to many different own-
ers (Casabianca, personal communication). Thus, other solutions
are needed to limit contacts between herds. Good management
of reproduction, as well as management of carcasses and animal
wastes appear to be key factors in limiting interactions between
pig herds as well as between wild boars and pigs herds in Corsican
pastures.

The presence of sows in oestrus in pastures may increase the
risk of contacts between herds and between wild boars and domes-
tic pigs. In extensive production systems in Spain and Portugal,
sows that are being fattened are spayed by veterinarians or trained
technicians to limit interactions due to sows in oestrus (Fredriksen
et al., 2009; Hadjikoumis, 2012). In Corsica, sows are mainly spayed
by individual farmers who have taught themselves the technique
and provide this service to the neighbouring farmer community.
Keeping domestic boars enclosed, using properly-fenced areas for
mating, and calling on veterinarians or trained technicians to spay
females could be useful ways of limiting the local spread of infec-
tious diseases in Corsica.

The interviews also suggested lack of organisation and limited
encouragement to collect carcasses and animal waste, leading to
different practices depending on the ease of collecting carcasses
and the perceived risk of disease spread. Indeed, farmers commit-
ted to a quality label and farmers who raise pigs in small relatively
flat pastures were more likely to destroy the carcasses they found
than the other farmers.

Feeding pigs with swill has been reported to be a common
way of introducing exotic diseases, including foot and mouth dis-
ease, Aujeszky disease, CSF and ASF (Wittmann and Rziha, 1989;
Leforban and Gerbier, 2002; Ribbens et al., 2004). In the present
study, only one farmer reported giving restaurant leftovers to his
pigs. This figure seems surprisingly low compared to some indus-
trialised pig production areas where almost 8% of farmers reported
feeding waste food to their animals (Ribbens et al., 2008). One
possible explanation for this low figure is that farmers may have
under reported this practice as it is banned by European legislation
(European Union, 2002). However, many farmers expressed the
opinion that this could alter the taste and the quality of processed
meat, which could also explain its low frequency.

Another interesting feature of pig management in traditional
extensive systems is the seasonality of production and pig demo-
graphics. This may influence the dynamics of infectious diseases:
outbreaks of infectious diseases with a short latent period are more
likely to occur in early summer, when pig density increases, and
are less likely at the end of winter, when many adult pigs have
been slaughtered, as described in the case of ASF in the Russian
Federation (Khomenko et al., 2013).

The present study also provides useful information for the
design of surveillance and control strategies for infectious diseases.
It revealed that a non-negligible proportion of farmers who still reg-
ularly practice on-farm slaughtering for various reasons, including
the difficulty to get to a slaughterhouse. On-farm slaughtering is
permitted in France if the pigs have been reared on the farm for
domestic consumption (Code rural et de la pêche maritime, 2015),
however, this was not the case for most of Corsican farmers who
reported on-farm slaughtering in this study. Disease surveillance
which is only conducted in slaughterhouses may underestimate the
prevalence of some diseases, particularly in areas where slaughter-
houses are difficult to reach, as already reported in Richomme et al.
(2007) and Richomme (2009). Our study also highlighted the fact
that a mountainous environment may delay the discovery of dead
animals, and the subsequent reporting of an outbreak.

HCA results revealed the different strategies used by Corsi-
can farmers to rear pigs, which may have a different impact on
the spread of infectious diseases. Two clusters (clusters 3 and 4) were close to previously described traditional extensive production (Casabianca and Maestrini, 2001; Albarella et al., 2007), in which the farmer rarely intervenes during rearing (semi-open pastures, supplementary feed only provided in summer), and pays no particular attention to animal waste and carcasses. In such systems, the limited contact between farmers and their pigs may increase the time it takes to detect diseases. Farmers in cluster 4 did not control reproduction, which may increase the risk of disease transmission through sexual contacts, while in cluster 3, the frequency of on-farm slaughter may delay the discovery of signs of disease (assuming the farmers do not detect or report lesions), and animal wastes left in the field may increase the risk of disease spread in pastures.

The two other clusters (1 and 2) reflect the emergence of a more actively organised way of rearing pigs, targeting the production of high quality cured meats (genetic selection of the breed, supplementary feeding, on-farm slaughter is rare, correct disposal of carcasses and animal waste), but with different choices concerning pasture management: sharing pastures to keep pigs in semi-open pastures (cluster 2), or not shared and fully fenced pastures (cluster 1). These farmers may more rapidly detect and report outbreaks of an infectious disease. On the other hand, they may be more at risk of coming into contact with disease through animal trade and fomites.

The region of production, the age and the experience of farmers tended to be associated with the choice of a strategy. Farmers in Castagniccia tended to adopt the “traditional” way of rearing (cluster 3), while the younger and less experienced farmers tended to adopt quality production with fully fenced enclosures (cluster 1). Other factors such as education, the availability and accessibility of pastures (mountainous vs. relatively flat areas) may also have influenced the choice of a farm management strategy, but these factors were not investigated in this study.

As the type of production was not documented in the sampling frame, it was not possible to stratify the sampling based on it. This led to a limited representation of some types of production, such as backyard production, in which pigs are reared for home consumption, or a more industrialised type of production. These types of farms, even if less frequent, may play an important role in the introduction and spread of infectious diseases in Corsica. Backyard farms in Corsica could risk introducing diseases through the purchase of pigs, but not risk spreading disease, as observed for backyard farms in other European countries (Martinez-Lopez et al., 2013). Conversely, Corsican industrial pig farms may be at risk of spreading infectious disease regionally through the trade in pigs. It is thus important to ensure that farmers of this type are made aware of the need to recognise and rapidly report any suspected infectious disease. More interviews need to be conducted in these types of farms to check if management practices are similar to those used in other farms with the same particular types of production in Corsica, as well as in other countries, particularly backyard farms and small producers, whose practices may differ between countries and between continents, but are little documented (Costard et al., 2009; Beltran-Alcrudo, 2012; Alawneh et al., 2014). The owner of the industrial farm we interviewed was, to our knowledge, the only industrial farm in Corsica at the time of the study.

The aim of the present study was to better characterize contacts between pig herds, and contacts between domestic pigs and stray dogs, and other domestic and wild animals (cattle, foxes, etc.) were not been investigated. These contacts could play an important role in the dissemination of some infectious diseases like trichinellosis or foot and mouth disease (Sutmoller et al., 2003; Richomme et al., 2007). Further studies should be conducted to quantify the risk of these particular interactions for Corsican pig herds.

## 5. Conclusion

The present study of the management of extensive pig production in Corsica increases our knowledge on traditional management practices in Mediterranean Europe, and points to possible patterns for the introduction and spread of pathogens in such systems. The traditional extensive systems analysed in our study, together with the mountainous Corsican environment, increase the risk of local transmission of pathogens within and between herds in pastures through the movement of free-ranging boars and the presence of carcasses and animal waste in the environment. However, not all Corsican pig herds are exposed to the same level of risk. In fact, the decision by some farmers to produce high quality certified products appears to have reduced the risk of transmission through the progressive implementation of improved reproductive and feeding management procedures and the physical separation of pig herds through fencing. Nevertheless, certain management practices continue to maintain a very high risk of spread of infectious diseases between pig herds in Corsica. Considering the proximity of the island to current foci of ASF in Sardinia, awareness campaigns among pig stakeholders will be necessary to improve surveillance levels and to reduce risky practices.

## Acknowledgments

The research leading to these results received funding from the European Union, Seventh Framework Programme [FP7/2007–2013] under grant agreement no 311931 (ASFORCE). The authors acknowledge the farmers who participated in the project and the Corsican Animal Health services (GDS 2A, GDS 2B, Corsica, France) for providing initial data. Many thanks also to Paul Deminati (Pig producer advisor, Bastelicaccia, France), and Agnès Waret-Szkuta (Assistant Lecturer, swine production and pathology, Toulouse Veterinary School, France) for their assistance during the initial phases of the project, and to Jean-Charles Sicard (Statistician, CIRAD, Montpellier) for help with the statistics.

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1.2. Questionnaire-based assessment of wild boar/domestic pig interactions and implications for disease risk management in Corsica [#2]

Référence: [#2]

Jori, F., Relun, A., Trabucco, B., Charrier, F., Maestrini, O., Chavernac, D., Cornelis, D., Casabianca, F., Etter, E. M. C. (2017). Questionnaire-based assessment of wild boar/domestic pig interactions and implications for disease risk management in Corsica. *Frontiers in Veterinary Science*, *4*, 1-14.
<figure><img src="image_60.png" /></figure>

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# Questionnaire-Based Assessment of Wild Boar/Domestic Pig Interactions and Implications for Disease Risk Management in Corsica

Ferran Jori¹,²*, Anne Relun¹,²,³, Bastien Trabucco¹,⁴, François Charrier⁴,
Oscar Maestrini⁴, David Chavernac¹,², Daniel Cornelis¹,², François Casabianca⁴
and Eric Marcel Charles Etter¹,²,⁵

¹CIRAD, UMR Animal, Santé, Territoires, Risque et Ecosystèmes (ASTRE), Montpellier, France, ²ASTRE, Univ Montpellier, CIRAD, INRA, Montpellier, France, ³BIOEPAR, ONIRIS, INRA, Nantes, France, ⁴INRA, Laboratoire de Recherches sur le Développement de l'Elevage (LRDE), Corte, France, ⁵Epidemiology Section, Department of Production Animal Studies, Faculty of Veterinary Science, University of Pretoria, Pretoria, South Africa

OPEN ACCESS

**Edited by:**
Francisco Ruiz-Fons,
Spanish National Wildlife
Research Institute (IREC),
Spanish Research Council, Spain

**Reviewed by:**
Anette Boklund,
Technical University of
Denmark, Denmark
Hans-Hermann Thulke,
Helmholtz-Zentrum für
Umweltforschung (UFZ), Germany
Kurt VerCauteren,
United States Department of
Agriculture, United States

*Correspondence:
Ferran Jori
ferran.jori@cirad.fr

**Specialty section:**
This article was submitted to Veterinary Epidemiology and Economics, a section of the journal Frontiers in Veterinary Science

Received: 29 June 2017
Accepted: 03 November 2017
Published: 01 December 2017

**Citation:**
Jori F, Relun A, Trabucco B, Charrier F, Maestrini O, Chavernac D, Cornelis D, Casabianca F and Etter EMC (2017) Questionnaire-Based Assessment of Wild Boar/Domestic Pig Interactions and Implications for Disease Risk Management in Corsica. *Front. Vet. Sci.* 4:198.
doi: 10.3389/fvets.2017.00198

Wild boars and domestic pigs belong to the same species (*Sus scrofa*). When sympatric populations of wild boars, feral pigs, and domestic pigs share the same environment, interactions between domestic and wild suids (IDWS) are suspected to facilitate the spread and maintenance of several pig pathogens which can impact on public health and pig production. However, information on the nature and factors facilitating those IDWS are rarely described in the literature. In order to understand the occurrence, nature, and the factors facilitating IDWS, a total of 85 semi-structured interviews were implemented face to face among 25 strict farmers, 20 strict hunters, and 40 hunting farmers in the main traditional pig-farming regions of Corsica, where IDWS are suspected to be common and widespread. Different forms of IDWS were described: those linked with sexual attraction of wild boars by domestic sows (including sexual interactions and fights between wild and domestic boars) were most frequently reported (by 61 and 44% of the respondents, respectively) in the autumn months and early winter. Foraging around common food or water was equally frequent (reported by 60% of the respondents) but spread all along the year except in winter. Spatially, IDWS were more frequent in higher altitude pastures were pig herds remain unattended during summer and autumn months with limited human presence. Abandonment of carcasses and carcass offal in the forest were equally frequent and efficient form of IDWS reported by 70% of the respondents. Certain traditional practices already implemented by hunters and farmers had the potential to mitigate IDWS in the local context. This study provided quantitative evidence of the nature of different IDWS in the context of extensive commercial outdoor pig farming in Corsica and identified their spatial and temporal trends. The identification of those trends is useful to target suitable times and locations to develop further ecological investigations of IDWS at a finer scale in order to better understand diseases transmission patterns between populations and promote adapted management strategies.

**Keywords:** Sus scrofa, wild boar, pig farming, Corsica, disease management, contacts, transmission, human practices
# INTRODUCTION

Outdoor pig farming is becoming a widespread activity in many European countries driven by public demand for more ethical and natural approaches to produce pigs and a higher quality of pork products. In the French island of Corsica, pig production of indigenous breeds raised in extensive pastures and forests (chestnut and oaks) for the processing of high quality dry cured meats is a traditional activity. The processed pork products obtained through this type of farming are highly ranked among tourists and French consumers. This type of production has important socio-economic, ecological, and cultural benefits and is being promoted to revert to traditional farming practices in Corsica.

The natural and vast pig farm environment is often shared with an abundant population of wild boars, feral pigs, and hybrids resulting of cross breeding between these different forms of *Sus scrofa* that cohabitate in the Corsican Mediterranean forests. Hunting is extremely popular in the island and practiced by approximately 17,000 licensed local hunters and 250 hunting teams who regulate wild boar populations with an estimated annual offtake of 30,000 (1, 2). Such socio-ecological context provides numerous opportunities for direct (synchronous) or indirect (asynchronous) interactions between wild/feral and domestic suids (IDWS), which are known to be responsible for the maintenance and transmission of many infectious diseases circulating between wild and domestic compartments such as Hepatitis E virus, bovine tuberculosis, or trichinellosis (3-5). Equally, Aujeszky disease, has been eradicated from domestic pigs in continental France, but remains present in Corsican herds as result of IDWS (6). Last but not least, IDWS are reported to contribute to the maintenance of African swine fever in the neighboring island of Sardinia, which has remained endemic for more than 35 years (7, 8). However, despite their suspected epidemiological importance, no studies have ever analyzed the characteristics, drivers, or patterns of occurrence of IDWS in the framework of a complex Mediterranean socio-ecosystem (9). Knowledge on the spatial and temporal patterns of IDWS can be used to target preventive disease management measures and to reduce the risk of transmission more efficiently at specific times and locations.

Several ecological or laboratory methods have been used to assess interactions between wild and domestic animals such as telemetry (10, 11), camera trapping (12), or biomarkers (13, 14). In addition, the collection of local knowledge by interviews with stakeholders has been reported as a useful and cost effective tool to collect qualitative and quantitative information on events that are otherwise difficult to observe (15-17). Questionnaires have proven useful to assess potential contacts between wildlife and livestock in different parts of the world (17-19). They have been successfully used to assess IDWS in Switzerland (20), and more recently in Uganda (21).

In this study, we implemented questionnaires in the Corsican socio-ecological context of outdoor pig farming with the specific aims of (a) describing the nature, frequency, duration, and seasonality of IDWS, (b) identifying their seasonal and spatial drivers, and (c) identifying potential hunting and farming practices that could facilitate or reduce their occurrence.

# MATERIALS AND METHODS

## Study Area

Corsica is located off the western shores of the Italian peninsula, 11 km north of the Italian island of Sardinia. Its territory, divided in the two Corsican Provinces (North and South Corsica), is sparsely populated (32 inhabitants/km²) and its economy is mainly based on services closely linked with tourism (22). Pigs are mostly reared in mountainous and semi-mountainous areas, called “Pieve” (“diocese” in Corsican language) which include the 14 main pig production areas distributed within 6 major pastoral areas with an average size of 557 km² (23). In the eastern part of the island, the valleys reach a plain (“Plaine orientale”) dominated by more intensive pig production. The highland habitats in the center form a single chain of 21 summits reaching more than 2,000 m (6,600 ft) above sea level (a.s.l.). The slope of the terrain varies significantly from area to area. In order to produce typical Corsican ham, pigs are left roaming in this mosaic of oak and chestnut forests in autumn, whereas in summer, pigs are traditionally kept in often unfenced grass pastures and beech forests found in altitude (24). The pasture vegetation reflects the influence of both Mediterranean and mountain climates, with shrubs (“maquis”), a mixture of rapidly growing evergreen herbs, bushes, and small trees, holm oak (*Quercus ilex*), cork oak (*Quercus suber*), and olive trees up to 600 m, chestnut trees between 600 and 1,000 m, and mostly grass and beeches above 1,000 m a.s.l.

## Study Design

A cross-sectional study was conducted between March and October 2013 in the 14 main extensive pig production areas (Figure 1), which encompass 287 extensive farms registered in 50 Corsican municipalities. The sampling frames were based on two databases facilitated by (i) the Animal Health Associations (“Groupements de Défense Sanitaire”) and (ii) the Corsican Hunters Federation, respectively, and consisted of a list of all registered commercial outdoor pig farms (372 properties) and 17,000 licensed hunters.

Considering the time and resources available for the study, we estimated that 70 pig farmers and 20 hunters could be interviewed. The distribution of the farmers in the island was stratified according to the importance of 14 main extensive pig-farming areas within the two Corsican Provinces (55% in Southern Corsica and 45% in Northern Corsica). In each production area, extensive farmers were randomly chosen from the lists provided and contacted by phone to check if they were still active and would be willing to take part in a face-to-face interview on farming practices and IDWS. Farmers were classified as hunting farmers (HF), if they dedicated at least 2 days a week to hunting activities and had a hunting license or as strict farmers (SF) if they had little or no hunting activity. In addition, a sample of 20 hunters (not farmers) operating in the same municipalities as the farmers was randomly chosen from the Hunters Federation list in order to obtain responses from farmers and hunters in the same municipalities.

## Data Collection

The selected farmers and hunters were subjected to a semi-open face-to-face questionnaire interview. The questionnaire was
<figure><img src="image_61.png" /><figcaption>**FIGURE 1** | Two maps of Corsica showing the 14 main outdoor pig production areas in the island (left) and the spatial distribution of the interviewed stakeholders (right).</figcaption></figure>

designed to reveal stakeholders’ knowledge and perception of IDWS and to collect data on their herd management or hunting practices that could influence IDWS (copy of the questionnaire available).

All stakeholders were asked to recall their field observation of proximity of wild pigs to pig estates and of direct IDWS events, as well as their herd management or hunting practices within the previous 12 months.

For field observation of direct (i.e., synchronous) IDWS events, stakeholders were asked to recall specifically on mating activities between domestic and wild pigs (hereafter referred as sexual IDWS), fighting between domestic and wild pigs (agonistic IDWS), and foraging together around common food or water (trophic IDWS). They were asked to specify the duration of each events, their annual frequency and seasonality, and to give some additional information on the circumstances of such IDWSP (for example, did you observe mating between wild boar and a domestic sow or the inverse?).

To avoid confusion, we defined wild *Suidae* as the term encompassing any animal living in the wild without an owner, including pure wild boars, feral pigs, and hybrids. We defined a feral pig as a domestic pig that escaped its original farming premises, had no owner, and roamed free without confinement. Hybrids were distinguished from “pure wild boars” on the basis of presence of phenotypical or behavioral indicators such as patches of white or clear coat, the shape of the ears, or their ability in confronting hunting dogs (25).

Farmers were asked about the number of domestic boars injured, and the number of hybrid litters in the previous 12 months, as well as on their herd management practices, focusing on reproduction, feeding, confinement, and disposal of carcasses, offal, and animal waste. Maps and a 2-year round calendar were used to

collect information on the location of pastures and neighboring farms and the production schedule for the whole year. Hunters were asked on their hunting practices, focusing on the type or intensity of hunting activity (number of hunting days per week, shooting feral pigs, or driven hunts).

The questionnaires were pre-tested with three pig farmers and three hunters and modified accordingly before being implemented in the field. The final version of the questionnaire was administered by two interviewers who were involved in its design. The interviewers took notes during the interview and the data were entered in a LimeSurvey database (26). Answers to open questions were coded according to the analysis methods used in human sciences (27).

## Ethics

To the best of our knowledge, the implementation of questionnaires among French citizens does not require a specific Ethics review process. Participation of hunters and farmers to the interviews was done on a voluntary basis after phone call contact. Participants were informed in advance about details of how the data would be used, assuring anonymity, and informed consent was obtained.

## Data Analysis

Data collected through the questionnaires consisted on a series of binary categorical or quantitative continuous variables describing the intensity and frequency of IDWS (non-specific, sexual, agonistic, and trophic) and a large number of human practices implemented by farmers (SF or HF), potentially influencing IDWS. For hunters, the number of practices was lower and linked to the type or intensity of hunting activity.
Four annual frequencies of IDWS (non-specific, sexual, agonistic, and trophic IDWS) and two quantitative variables (number of observations of wild boar near the farm and number of hybrid litters during the previous year) were used to assess the intensity of interactions per farm and compare those frequencies between stakeholder categories [strict hunters (SH), SF, or HF] during the previous 12 months to the interview. For hunters, we considered the same four annual frequencies of observation of IDWS, the rate number of hybrids/total number of suids shot, and the number of hunting days during the previous 12 months to the interview. Each indicator was scored as 1 (1-3 times), 2 (4-6 times), 3 (6-10 times), or 4 (>10 times), depending on the frequency of events reported by farmers and hunters during the previous 12 months. The sum of these four indicators was used to calculate an interaction index which was used to classify farms or hunting properties with a quantitative value ranging from 0 (lowest level of IDWS) to 16 (highest level of IDWS).

A preliminary selection of the variables linked to those practices was made by suppressing binary variables for which less than 5% of respondents gave a positive (or negative) answer or by suppression of variables for which at least 20% stakeholders did not provide any answer (28). Other variables from the questionnaire were removed due to lack of discrimination (all the same answer). After selection, analysis of farming practices

was performed on 34 selected variables grouped in 6 sets of data and analysis of hunting activities on IDWS was performed on 10 variables from the hunter's questionnaire (Table 1).

Quantitative values of frequency and seasonality of IDWS were calculated as medians and interquartile ranges (IQR), percentages and histograms of IDWS reported by month, and stakeholder categories. Potential differences in frequency, seasonality, or duration of reported IDWS by different observers (SF, SH, and HF) were assessed using the ANOVA test for inequality of means. Differences were considered significant when *p* values were lower than 0.05. Seasonal variations were detected by analyzing the frequency of observations across time in the questionnaire responses.

The geographical coordinates of the farming and hunting areas were not always available, but we could obtain the name of the municipality where each farmer and hunters was living. Therefore, we used this information as a proxy of farming area and hunting grounds. For mapping purposes, an average municipality IDWS index was calculated as the ratio between the sum of the interaction indexes per farm or hunting area/the number of interaction indexes observed in each municipality. Those IDWS municipality indexes were then manually classified with ARC-GIS, as high (10-6), medium (45-3), low (1-2), and null (0) in order to obtain equally represented categories (null 20%, low 22%,

<table><caption>**TABLE 1** | Presentation of the 30 variables selected from farmers and 10 variables selected from hunters on which principal component analysis and multiple component analysis was performed.</caption><thead><tr><td>Farmer characteristics</td><td>Pasture management</td><td>Reproduction management</td><td>Hunter characteristics and management</td><td>Carcass management</td><td>Interactions between wild suidae (IDWS)</td></tr></thead><tbody><tr><td>Farming is the main activity (Y/N)</td><td>Maximum surface of the outdoor area (&lt;50/&gt;50 ha)</td><td>Births during all the year (Y/N)</td><td>Number of pig farms in the neighborhood (quantitative)<sup>a</sup></td><td>Carcasses left outdoor (Y/N)</td><td>WB in proximity (seen in the farm/estate) (Y/N)</td></tr><tr><td>Pork produced as controlled designation of origin (Y/N)</td><td>Pastures are totally fenced (Y/N)</td><td>Mating in non-fenced areas (Y/N)</td><td>Practice of beat hunts (Y/N)<sup>a</sup></td><td>Offal left outdoor (Y/N)</td><td>WB/pig interaction (Y/N)</td></tr><tr><td>Size (&lt;100/&gt;100 ha)</td><td>Share of pastures with other pig farmers (Y/N)<sup>a</sup></td><td>Sterilization of sows (Y/N)</td><td>Number of hunting days/week (quantitative)<sup>a</sup></td><td>Home slaughtering (Y/N)</td><td>IDWS frequency (seen more than 4 times a year) (Y/N)<sup>a</sup></td></tr><tr><td>Breed "Nustrale" (Y/N)</td><td>Use of summer pastures (Y/N)<sup>a</sup></td><td>Reproductive stock sold (Y/N)</td><td>Shooting feral pigs (Y/N)<sup>a</sup></td><td></td><td>Sexual interactions (Y/N)</td></tr><tr><td>Cross bred with Corsican breeds (Y/N)</td><td>Additional food supply all year (condensed) (Y/N)</td><td>Fattening offspring sold (Y/N)</td><td>Ratio hunted hybrids/total hunted pigs<sup>a</sup></td><td></td><td>Agonistic interaction (Y/N)</td></tr><tr><td>Farming experience (&lt;20/&gt;20 years)</td><td></td><td></td><td>Farmer (Y/N)<sup>a</sup></td><td></td><td>Trophic interactions (Y/N)</td></tr><tr><td>Age (&lt;40, &gt;40 years)</td><td></td><td></td><td></td><td></td><td>Sexual interactions frequency (&gt;4 times) (Y/N)</td></tr><tr><td>Isolation of the farm (nearest other pig farm ≥10 km)</td><td></td><td></td><td></td><td></td><td>Agonistic interaction frequency (&gt;4 times) (Y/N)</td></tr><tr><td>Hunter (Y/N)</td><td></td><td></td><td></td><td></td><td>Trophic interaction frequency (&gt;4 times) (Y/N)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Presence of hybrids in the litters (Y/N)<sup>a</sup></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Keep the hybrids born in farm (Y/N)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Observation of wounds (&gt;1) on boars (Y/N)</td></tr></tbody></table>

<footnote><sup>a</sup>Variables used for the analysis of hunters practices in relation with IDWS.</footnote>
medium 34%, and high 25%). Similarly, assessed municipalities were equally classified by the average altitude in meters above sea level (m) in four categories ranging between <100, 100–350, 350–700, and >700 m. The association between those categories (IDWSP intensity vs municipality altitude) was measured using a simple Chi square test.

Multivariate exploratory data analysis including multiple
factorial analysis (MFA) and principal component analysis
(PCA) were conducted to identify farming and hunting prac-
tices correlated with each other or with the intensity of IDWSP
variables (outcome). Principal components (or dimensions)
were produced based on the linear combination of the variables.
The selection of the dimensions was based on a combination of
the Kaiser–Guttman criterion (eigenvalues >1) and the screen
test (29, 30). The relative importance of each component is
expressed by the variance (i.e., eigenvalue) of its projection or
by the proportion of the total variance expressed. Variables or
individuals are projected on planes which axes are represented
by these principal components. Close proximity of the variables
or individuals on one plane of the projections suggests that the
variables/individuals are positively correlated while position on
opposite quadrant of the plane suggests negative correlation.
At individual level, these analyses allow to assess the variability
or similarity between individuals considering simultaneously all
their variables and to look for potential clusters (31). Variables
used in the PCA were created using either categorical variables
(but reducing the categories to a maximum of three) or quan-
titative variables categorized using mode or thirtiles of their
distribution. Two or three categories were used in order to avoid
variables to overweight each other in the analysis (32, 33). Two
out of the 10 variables kept for the hunter's data analysis were
considered as supplementary variables. These two variables were
not used to build the principal components but were added on
the correlation circle afterward. Considering that some variables
of the farmer's analysis could be organized into groups, MFA was
preferred to PCA. MFA could be considered as a weighted PCA
where the influence of each group of variables was balanced in
the analysis.

To quantify the level of correlation between some variables relative to farming or hunting practices and some indicators of interaction identified in the PCA, we extracted the correlation matrix from the PCA. The correlation between two variables was expressed by a correlation coefficient (r) and was considered significant if the *p* value was lower than 0.05. The use of correlation coefficient even for categorical variables with two or more modalities is possibly due to the statistical relationship between correlation coefficient and the Chi² (33). Descriptive uni- and multivariate analysis were performed with R software version 2.15.3 using the package FactoMineR for explanatory data analysis (34).

RESULTS

A total of 85 persons from 56 different municipalities were inter-
viewed (Figure 1). Among those, 29.5% (n = 25) were SF, 23.5%
(n = 20) were SH, and 47% (n = 40) were at the same time farmers
and hunters (HF).

Pig Management Practices

Pig management practices from Corsican traditional farmers have been recently described in detail (35). “Nustrale” pigs, the selected and recognized local breed which is subject to selection programs were the most common breed, followed by the “Corsican-type” pigs, which refers to the original local non-selected pig population and cross-bred animals from different origins. Median time of experience for farmers was 25 years IQR [15–30]. The majority were farrow-to-finishers. The median herd size was 115 pigs [89–159] encompassing 10 adult sows [8–20], 2 boars [1–3], and 100 fattening pigs [89–159]. There was no significant difference between North and South Corsica with respect to herd size among SF or HF.

In relation to herd management, 100% (n = 65) of farmers (SF + HF) kept their pigs outdoors in areas with a median surface of 60 ha [20–700]. Producers mixed their herds in grazing areas in 49% cases (n = 32) and only 23% of farms were totally fenced (n = 15). The type of fence used by 86% of them (13/15) was a simple fence, while one farmer used a double fence and another one an electric fence. Among the 65 interviewed farmers, 17% moved pigs to high pastures in summer and left them free ranging in altitude for several weeks (n = 11). A total of 52% provided supplementary feed all year round (n = 34), 45% of them (n = 29) only in summer and three of them did not supplement food at all.

Around 15% of the farmers interviewed (10/65) reported that mating of sows occurred in free ranging natural conditions, while the others (85%) kept their reproductive females confined in outdoor specific paddocks for monitored reproduction. Most farrowing occurred from April to August. About half the farmers (47%) said that their sows farrowed once a year, mostly in spring and summer (39%) and a minority (8%) in autumn and winter. A similar proportion (42%) of farmers synchronized mating time so that farrowing could occur twice a year, once in the spring–summer season and once in the autumn–winter season. In a limited number of farms (11%; 7/65), farrowing was spread over the year. All farmers castrated their fattening males at weaning age (3 months). Of all farmers, 34% (22/65) also spayed reproductive sows of different ages (4–48 weeks) aimed for fattening, to avoid undesired mating with domestic or wild boars. Furthermore, 43% (28/65) of the farmers (including SF and HF), dumped carcass offal of domestic pigs in the environment. This was reported to occur mostly during the pig-butchery period (between November and April).

In addition, a non-negligible proportion of farmers slaug-
tered their animals at home (17%, 11/65) and not at the
slaughterhouse (83%). Among those, 81% of them reported leav-
ing carcass offal in nature without disposal. A limited number
of farmers (8%, 5/65) even reported feeding carcass left overs
directly to their pigs. Regarding management of dead animals,
47% (38/65) of the farmers transported the carcasses to a specific
area for disposal, 32% (21/65) reported leaving dead carcasses
outdoors without any management, while 13% (6/65) of the
farmers did not answer this question.

Thirteen farmers (12 HF and 1 SF) reported the deliberate use of supplementary entire males around the sows on pasture, as a specific strategy to mitigate sexual interaction with wild boar
(males are supposed to cover the sows and hence to avoid the heats attracting wild boars).

## Hunting Practices

The following results were extracted from the analysis of questionnaires including 20 SH and 40 HF that provided quantitative data on the IDWS. The median age of a hunter in our study was 53 [46–58] years. The median number of days per week dedicated to hunting during the hunting season (beginning of September to the end of February) was 2 [2–3]. The most widespread hunting method was the driven hunt, practiced by 88% of hunters. A hunting team was reported to encompass a median of 10 persons [8–15] and 15 dogs [8–20]. Hunting alone and/or from a hide were practiced by 30 and 16% of the hunters, respectively. The median number of wild pigs shot per hunting team per year was 100 individuals [65–137] encompassing 53% of suspected hybrid pigs and 47% of suspected pure wild boars. Feral domestic pigs were not a preferred target, being only shot occasionally by 25% of the hunters to control their population at a median range of 1–2 individuals per year. A large majority of the SH (85%) declared dumping hunted pig offal in the natural environment during the hunting season.

## Types and Occurrence of IDWS

The proportions of the different types of IDWS reported by each stakeholder category are presented in **Figure 2**. All the farmers reported having wild boars close (less than 500 m) from their farm area. More than 3/4 (51/65) of the farmers (including SF and HF) and a majority of hunters (SH) (18/20) reported having

seen wild boars within the premises of pig farms. Non-specific IDWSs were observed by 69% of the persons interviewed (52% of SF, 65% of SH, and 83% of HF). More than 2/3 of the interviewed individuals had observed some kind of IDWS (1–3 times per year in 40% of the cases, 4–6 times per year in 10% of the cases, and more than 7 times per year in 12% of the cases). Specific IDWS most commonly reported were sexual (61%, 52/85 interviewees), followed by trophic (47%, 40/85) and agonistic (43%, 37/85) IDWS.

## Sexual Interactions (Mating)

The observation of sexual IDWS were reported by 61% (52/85) of the respondents, encompassing 36% of SF, 65% of SH, and 75% of HF. In median, stakeholders observed sexual interactions 5 times [2–8] per year and differences between stakeholder categories were not significant. The median reported duration of courtship was 2.3 days [2–2.5] and the perceived duration was significantly higher among SH (p < 0.02). The median duration of a sexual intercourse observed was 5 [5–15] min and differences between stakeholders were not significant. Sexual IDWS were reported to occur when a domestic sow was mated by a wild boar or feral pig in the farm paddocks (63%) or in unfenced areas (32%), while wild or feral sows being sexually harassed by domestic boars in farm paddocks or outside was only reported by 9 and 1% of the farmers, respectively. More than half of the farmers interviewed (57%, 37/65) reported births of hybrid litters in their premises because of those sexual contacts. Those included eight farmers who had not reported the observation of sexual interactions, so the number of farmers reporting some

<figure><img src="image_62.png" /><figcaption>**FIGURE 2** | Proportion of responses given by the different categories of stakeholder interviewed (SH, SF, and HF), regarding the different kinds of interaction observed. SF, strict farmers; SH, strict hunters; HF, hunting farmers.</figcaption></figure>
kind of sexual IDWS (mating or hybrid litters) was 70% (60/85). The median number of hybrid litters reported/year in those 37 farms was 2 [1–4]. Among the 37 farmers who reported hybrid litters, 21 (57%) did not keep those hybrids, while 43% kept them for consumption (7/16) or sold them in the market (9/16).

## Agonistic Interactions (Fighting)

The observation of fights between wild and domestic boars when trying to mate with domestic sows on heat was reported by 44% (37/85) of the respondents, encompassing 20% of SF and 60% HF and 40% of SH. In addition, there were five farmers (3HF + 2SF) who reported wounds in domestic boars because of those fights, so the total number of respondents having observed some kind of agonistic interaction was 49% (42/85). The median reported frequency was 2.5 times/year [2–4] and the median reported duration of those boar fights was 1 h [0.4–2]. In both cases, measures provided by different stakeholders were not significantly different (Table 2). In 84% of the observed fights, wild boar succeeded in chasing their domestic opponents away. The occurrence of wounds resulting from bites during fighting was reported by 62% (23/37) of the farmers who observed fighting interactions. The median annual frequency of observation of boar wounds was 2.5 [2–4] per farm.

## Trophic Interactions (Foraging)

Wild and domestic suidae were reported to share foraging sites by 47% (40/85) of the persons interviewed, including 24% of SF, 53% of HF, and 65% of SH. This was reported to be observed three times per year [1–3] and the interaction could last for a median of 7 days [2–75].

## Seasonality of IDWS

In terms of seasonality, most of the interactions were observed in the autumn months, with a peak of observation in November. Observations from SH coincided with the autumn months while SF mostly reported interactions on other seasons (Figure 3).

Sexual IDWS were observed mostly (70%) in autumn (September to December) and 30% in winter (January to March). Fights were also reported to occur in autumn, and mostly in November coinciding with the period of estrus of the sows. Trophic interaction was reported to occur at different periods of the year depending on the availability of different berries and fruits (Figure 3). Most interactions were reported to occur from October to April around fallen oak fruits (45/55, 81%), from October to January around the chestnuts (47/61, 77%), from May to August around summer berries (39%, 13/33), and from August to October around beech nuts (8/30, 26%).

## Spatial Distribution of IDWS

Figure 4 shows the map with the classified IDWS indexes per municipality. A majority (59%, 33/56) of the municipalities assessed reported medium to high level of IDWS. There was a trend of lower interactions toward the coastal areas and farms located at lower altitude (Bas Taravo and lower areas of Gravone and Prunelli) which implement less extensive farming practices (smaller estates, total fencing, limited use of summer pastures). Conversely, high and medium levels of IDWS were localized in the central higher regions of the island, where the pig farms are more extensive and isolated (Boziu, Verde, and Alesani). This association between higher levels of IDWS at municipalities with higher altitudes was significant (p < 0.05).

## Human Practices Driving IDWS

A number of quantitative indicators of interaction and potential management practices likely to influence those IDWSs were identified for farmers and hunters through exploratory data analysis. For the PCA analysis, only the two first dimensions were kept and the third dimension was also kept for the MFA. As a result, from the initial 34 variables, only the 16 categories that contribute the most to the two first dimensions were represented in Figure 4A (36). MFA highlighted a spatial proximity of all IDWSs indicators (Figure 4A), which appeared negatively correlated with practices such as total fencing of the farm

<table><caption>TABLE 2 | Frequency and duration of different types of interactions between wild or feral pigs and domestic pigs reported by different stakeholders in Corsica in 2013 [SF, strict farmers; SH, strict hunters; HF, hunting farmers (i.e., the farmers hunting at least 2 days a week)].</caption><thead><tr><th>Type of interaction observed</th><th>SF (n = 25)</th><th>SH (n = 20)</th><th>HF (n = 40)</th><th>Overall (n = 65)</th></tr></thead><tbody><tr><td><strong>Sexual</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of observers (%)</td><td>9 (36)</td><td>13 (65)</td><td>30 (75)</td><td>52 (80)</td></tr><tr><td>Median annual frequency (IQR)</td><td>2.3 (1–7)</td><td>5 (2–6)</td><td>5.5 (3–10)</td><td>5 (2–8)</td></tr><tr><td>Median duration of courtship (days) (IQR)</td><td>2 (2–2.5)</td><td>2.5 (2.5–3)*</td><td>2.3 (2–2.5)</td><td>2.5 (2.3–3.3)</td></tr><tr><td>Median duration of intercourse (min) (IQR)</td><td>5 (5–15)</td><td>10 (5–15)</td><td>5 (5–15)</td><td>5 (5–15)</td></tr><tr><td><strong>Agonistic</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of observers (%)</td><td>5 (20)</td><td>8 (40)</td><td>24 (60)</td><td>37 (57)</td></tr><tr><td>Median annual frequency (IQR)</td><td>3.5 (2–4)</td><td>2 (2–3)</td><td>3 (2–5.5)</td><td>2.5 (2–4)</td></tr><tr><td>Median duration of fights (h) (IQR)</td><td>1 (0.5–2)</td><td>0.5 (0.4–2.5)</td><td>1 (0.2–1.5)</td><td>1 (0.4–2)</td></tr><tr><td><strong>Trophic</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of observers (%)</td><td>6 (24)</td><td>13 (65)</td><td>21 (53)</td><td>40 (62)</td></tr><tr><td>Median annual frequency (IQR)</td><td>2 (1–3)</td><td>3 (2–10)</td><td>3 (1–3)</td><td>3 (1–3)</td></tr><tr><td>Median duration of foraging together (days) (IQR)</td><td>20 (7–120)</td><td>5 (1–19)</td><td>19 (2–75)</td><td>7 (2–75)</td></tr></tbody></table>

<footnote>*Indicates significant differences (p < 0.05).</footnote>

<footnote>IQR, interquartile ranges.</footnote>
<figure><img src="image_63.png" /></figure>

perimeter or regular food supplementation. There was also a spatial proximity between practicing female sterilization and the observation of sexual interactions. On the third axis of the MFA (data not shown), the observation of wild boars appeared cor- related to the use of summer pastures as well as the communal use of pastures between farmers.

Regarding hunters' practices, the practice of selective shoot-
ing of feral pigs populations and cross bred animals that could
have been seen on the farm (“Shoot feral pigs”), appears in
proximity of the observed frequency of wild boars near the farm
and the observation of agonistic IDWS (**Figure 4B**). The
observation of sexual and agonistic IDWS is associated with a
higher number of days per week dedicated to hunting activi-
ties (spatial proximity of these factors; **Figure 4B**). The type of
hunting (driven hunts) seemed to equally influence negatively
the observations of IDWS. Finally, being a SH showed to be
negatively linked with IDWS reporting.

Results of the matrix identified correlations between some
indicators of interaction and stakeholder's management prac-
tices, which are listed in **Table 3**. There was a strong positive
correlation between annual frequencies of agonistic and sexual
interactions (R = 0.52).

Regarding farming practices, the fact of having a farming
estate totally fenced reduced the number of observations of
wild boars around the farm (R = -0.69) and the number of
observed IDWS (R = -0.42). Regular use of supplementary
feeding was negatively correlated with the observation of wild
or feral boars (R = -0.31) and non-specific IDWS (R = -0.26).

The sterilization of females reduced the observation of agonistic IDWS (R = -0.29), but was positively correlated with the observation of non-specific IDWS (R = 0.25) and sexual interactions (R = 0.26). Farming surfaces larger than 50 ha or the communal use of pastures by different herds increased the observation of wild boars near the farm (R = 0.28 and 0.37, respectively). Finally, the non-confinement of reproductive sows was positively correlated with the annual frequency of non-specific IDWS (R = 0.36). For hunters, there was a strong correlation between the observation of non-specific IDWS and wild boars in proximity of pig farms (R = 0.4). In addition, we found an association between driven hunts and sexual interactions (R = -0.3).

DISCUSSION

IDWS are widespread worldwide and the number of shared
pathogens between domestic and wild pig populations is con-
siderable. As a result, there is a serious need to understand with
more detail the nature and drivers of those interactions (37).
In Corsica, IDWS are suspected to be intense and result in several
pathogens such as bovine tuberculosis (3, 38), trichinellosis (4),
or Hepatitis E virus (5, 39) circulating between both populations.
Aujezsky disease (40, 41) remains present in Corsica despite
being eradicated in the rest of the French territory, possibly as a
result of IDWS (42). Others serious pig diseases such as African
swine fever are endemic in neighboring Sardinia since 35 years
partly as a result of IDWS (7, 8, 43). To the best of our knowl-
edge, this work represents the most detailed study on IDWS
<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /><figcaption>FIGURE 4 | Continued</figcaption></figure>
**FIGURE 4** | Continued

Potential associations between the different practices according to the multivariate exploratory data analysis: (A) correlation circle of the MFA for farming practices (including SF ad HF) and (B) correlation circle of the PCA for hunters (including SH and HF). Legend for the variables in (A) AOC: farmers involved in quality recognition process; BirthAlly: no reproductive synchronization; FCast: sterilization of females; FencTot: total fencing of farm perimeter; FoodSeas: additional food supply; FSale: sale of females; HomSl: slaughtering of pig on the farm; Hunt: farmers practicing hunting activity; Cadav: disposal of the carcasses outdoor (no specific area); Inter_Fight: observation of fighting interaction (Y/N); Inter_Mating: observation of mating interaction (Y/N); MainActiv: farmers having pig farming as main activity; MatingFree: mating of sow in non-fenced area; Nustr: farmers having only Nustrale breed in the farm; Offal: disposal of carcass left-over outdoor (no specific area); WB_Pig_Inter: observation of any IDWS (Y/N). Legend for the variables in (B) BeatHunt: practice driven hunt (Y/N); rossNbHunted: ratio of cross-bred/wild boar by hunt; D.W_Hunt: number of hunting days per week; FreqWBMPark: annual frequency of observed sexual interactions. HighMont: farming in mountainous areas; NbFight: annual number of fights between wild and domestic boars; ShareArea: share of pastures with other pig farmers; ShootFeral: hunter shooting feral pigs (Y/N); Type: pure hunter (no farming); WB.CrossBred_onF: observed presence of wild pigs around the farm. SF, strict farmers; HF, hunting farmers; SH, strict hunters; MFA, multiple factorial analysis.

in a specific area where they are suspected to be particularly intense and widespread. The use of the questionnaire method to collect traditional knowledge from rural stakeholders, which are privileged observers of natural events, proved to be efficient in capturing relevant ecological and epidemiological information from a large territory with limited time and resources. The preliminary identification of suitable seasons and locations for IDWS can allow to deploy more sophisticated and costly methods such as camera traps (12), or radio collars with data loggers (11) to monitor IDWS at a finer scale. As suspected, our study suggests that IDWS are common and widespread in the main outdoor pig production areas of Corsica, particularly in higher areas. Actually, the recorded levels of IDWS are probably underestimated for several reasons. First, we did not interview small-scale farmers keeping pigs outdoors, many of which are registered in the Animal Health Services records. This category of small-scale farmer (>300 individuals) is characterized by improvised farming facilities, poor reproductive management, and low biosecurity measures. These conditions should facilitate the incursions of wild boar, which are attracted by sows on heat or food remains (20, 44). Second, the observations collected referred to diurnal events and thus most nocturnal interactions, which might be equally common were unlikely to be observed by the respondents and not captured by the questionnaire.

In addition, some other sources of bias could have influenced the results of our study, which should be taken with caution until they can be validated with other studies or methodologies. Despite the sampling size was quite representative of the number of extensive farmers in each production area, the proportion of hunters interviewed was limited. Moreover, most of hunter's observations refer mostly to the hunting season. In addition, a possible recall bias when asking for retrospective observations within a period of one whole year could also have influenced some of the responses. As a result, some data, particularly frequencies or durations of interactions, should be monitored with more sophisticated methods such as GPS collars or camera traps to confirm the stakeholder's observations.

Nevertheless, the results obtained are biologically sound and the information collected by the different categories of stakeholders was consistently similar. For example, frequencies of sexual interactions, duration of intercourse, or season of sexual interactions were reported by the different stakeholders were very similar (Table 2). This suggests that the chosen method was efficient to collect abundant and detailed information, which is otherwise difficult to observe and quantify in a large territory.

Surprisingly, our questionnaire was equally able to capture some illegal or compromising practices such as inappropriate carcass management or the distribution of carcass offal to domestic pigs. This kind of behavior widespread is an extremely effective pathway for pathogen transmission between the domestic and wild compartments because it allows direct contact between individuals or their potentially infected tissues (45).

Indirect trophic interactions were less commonly observed but seemed to occur more regularly along the year, while direct sexually driven interactions (such as mating or fighting) appeared extremely common (Figure 2). As a measure of comparison, in a recent study in Switzerland, between 25 and 30% of the stakeholders had observed IDWS during the previous year to the interview and hybrid animals were reported in 5% of the 329 pigries investigated (20). In our study, interactions were reported by 75% of the respondents and cross bred litters were reported (median 2 [1–4] per year) in 57% of the 65 farms investigated.

The seasonal concentration of contacts in the autumn months (Figure 3) and at high altitude municipalities (Figure 5) suggests a specific risk of disease transmission in those periods and locations. Interestingly, it is worth noting that this seasonal pattern is consistent with observed seasonal outbreaks of African swine fever in wild boars as results of contacts with infected domestic pigs in Sardinia (8) and the Russian Federation (46). However, further research is necessary to confirm if this seasonal pattern is related to IDWS or to a potential observation bias, due to a higher level of human activity during summer months due to the hunting season.

A potential impact of reported sexual interactions is the possible increase of a hybrid pig population which roams free in the Corsican ecosystem interacting with domestic pigs and pure wild boar populations. Although insufficiently studied, these hybrids are suspected to have distinct reproductive, behavioral, and genetic patterns than pure wild boars that could make them exposed to higher disease burdens than those observed in pure wild boar populations (5, 47, 48). Considering the abundance and distribution of hybrid feral populations in Corsica, and the role they seem to play in similar settings (43), their potential epidemiological role in disease spread and maintenance deserves further investigation.

Many of the stakeholders identified during the questionnaires were implementing specific preventive mitigating measures to reduce IDWS. This could explain, for instance, some of the positive correlation presented in Table 3 between the number of hybrid litters and the shooting of feral pigs (R = 0.32), or the one between annual frequency of sexual IDWS and the sterilization
<figure><img src="image_66.png" /><figcaption>**FIGURE 5** | Map showing the average level of IDWS captured in the different interviews per municipality, classified as null, low, medium, or high.</figcaption></figure>

of females (R = 0.25, Table 3). In some cases, these measures appear as having a non-negligible mitigating impact on the occurrence of IDWS, such as the case of driven hunts on sexual IDWS (R = -0.42), or the effect of supplementary feeding on the observation of non-specific IDWS (R = -0.26).

This suggests that there is some degree of risk perception among stakeholders and that the active promotion of some

practices can potentially reduce the risk of disease transmission and spread. Furthermore, it highlights that local knowledge can be used to implement collective disease management strategies. Indeed, if this study increases knowledge about IDWS in extensive production system areas, it also reveals that farmers have an accurate knowledge on what happens with their animals, in and around their farm.
<table><caption>**TABLE 3** | List of the most significant correlation coefficients between indicators of contact and farming or hunting practices (*p* < 0.05).</caption><thead><tr><th>Practice</th><th>Interaction indicator</th><th>Correlation coefficient</th></tr></thead><tbody><tr><td><strong>Farmers</strong></td><td></td><td></td></tr><tr><td>Total fencing of farm perimeter (Y/N)</td><td>Number of observations of wild boar near farm</td><td>-0.69</td></tr><tr><td>Total fencing of farm perimeter (Y/N)</td><td>Annual number of interactions</td><td>-0.42</td></tr><tr><td>Additional food supply (Y/N)</td><td>Number of observations of wild boar near farm</td><td>-0.31</td></tr><tr><td>Sterilization of females (Y/N)</td><td>Annual number of observed fights</td><td>-0.29</td></tr><tr><td>Additional food supply (Y/N)</td><td>Observation of interactions (Y/N)</td><td>-0.26</td></tr><tr><td>Sterilization of females (Y/N)</td><td>Observation of interactions (Y/N)</td><td>0.26</td></tr><tr><td>Sterilization of females (Y/N)</td><td>Annual frequency of sexual interactions</td><td>0.25</td></tr><tr><td>More than 50 ha of outdoor area surface (Y/N)</td><td>Number of observations of wild boar near farm</td><td>0.28</td></tr><tr><td>Farming as main activity (Y/N)</td><td>Observation of interactions (Y/N)</td><td>0.31</td></tr><tr><td>Mating in non-fenced areas (Y/N)</td><td>Annual number of interactions</td><td>0.36</td></tr><tr><td>Communal use of pastures by different herds (Y/N)</td><td>Number of observations of wild boar near farm</td><td>0.37</td></tr><tr><td>Annual frequency of observed fights</td><td>Annual frequency of sexual interactions</td><td>0.52</td></tr><tr><td><strong>Hunters</strong></td><td></td><td></td></tr><tr><td>Driven hunt (Y/N)</td><td>Annual frequency of sexual interactions</td><td>-0.33</td></tr><tr><td>Shooting feral pigs (Y/N)</td><td>Annual number of hybrid litters</td><td>0.32</td></tr><tr><td>Hunting practice (Y/N)</td><td>Number of observations of wild boar near farm</td><td>0.36</td></tr><tr><td>Hunting practice (Y/N)</td><td>Annual number of interactions</td><td>0.43</td></tr></tbody></table>

Our study provided useful information of the potential disease transmission risk of certain hunting and farming practices. This information should be used to engage a proactive communication and participative exchange of information with stakeholders in order to initiate decision-making processes or monitor risk mitigation strategies (49). From a management perspective, awareness campaigns should take advantage of the very seasonal (autumnal) and localized nature of many IDWS in areas of higher altitude, to concentrate efforts in those periods and areas (Figure 5). Similarly, farms with a high level of sexual interactions with wild boar could be more exposed to Aujeszky disease infection in domestic sows (42). In order to increase awareness, any communication on disease risks is likely to have a higher impact if it targets diseases of public health importance and emphasize

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## CONCLUSION

The implementation of face-to-face semi-open questionnaires proved to be a useful tool to characterize IWDS in areas of extensive pig-farming production in Corsica, providing abundant and unique information on events that are otherwise difficult to observe. It also contributed to identify seasons and areas where IDWS are particularly common. This information can be used to concentrate efforts and resources in those particular spatial and temporal windows in order to implement risk-mitigating measures. In addition, IDWS can also be monitored and analyzed at a finer scale in order to provide data for the development of epidemiological models of disease transmission at the wildlife livestock interface (51, 52). Finally, this information can be used to engage participative decision-making processes on disease risk management with stakeholders.

## AUTHOR CONTRIBUTIONS

FJ designed the study, analyzed some of the data, supervised field work, and wrote the paper; AR implemented interview, analyzed part of the data, and reviewed the manuscript; BT implemented interviews and analyzed some of the data; OM, FR, DC, DCh and FC supervised field work and contributed to design the sampling approach, EE analyzed part of the data, supervised the research project and contributed to the manuscript.

## ACKNOWLEDGMENTS

The authors acknowledge the farmers who participated in the project, the Corsican Animal Health services (GDS 2A, GDS 2B, Corsica, France) and the Corsican Hunter's Federations from *Haute Corse* and *Corse du Sud*, for providing initial data.

## FUNDING

This study was funded by European Union, Seventh Framework Programme (FP7/2007-2013) under Grant Agreement no. 311931 (ASFORCE).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Jori, Relun, Trabucco, Charrier, Maestrini, Chavernac, Cornelis, Casabianca and Etter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
1.3. Aujeszky's disease and Hepatitis E viruses transmission between domestic pigs and wild boars in Corsica: evaluating the importance of wild/domestic interactions and the efficacy of management measures [#3]

Référence: [#3]

Charrier, F., Rossi, S., Jori, Maestrini, O., Richomme, C., Casabianca, F., Ducrot, C., Jouve, J., Pavio, N., Le Potier, M.-F. (2018). Aujeszky's disease and Hepatitis E viruses transmission between domestic pigs and wild boars in Corsica: evaluating the importance of wild/domestic interactions and the efficacy of management measures. *Frontiers in Veterinary Science*, *5*.
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# Aujeszky's Disease and Hepatitis E Viruses Transmission between Domestic Pigs and Wild Boars in Corsica: Evaluating the Importance of Wild/Domestic Interactions and the Efficacy of Management Measures

OPEN ACCESS

**Edited by:**

Francisco Ruiz-Fons,
Instituto de Investigación en
Recursos Cinegéticos IREC
(CSIC-UCLM-JCCM), Spain

**Reviewed by:**

Fedor Korennoy,
Federal Center for Animal
Health (FGBI ARRIAH), Russia
Jorge Ramón López-Olvera,
Universitat Autònoma
de Barcelona, Spain

*Correspondence:
Marie-Frédérique Le Potier
marie-frederique.lepotier@anses.fr

¹These authors have contributed
equally to this work.

**Specialty section:**
This article was submitted
to Veterinary Epidemiology
and Economics,
a section of the journal
Frontiers in Veterinary Science

**Received:** 21 July 2017
**Accepted:** 03 January 2018
**Published:** 24 January 2018

**Citation:**
Charrier F, Rossi S, Jori F, Maestrini O, Richomme C, Casabianca F, Ducrot C, Jouve J, Pavio N and Le Potier MF (2018) Aujeszky's Disease and Hepatitis E Viruses Transmission between Domestic Pigs and Wild Boars in Corsica: Evaluating the Importance of Wild/Domestic Interactions and the Efficacy of Management Measures. *Front. Vet. Sci.* 5:1. doi: 10.3389/fvets.2018.00001

François Charrier¹,²†, Sophie Rossi³†, Ferran Jori⁴,⁵, Oscar Maestrini¹, Céline Richomme⁶, François Casabianca¹, Christian Ducrot⁷, Johan Jouve⁸, Nicole Pavio⁹ and Marie-Frédérique Le Potier¹⁰*

¹Laboratoire de Recherche sur le Développement de l'élevage (LRDE), INRA, Corte, France, ²Laboratoire Interdisciplinaire, Sciences, Innovations, Sociétés, INRA, Marne-la-Vallée, France, ³Unité sanitaire de la Faune, Office National de la Chasse et de la Faune Sauvage, Gap, France, ⁴CIRAD, UMR ASTRE (Animal, Santé, Territoires, Risque et Environnement), Montpellier, France, ⁵ASTRE, University of Montpellier, CIRAD, INRA, Montpellier, France, ⁶Laboratoire de la rage et de la faune sauvage, ANSES, Nancy, France, ⁷UR346 EpiA, Centre de recherche Auvergne Rhône Alpes, INRA, Saint Genes-Champanelle, France, ⁸UMR Lieux, Identités, Espaces et Activités (LISA), CNRS-Université Pascal Paoli, Corte, France, ⁹UMR 1161 Virology, Animal Health Laboratory, ANSES-INRA-ENVA, Maisons-Alfort, France, ¹⁰Unité Virologie Immunologie Porcines, Laboratoire de Ploufragran/Plouzané, ANSES, Ploufragan, France

Wildlife species as reservoirs of infectious pathogens represent a serious constraint in the implementation of disease management strategies. In the Mediterranean island of Corsica, the dynamics of hepatitis E virus (HEV) and Aujeszky's disease virus (ADV) are suspected to be influenced by interactions between wild and domestic pigs. To improve our understanding of these influences, we first compared the seroprevalences of both viruses in domestic pig populations from different locations with contrasted levels of wild–domestic interactions, ADV vaccination, biosafety, and farm husbandry. Second, we performed an analysis at a more restricted geographical scale, to assess the matching of ADV or HEV prevalence between sympatric wild boar and outdoor pig farms most exposed to interactions with wildlife. Logistic models were adjusted to the observed data. A high seroprevalence of HEV (>80%) and ADV (40%) in pigs, with no significant difference according to the region, confirms that both pathogens are enzootic in Corsica. Vaccination against ADV had a strong protective effect, even when performed voluntarily by farmers. Farm biosafety had an additional effect on pigs' exposure, suggesting that contact between wild boars and pigs were involved in disease transmission. A strong correlation in HEV seroprevalence was observed between pigs and wild boars that were in close contact, and significantly lower seroprevalence was observed in pigs when they had little contact with wild boars due to spatial segregation. These results suggest a regular HEV circulation between sympatric wild boar and domestic pigs. The high HEV
seroprevalence observed in domestic pigs (>80%) suggests a spillover of the virus from domestic to wild populations through environmental contamination, but this hypothesis has to be confirmed. Conversely, even though avoiding sows' release on pasture during estrus showed some protecting effect in the free ranging pig farms regarding ADV, ADV seroprevalence was not dependent on the swine populations (wild or domestic) or on the wild-domestic spatial overlap, suggesting two quasi-separate enzootic cycles. This information will prove useful for designing more efficient disease management strategies in Corsica and similar contexts.

Keywords: Sus scrofa, wild boar, interface, virus transmission, Aujeszky's disease, hepatitis E, Corsica

## INTRODUCTION—CONTEXT: TWO POPULATIONS, TWO PATHOGENS, AND MULTIPLE STORIES

Interactions between wild and domestic animals can play a role in the maintenance of pathogens and thereby compromise the efficiency of disease control strategies (1–3). Contacts may rely on direct (e.g., mating or fighting) and/or indirect transmission routes (e.g., sharing the same contaminated habitat) and can be influenced by human activities such as farming or hunting (4, 5). In extensive outdoor farming areas, wild/domestic interactions can be facilitated by some farming practices that expose domestic animals to contacts with wildlife (6). Similarly, hunting practices can influence the spatial distribution of game populations and their interactions with domestic animals. Wild and domestic swine are particularly at risk of inter-population transmission because they belong to the same species and share the same community of potential pathogens (4, 5, 7). In this context, certain farming practices (use of shared pasture areas, reduced surveillance of the herd, etc.) can facilitate interactions and therefore, have a strong influence on the transmission and circulation of pathogens. On the other hand, biosecurity measures can prevent transmission from wild boars to domestic pigs in high-risk areas (8). The reproductive management of domestic sows during the estrus period can influence the occurrence of sexual interactions between wild boars and domestic females or fights between wild and domestic boars (6, 9). The risk of transmitting pathogens is considered the highest in areas with traditional extensive farming and can determine the dynamics of emerging or reemerging pig diseases (10).

In Corsica, a French Mediterranean island, traditional extensive outdoor pig farming systems remain common. Based on the use of local resources (pastures, chestnut, and oak forests), these systems produce high-quality processed meat for the local and national market. However, this kind of farming facilitates contacts with an important wild boar population, sharing pathogens such as the Aujeszky's disease virus (ADV) (11–14), hepatitis E virus (HEV) (15–18), or bovine tuberculosis (19). In addition, serious threats such as African swine fever, which is endemic in the neighboring island of Sardinia (20), increase the need to understand and manage wild-domestic pig interactions (17, 21).

The objective of this study is to explore the influence of different farming practices (biosafety, use of natural pastures/forests, ADV

vaccination, and reproductive management) and individual factors (phenotype, age, sex, etc.) on the prevalence of two pathogens (ADV and HEV) known to circulate within and between domestic and wild pig populations in Corsica. These pathogens have different transmission routes, being able to infect new susceptible hosts through direct (requiring physical contact) or indirect (not requiring close proximity) interactions. ADV, which remains enzootic among domestic pigs and wild boars on the island (22), can be transmitted by close contact including mating (23–25). In this case, the absence of reproductive management of sows can play a role on its transmission between wild and domestic populations. Conversely, HEV is a pathogen widely spread either by direct contact or through a contaminated environment, which is known to be common in free ranging populations of domestic pigs, wild boars and cross bred animals in Corsica and many other European countries (16, 17). We assumed that the dynamics of both pathogens can be influenced by different degrees of interaction at the wild/domestic interface and domestic pig management practices implemented by farmers.

To test these hypotheses, we implemented a two-step approach to investigate different risk factors. Initially, the seroprevalence of ADV and HEV was analyzed in the domestic pig population in a sample of farms across Corsica, to study the effect of the farm biosafety level, vaccination against ADV and the distribution of these two pathogens among different pig production systems and microregions in Corsica. Subsequently, we focused on traditional pig farms exposed to wild boar populations, located within one particular microregion from north-central Corsica (the Boziu-Verde microregion), to better determine the effect of the spatial interface between the two swine populations (through the shared use of natural pastures and forests) and the protective effect of excluding sows in estrus from natural pastures.

## MATERIAL AND METHOD: COLLECTING DATA ON SEROPREVALENCE AND FARMING SYSTEMS IN A DOUBLE SCALED APPROACH

To cover the potential diversity of extensive farming practices existing in Corsica, our study design incorporated an initial large scale, across-island approach to capture this diversity, and then focused on one specific microregion to assess the interaction
between domestic pigs and wild boars (see Study Design: A Two-Step Study on Wild Boar/Domestic Pig Interactions in Corsica). We thus combined different sampling procedures (see Serological Data) to build three datasets for statistical modeling (see Dataset Construction and Data Analysis).

# Study Design: A Two-Step Study on Wild Boar/Domestic Pig Interactions in Corsica
## Domestic and Wild Swine Populations

Corsica is a French Mediterranean island characterized by a sparse human population (32 inhabitants/km²) and an economy principally based on tourism. Pig production is based on extensive outdoor systems, partly relying on pasture resources (acorns and chestnuts), using local and common breeds (*Nustrale* pigs, *Large White*, or *Duroc*), and generating products for several types of markets ("Protected Designation of Origin" products with high added value and short supply chains with direct sales). Pig farmers are thus breeders, but also processors and retailers of their own production as described in several other localities in the Mediterranean area (26). Pigs are slaughtered between November and March after the autumnal finishing period. However, although traditional farming systems are prevalent in Corsica, farming practices may differ greatly across the 300 registered farms, especially regarding epidemiological interactions with wild boars (21, 27).

On this mountainous island, extensively covered by grazing lands with typical Mediterranean vegetation (scrub, bushes, small trees, oak trees, etc.), the wild boar density is estimated to be very high, with around 30,000 wild boars being hunted every year (28). Because the wild and domestic populations share the same resources on the same areas, and sometimes at the same moment, this situation is of key interest to study infectious interactions between domestic and wild pigs.

## Two-Step Study: Large-scale Approach and Microregional Focus

Both virus dynamics may rely on farming practices, such as biosecurity measures (e.g., fences), vaccination against ADV, and characteristics of the production system: age of slaughtering/hunting (farmers choose the age according to the quality of production output, hunters usually shoot older wild boars, etc.), mating management or spaying females (i.e., farmers may avoid encounters between the sow in estrus and male wild boars either by organizing sow mating before releasing them on pastures or by spaying them). These key factors were included in our datasets to be analyzed (see below). To capture the role of farming practices on pathogen dynamics, we combined two different approaches.

### Step 1: Study Based on Sampling in Slaughterhouses (Dataset 1)

The purpose of this part of the study was to determine the seroprevalence of both pathogens and their geographical distribution and to characterize the potential existing risk factors due to farming practices. To do so, we sampled domestic pigs opportunistically at slaughterhouses to obtain a spatially diversified picture of disease exposure from different areas of Corsica. This sample which was purposive and mainly driven by pragmatic

opportunities included 213 pigs originating from 32 farms from several pig production areas across Corsica (Figures 1A,B): investigators collected blood samples during the slaughtering procedure and interviewed farmers when they came to slaughterhouses to bring their pigs or pick up the carcasses. Ten visits to the abattoirs were carried out during the main slaughtering period (December 2014–January 2015) to gage the overall status of each pathogen (HEV and ADV) and to identify the farming practices that play a role in pathogen dynamics.

### Step 2: Study Based on a Focus on a Particular Microregion (Datasets 2 and 3)

The purpose was to produce knowledge on pathogen seroprevalence and distribution in domestic pig and wild boar population, targeting traditional outdoor pig farms, supposed to be at risk, from the Boziu-Verde microregion (cf., Figures 2A,B). Six farms were particularly selected for their irregular participation in vaccination campaigns (none of them participated to Aujeszky disease management plan implemented in 2011). Only one farm implemented techniques aiming at reducing risk of contact with wild boars (by female castration and reproduction management), and three of them were known to implement “informal” farming practices (i.e., unofficial farmers and on-farm slaughtering). By focusing on these farms, we hypothesized that the common extensive farming practices (i.e., the regular use of natural pastures) maximized the risk of pathogen transmission between both wild and domestic swine populations. We thus collected data on ADV and HEV seroprevalences and farming practices in pig farms, as well as ADV and HEV seroprevalence in wild boar populations (blood samples collected during hunting sessions, from August to January).

## Serological Data
### Sample Collection

To study the seroprevalence of these two diseases in the wild and domestic populations, serum samples were collected from wild boars and domestic pigs. For wild boars, blood samples were collected by hunters by means of cardiac puncture on recently shot animals. Blood samples from domestic pigs were collected by veterinarians at the slaughterhouse or by technicians in the herd by blotting filter paper with a drop of blood from the tail of sows.

Blood samples and dried filter papers were sent within 1 day to the INRA research facility in Corte to be centrifuged or dried, respectively. All sera were stored at -20°C, and dried filter papers were kept at 4°C until analysis.

## Serological Methods

For ADV, all sera and filter papers were tested using an ELISA for the specific detection of antibodies to the ADV gB protein (IdVet-ID Screen® Aujeszky gB competition), according to the manufacturer's instructions. Because pigs could have been vaccinated with a gE-deleted vaccine, the sera collected from the slaughterhouse were further tested with an ELISA gE (Idexx PRV/ADgI) when they tested gB positive in a vaccinated herd. These two commercial kits have been approved by the ANSES-Ploufragan OIE reference laboratory because they can detect,
A

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B

<figure><img src="image_69.png" /></figure>

**FIGURE 1** | Localization of sampled pigs sampled at the slaughterhouse (dataset 1). (A) Number of sampled pigs (slaughterhouse, dataset 1) at the scale of the nine samples microregions and Aujeszky's disease virus seroprevalence. (B) Number of sampled pigs (slaughterhouse, dataset 1) at the scale of the nine samples microregions and hepatitis E virus seroprevalence.

A

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B

<figure><img src="image_71.png" /></figure>

**FIGURE 2** | Localization of sampled pigs and wild boars in the "Boziu-Verde" microregion (datasets 2 and 3). (A) Localization of domestic pigs sample and hunting areas with Aujeszky's disease virus seroprevalence. (B) Localization of domestic pigs sample and hunting areas with hepatitis E virus seroprevalence.
respectively, the ADV-1 international serum standard at the dilutions of 1:2 for ELISA gB or 1:8 for ELISA gE.

The detection of anti-HEV antibodies in wild boars and domestic pigs was performed using the HEV ELISA 4.0v kit (MP Diagnostics, Illkirch, France) according to the manufacturer's instructions, except the serum quantity used, 10 µL instead of 20 µL. For detection on filter paper, elution was performed in washing buffer. This sandwich ELISA allows the detection of all antibody classes (IgG, IgM, and IgA) and uses a recombinant antigen that is present in all HEV strains. Samples were positive when the optical density at 450 nm wavelength obtained for the sample was higher than the threshold defined as the mean for negative controls +0.3 for serum and +0.4 for filter paper.

# Dataset Construction and Data Analysis
## Datasets

Three datasets were considered for the statistical analyses:

(1) *Dataset 1:* A total of 213 domestic pigs from 10 different microregions and 32 farms were sampled in slaughterhouses to assess the distribution of HEV and ADV in different parts of Corsica. Pig sera from vaccinated farms were tested for the presence of antibodies to gB and gE using the two ELISA tests to differentiate between vaccinated (gB+/gE-) and infected (gB+/gE+) animals. Pigs with two consecutive positive results were considered as exposed to ADV (i.e., seropositive), and those testing negative for gB or positive for gB but negative for gE were considered as non-infected (i.e., seronegative). In farms that did not vaccinate their pigs, only gB ELISAs were performed and all pigs with gB-positive results were considered as naturally infected with ADV (i.e., seropositive).

(2) *Dataset 2:* 80 domestic pigs were sampled on 6 traditional farms located in the Boziu-Verde microregion; these farms were not participating in the official vaccination plan and free ranging pigs shared natural pastures/forests with wild boars. For this sample, only gB ELISA was performed since no vaccination could interfere with surveillance.

(3) *Dataset 3:* A total of 297 wild boars were randomly sampled at seven different locations within the Boziu-Verde microregion during five hunting seasons, from 2009 to 2016. 198 sampled wild boars came from the four areas adjacent to farms sampled in dataset 2, and the 99 others came from the three areas that had no or limited contact with pigs (*Figures 2A,B*). Out of these 297 wild boars, 115 were young (i.e., less than 12 months old). For this sample, only gB ELISA was performed because wild boars are not vaccinated. This dataset is thus composed of 297 wild boars, randomly sampled in the Boziu-Verde area.

# Dependent and Independent Variables
## Individuals

For both pigs and wild boars, serological data were categorized as positive and negative results (some rare doubtful results were removed) depending on the detection of ADV and HEV antibodies (dependent variables). The age of domestic pigs was

determined by ear tags, whereas the age (young or adult) of wild boars was determined by tooth eruption patterns (29) and body size. Sex and the presence or absence of hybrid phenotype were also recorded. In boars, the presence of colors other than black or dark brown in the coat and the shape and length of the ears were considered as indicators of hybridization with domestic pigs (30). The “hybrid” category thus extended beyond just F1 generation.

## Farms

For each pig farm, data were collected on its localization (see **Figure 1**), the level of biosafety of the pig farming area (three levels: free ranging, fenced pastures, and closed piggery), the vaccination treatment (three levels: no vaccination, unofficial vaccination, and official vaccination plan), spaying of females (two levels: yes and no), the reproductive management of female during estrus (two levels: yes and no). Among the six traditional farms sampled in dataset 2, we also recorded the use of natural pastures/forests (two levels: seasonal and permanent) and the type of animal [two levels: reproductive animals (breeder pigs) and pigs intended for meat production (fattening pigs)].

## Hunting Areas

Hunting areas were classified according to the frequency of domestic pig presence during the year. Three types of hunting areas were distinguished according to hunters' observations: absence of pigs (no pigs at any time of the year); permanent presence (pigs are spotted on the area all year round); intermittent presence (pigs are spotted only during a specific period, autumn, and when they feed on chestnuts and acorns). Thus, each sampled wild boar was associated with a hunting area (28 areas), which was associated with a variable (3 levels) taking into account the proximity of domestic pigs.

# Statistical Model

Individual serological results were encoded as 0 (seronegative) or one (seropositive). We used general linear model with logit link to test the risk factors of seropositivity for swine regarding both ADV and HEV. We also tested general mixed models, accounting for the random effects of farm (for pigs) and hunting sector (for wild boars) (data not shown). Since this approach did not provide any improvement in model fit, we finally selected the simplest general linear model approach.

Model selection was based on the Akaike information criterion corrected for over-dispersion and small sample size (QAICc). We performed a preliminary correlation analysis of explicative factors that revealed an important correlation between sow castration and the management sows during estrus in the first data set, so that we finally only tested the female castration treatment as a risk factor in the first step analysis. Starting from a “complete” model including all the potential explanatory variables for the different datasets (detailed in **Table 1**), we explored simpler models (using the dredge function of the R package MuMin). Considering the QAICc of all the potential sub-models, we used a model-averaging procedure to account for uncertainty in the model selection, because our data corresponded to a small sample size exposed
<table><caption>**TABLE 1** | Description of the models and variables tested for each dataset.</caption><thead><tr><th colspan="4">(a) Complete model equations</th></tr><tr><th>Disease</th><th>Dataset</th><th>Number of animals</th><th>Complete model equation</th></tr></thead><tbody><tr><td rowspan="3">Aujeszky's disease virus (ADV)</td><td>1</td><td>151 pigs large scale</td><td>Age + castration + microregion + seroHEV + sex + farm type + vaccination</td></tr><tr><td>2</td><td>75 free ranging pigs (Boziu-Verde)</td><td>Age + crossbred + pasture use + owner + sow release + seroHEV + sex + repro status + zone</td></tr><tr><td>3</td><td>274 wild boar (Boziu-Verde)</td><td>Age + year + hybrid + seroHEV + sex + zone</td></tr><tr><td rowspan="3">Hepatitis E virus (HEV)</td><td>1</td><td>151 pigs large scale</td><td>Age + castration + microregion + seroADV + sex + farm type</td></tr><tr><td>2</td><td>75 free ranging pigs (Boziu-Verde)</td><td>Age + crossbred + pasture use + owner + sow release + seroADV + sex + repro status + zone</td></tr><tr><td>3</td><td>274 wild boar (Boziu-Verde)</td><td>Age + year + hybrid + seroADV + sex + zone</td></tr></tbody></table><table><thead><tr><th colspan="4">(b) Explicative variables description</th></tr><tr><th>Subspecies</th><th>Individual/pop</th><th>Variable</th><th>Classes</th><th>Datasets</th></tr></thead><tbody><tr><td>Pig or wild boar</td><td>Individual</td><td>Age</td><td>Young/adult</td><td>1-3</td></tr><tr><td>Pig or wild boar</td><td>Individual</td><td>Sex</td><td>Male/female</td><td>1-3</td></tr><tr><td>Pig or wild boar</td><td>Individual</td><td>Hybrid status</td><td>Yes/no</td><td>2 and 3</td></tr><tr><td>Pig</td><td>Individual</td><td>Reproductive status</td><td>Reproducer/fattening</td><td>2</td></tr><tr><td>Pig/wild boar</td><td>Individual</td><td>Serological status for HEV or ADV</td><td>0/1</td><td>1-3</td></tr><tr><td>Pig</td><td>Farm</td><td>Farm type</td><td>Closed/fenced pasture/traditional free ranging</td><td>1</td></tr><tr><td>Pig</td><td>Farm</td><td>Vaccination</td><td>Official plan/self vaccination/no vaccination</td><td>1</td></tr><tr><td>Pig</td><td>Farm</td><td>Castration or sows in heat management</td><td>0/1</td><td>1 and 2</td></tr><tr><td>Pig</td><td>Farm</td><td>Pasture use</td><td>Seasonal/all year round</td><td>2</td></tr><tr><td>Pig</td><td>Farm</td><td>Microregion</td><td>Boziu, Casinca, Alesani, Gravonna, Plaine Orientale, Cruzzini</td><td>1</td></tr><tr><td>Pig</td><td>Farm</td><td>Owner</td><td>6 owners anonymized</td><td>2</td></tr><tr><td>Pig or wild boar</td><td>Farm of hunting area</td><td>Area</td><td>6 areas for wild boars and 4 areas for pigs</td><td>2 and 3</td></tr></tbody></table>

to uncontrolled sampling processes (at the slaughterhouse or of voluntary farms). That means that we could not necessarily retain a “single best model” but rather, a set of potentially “best models” for which the increase in QAICc (delta-QAICc) was less than 2 (31). We finally calculated variable coefficients [i.e., odd ratios and 95% confidence intervals (CIs)] over this set of “best models” according to the process described by Burnham and Anderson (31) (and using the AICmodavg R package). These analyses were performed using R software (32), and the MuMin (33), and AICmodavg packages (34).

# RESULTS

## ADV and HEV Seroprevalence and Risk Factors in the Corsican Domestic Pig Population (Dataset 1)

A total of 213 domestic pigs were sampled at the slaughterhouse, but the analyses were performed on the 151 individuals for which there were no missing data for any of explanatory variables or serological assays. Average seroprevalence in this dataset was 41.7% (95% CI [33.8%; 49.6%]) for ADV and 85.4% (95% CI [79.8%; 91.0%]) for HEV. These values were higher in free ranging and fenced pasture farms than in closed farms [average prevalence was 58.4% (95% CI [48.8%; 68.0%]) and 93.0% (95% CI [88.1%; 97.9%]) in free ranging farms regarding ADV and HEV, respectively].

## Major Risk Factors Associated with ADV Seropositivity in Pigs

We retained three potential best models according to their QAICc values (delta-QAICc < 2) (QAICc are detailed in the additional materials), but the significant average effects only concerned the vaccination treatment, sow castration and farm biosafety (full model-averaged coefficients detailed in Table 2a). The vaccination treatment was negatively correlated to ADV prevalence: a lower seroprevalence was observed in farms that implemented a self-vaccination process (OR<sub>no vaccination/self-vaccination</sub> = 3.88, 95% CI [1.30; 11.57]), but this protective effect was much higher in farms that had implemented the official vaccination plan (OR<sub>self-vaccination/official</sub> = 3.39, 95% CI [1.07; 10.75]). We also observed a protective effect of sow castration (OR<sub>no castration/castration</sub> = 4.24, 95% CI [1.31; 13.71]). Traditional free ranging farms exhibited a higher seroprevalence compared with the closed ones (OR<sub>free ranging/close</sub> = 6.14, 95% CI [1.11; 34.12]); the absence of ADV seropositive individual in fenced pasture farms in our sample could not allow the comparison of that particular category to other biosafety levels. All other effects were not significant and were finally not retained (Table 2a).

## Major Risk Factors Associated with HEV Seropositivity in Pigs

We retained four potential best models according to their QAICc values (delta-QAICc < 2) (additional materials), but the significant average effects only concerned the farm biosafety (full model-averaged coefficients detailed in Table 2b). Traditional
<table><caption>**TABLE 2** | Parameters estimates among the set of best models (model averaging) for dataset 1 (pigs sampled at the slaughterhouse at a large scale).</caption><thead><tr><th></th><th>Estimate</th><th>SE</th><th>Adjusted SE</th><th>z-Value</th><th>Pr (&gt;|z|)</th></tr></thead><tbody><tr><td colspan="6"><strong>(a) Regarding Aujeszky's disease virus</strong></td></tr><tr><td>Vacc (no vs voluntary)</td><td>1.35680</td><td>0.55691</td><td>0.56158</td><td>2.416</td><td>0.0157*</td></tr><tr><td>Vacc (official vs voluntary)</td><td>-1.22555</td><td>0.58898</td><td>0.59395</td><td>2.063</td><td>0.0391*</td></tr><tr><td>Type (free ranging vs closed)</td><td>1.81566</td><td>0.87457</td><td>0.88168</td><td>2.059</td><td>0.0395*</td></tr><tr><td>Sows' castration (yes)</td><td>-1.44539</td><td>0.59826</td><td>0.60331</td><td>2.396</td><td>0.0166*</td></tr><tr><td>Type (open air vs closed)</td><td>-17.02230</td><td>1,279.67939</td><td>1,290.48297</td><td>0.013</td><td>0.9895</td></tr><tr><td>Age (young vs adult)</td><td>-0.15475</td><td>0.48168</td><td>0.48439</td><td>0.319</td><td>0.7494</td></tr><tr><td>Sex (male vs female)</td><td>-0.05546</td><td>0.23302</td><td>0.23456</td><td>0.236</td><td>0.8131</td></tr><tr><td colspan="6"><strong>(b) Regarding hepatitis E virus</strong></td></tr><tr><td>Type (fenced pasture and free ranging vs closed)</td><td>2.30771</td><td>0.68869</td><td>0.69288</td><td>3.331</td><td>0.000867*</td></tr><tr><td>Sows' castration (yes)</td><td>0.04097</td><td>0.23187</td><td>0.23342</td><td>0.175</td><td>0.860689</td></tr><tr><td>Age (young)</td><td>-0.36303</td><td>0.67060</td><td>0.67264</td><td>0.540</td><td>0.589398</td></tr><tr><td>Sex (male)</td><td>-0.06955</td><td>0.26421</td><td>0.26562</td><td>0.262</td><td>0.793440</td></tr></tbody></table>

<table><caption>**TABLE 3** | Parameters estimates among the set of best models (model averaging) for dataset 2 (free ranging pigs from Boziu Verde).</caption><thead><tr><th></th><th>Estimate</th><th>SE</th><th>Adjusted SE</th><th>z-Value</th><th>Pr (&gt;|z|)</th></tr></thead><tbody><tr><td colspan="6"><strong>(a) Regarding Aujeszky's disease virus</strong></td></tr><tr><td>Sows mating management (yes)</td><td>-4.2114</td><td>1.1991</td><td>1.2180</td><td>3.458</td><td>0.000545*</td></tr><tr><td>Reproductive status (yes)</td><td>3.3042</td><td>1.2305</td><td>1.2473</td><td>2.649</td><td>0.008071*</td></tr><tr><td>Sex (male)</td><td>1.8744</td><td>1.2264</td><td>1.2380</td><td>1.514</td><td>0.129999</td></tr><tr><td>Sero hepatitis E virus (HEV) (positive)</td><td>1.3607</td><td>1.4478</td><td>1.4569</td><td>0.934</td><td>0.350327</td></tr><tr><td>Zone 2 (vs Zone 1)</td><td>-0.0454</td><td>0.6389</td><td>0.6505</td><td>0.070</td><td>0.944364</td></tr><tr><td>Zone 3 (vs Zone 1)</td><td>5.9474</td><td>1,071.7341</td><td>1,091.4423</td><td>0.005</td><td>0.995652</td></tr><tr><td>Zone 4 (vs Zone 1)</td><td>0.2780</td><td>0.9535</td><td>0.9631</td><td>0.289</td><td>0.772872</td></tr><tr><td>Pasture use (seasonal vs permanent)</td><td>0.3833</td><td>1.0516</td><td>1.0627</td><td>0.361</td><td>0.718347</td></tr><tr><td colspan="6"><strong>(b) Regarding HEV</strong></td></tr><tr><td>Age (young vs adult)</td><td>-2.2175</td><td>0.7844</td><td>0.7956</td><td>2.787</td><td>0.00532*</td></tr><tr><td>Pasture use (seasonal vs permanent)</td><td>-2.0819</td><td>0.9163</td><td>0.9314</td><td>2.235</td><td>0.02541*</td></tr><tr><td>Sows management (yes)</td><td>0.1078</td><td>0.3747</td><td>0.3784</td><td>0.285</td><td>0.77582</td></tr><tr><td>Sex (male vs female)</td><td>-0.3589</td><td>0.6538</td><td>0.6579</td><td>0.546</td><td>0.58538</td></tr><tr><td>Reproductive status (yes)</td><td>-1.5416</td><td>1.0227</td><td>1.0317</td><td>1.494</td><td>0.13511</td></tr></tbody></table>

free ranging farms and fenced pasture farms both exhibited a much higher seropositivity compared with closed farms, we finally merged both categories fenced pasture and free ranging categories since these two categories exhibited the same risk level (OR<sub>free ranging or open air/closed</sub> = 10.05, 95% CI [2.61; 38.76]). Other effects were not significant and were finally not retained (Table 2b).

## Seroprevalence and Risk Factors in Domestic Pigs from Six Traditional Farms in Boziu-Verde (Dataset 2)

The analyses were performed on 75 pigs with no missing data for any of the explanatory variables and serological results. Average seroprevalence in this dataset was 48.0% (95% CI [36.7%; 59.3%]) for ADV and 30.7% (95% CI [20.3%; 41.1%]) for HEV.

### Risk Factors Associated with ADV Seropositivity

We retained six potential best models according to their QAICc values (delta-QAICc < 2) (additional materials), but significant average effects only concerned pig reproductive status and management of sows in estrus (full model-averaged coefficients detailed in Table 3a). Reproductive pigs were more at

risk than others (OR<sub>breeder/fattening</sub> = 27.23, 95% CI [2.44; 303.68]) and the management of sows on estrus had a protective effect (OR<sub>no management/mating management</sub> = 67.45, 95% CI [6.43; 707.43]). Other effects were not significant and were finally not retained (Table 3a).

### Risk Factors Associated with HEV Seropositivity

We retained four potential best models according to their QAICc values (delta-QAICc < 2) (additional materials), but significant average effects only concerned pigs' age and the intensity of natural pasture/forests use (full model-averaged coefficients detailed in Table 3b). Adult pigs were more at risk than young ones (OR<sub>adult/young</sub> = 9.18, 95% CI [1.97; 42.73]), and farms permanently using natural pastures/forests showed a higher risk than farms only using them in autumn (OR<sub>permanent/autumn</sub> = 8.02, 95% CI [1.33; 48.32]). Other effects were not significant and were finally not retained (Table 3b).

## Seroprevalence and Risk Factors of ADV and HEV in Wild Boar from Boziu-Verde (Dataset 3)

Out of 297 wild boars sampled, 274 analysis could be performed (23 samples could not be analyzed because of sample quality

<footnote>*Significant p-value.</footnote>

<footnote>Significant parameters for each data set are indicated in bold.</footnote>

<footnote>*Significant p-value.</footnote>

<footnote>Significant parameters for each data set are indicated in bold.</footnote>
defaults). Average seroprevalence in this dataset was of 45.1% (95% CI [39.8%; 50.4%]) for ADV and 38.7% (95% CI [33.2%; 44.2%]) for HEV.

## Risk Factors Associated with ADV Seropositivity

We considered three potential best models according to their QAICc values (delta-QAICc < 2) (additional materials), but the significant average effects only concerned wild boar's age and wild boar seropositivity for HEV (full model-averaged coefficients detailed in Table 4a). Adult wild boars had a higher risk than young ones (OR<sub>adult/young</sub> = 3.80, 95% CI [2.14; 6.77]) and HEV seropositive animals were more at risk than seronegative ones (OR<sub>HEV+/HEV-</sub> = 2.06, 95% CI [1.51; 3.52]). Other effects were not significant and were finally not retained (Table 4a).

## Risk Factors Associated with HEV Seropositivity

We considered 11 potential best models according to their QAICc values (delta-QAICc < 2) (additional materials), but the significant average effects only concerned the intensity of pig presence on natural pastures/forests, the year 2014, and wild boar seropositivity regarding ADV (full model-averaged coefficients detailed in Table 4b). The hunting areas with permanent pig presence showed the highest seroprevalence (OR<sub>permanent/other</sub> = 3.63, 95% CI [1.12; 11.71]), while the seasonal use of pasture did not show higher seroprevalence than areas with no pig presence (p = 0.53). As previously observed, HEV and ADV serological status were correlated (OR<sub>aujeszky+/aujeszky-</sub> = 2.36, 95% CI [1.37; 4.08]). Year 2014 appeared at risk compared with other years. However, this result relied on a very low sample on that year and might thus correspond to a particular family/spatial cluster rather than a real year effect (OR<sub>2014/other</sub> = 9.07, 95% CI [1.92; 42.87]). Other effects were not significant and were finally not retained (Table 4b).

## DISCUSSION

We explored seroprevalence regarding two infectious pathogens corresponding to different transmission patterns (ADV with direct transmission; HEV with direct and indirect transmission

routes) at the interface of wild and domestic swine in Corsica. During the first step, we observed pigs' seroprevalences at a large scale. Regarding both diseases, the high seroprevalence observed in all pig age classes and the absence of differences in seroprevalence among the different microregions confirmed the enzootic situation in Corsican pigs. Regarding ADV, our results confirmed the protective effect of vaccination. The official vaccination plan proposed by the French animal health authorities<fnref n="1" /> (35) between 2011 and 2013 is highly effective in comparison with farms that do no vaccinate their pigs and also compared with the partial protective effect of vaccination performed outside the official vaccination plan. Regarding both diseases, we observed a higher risk in free ranging pig farms (and open air fenced farms for HEV) than in intensive indoor ones, suggesting that the wild boar/pig interface and/or extensive farming practices in the contact with the natural environment might play a role in the exposure to both diseases.

Concerning free ranging pig farms, we confirmed the protective effect of limiting the release of sows during estrus on pastures for ADV seroprevalence (i.e., by mating sows before transferring them to natural pastures/forests), suggesting a risk linked to contacts with wild boars and highlighting the value of mating management limiting ADV transmission on free ranging farms. We also observed a higher ADV seroprevalence in breeder pigs than fattening ones, which may reflect the higher risk of breeders and their longer life compared with fattening pigs. These results are consistent with the expected sexual transmission pattern associated with ADV and the polygynous reproductive pattern observed in swine. The level of seroprevalence was similar in wild boars and pigs (i.e., 40-45%) and matched with previous studies performed in Corsica confirming the fact that ADV has been circulating for a long time in both wild and domestic

<table><caption>TABLE 4 | Parameters estimates among the set of best models (model averaging) for dataset 3 (hunted wild boar from Boziu Verde).</caption><thead><tr><th></th><th>Estimate</th><th>SE</th><th>Adjusted SE</th><th>z-Value</th><th>Pr (&gt;|z|)</th></tr></thead><tbody><tr><td colspan="6"><strong>(a) Regarding Aujeszky's disease virus (ADV)</strong></td></tr><tr><td>Age (young vs adult)</td><td>-1.144144</td><td>0.269294</td><td>0.270408</td><td>4.231</td><td>2.32e-05*</td></tr><tr><td>Sero hepatitis E virus (HEV) (positive)</td><td>0.701042</td><td>0.270076</td><td>0.271184</td><td>2.585</td><td>0.00973*</td></tr><tr><td>Year (2014 vs other)</td><td>17.137850</td><td>912.961823</td><td>916.756557</td><td>0.019</td><td>0.98509</td></tr><tr><td>Sex (male vs female)</td><td>0.046974</td><td>0.148616</td><td>0.148972</td><td>0.315</td><td>0.75252</td></tr><tr><td>Hybrid status (yes)</td><td>-0.116537</td><td>0.248816</td><td>0.249249</td><td>0.468</td><td>0.64010</td></tr><tr><td colspan="6"><strong>(b) Regarding HEV</strong></td></tr><tr><td>Year (2014 vs other)</td><td>2.16122</td><td>0.78643</td><td>0.78969</td><td>2.737</td><td>0.00620*</td></tr><tr><td>Pig presence (permanent vs absent)</td><td>0.93853</td><td>0.32747</td><td>0.32876</td><td>2.855</td><td>0.00431*</td></tr><tr><td>Sero ADV (positive)</td><td>0.78865</td><td>0.28158</td><td>0.28269</td><td>2.790</td><td>0.00527*</td></tr><tr><td>Pig presence (intermittent vs absent)</td><td>0.09843</td><td>0.35303</td><td>0.35450</td><td>0.278</td><td>0.78126</td></tr><tr><td>Age (young vs adult)</td><td>-0.47865</td><td>0.35488</td><td>0.35562</td><td>1.346</td><td>0.17832</td></tr><tr><td>Sex (male vs female)</td><td>-0.03197</td><td>0.12827</td><td>0.12863</td><td>0.249</td><td>0.80369</td></tr><tr><td>Hybrid status (yes)</td><td>-0.10445</td><td>0.24468</td><td>0.24512</td><td>0.426</td><td>0.67003</td></tr></tbody></table>

<footnote n="1">¹Fédération Régionale des Groupements de Défense Sanitaire de Corse (FRGDSB20), 2014. Plan Expérimental de lutte contre la maladie d'Aujeszky—Bilan après trois années de mise en œuvre. Technical report presented in Ajaccio, on the 19th of September 2014. This report presents the technical results of the Aujeszky disease management strategy implemented by Health Authorities in Corsica from 2011 to 2014.</footnote>

<footnote>*Significant p-value.</footnote>

<footnote>Significant parameters for each data set are indicated in bold.</footnote>
populations (11, 12). This is one of the highest seroprevalence rates observed in wild boars in France (12) although wild boars are not as intensively managed in Corsica as in Central-Southern Spain (36, 37). We did not observe any additional risk in areas where free ranging pigs shared natural pastures/forest with wild boar, suggesting that the sylvatic and domestic epidemiological cycles are mostly independent even though the two populations are sympatric and sexual transmission is known to occur. In other European countries being in a situation of strict segregation, such as in Germany and continental Northern Italy, a complete asynchrony has been observed and different virus strains have been identified in the wild and domestic swine populations (38, 39). In our study, unmanaged domestic sows had a higher risk of becoming infected than other individuals, suggesting that sylvatic and domestic cycles are partly connected and that managing female in estrus limits the risk of ADV spill over. In addition, even if recent studies describe endemic patterns of ADV in wild boar populations, through female social behavior favoring intraspecific contacts (40, 41), further studies aiming at genotyping circulating virus strains, describing the space use and the genetic flow between domestic and wild populations are needed to better characterize these connections.

Regarding HEV, the study suggests a twofold lower exposure in wild boars (<40%) compared with domestic pig populations (>85%). The higher seroprevalence observed in domestic pigs suggests the spillover of HEV from pigs to wild boar rather than the contrary. However, since only wild boar populations from the Boziu-Verde were sampled in our study, we recommend a larger wild boar surveillance design, extended to other microregions combined with virus investigation and typing, to confirm this hypothesis. In free ranging farms (dataset 2), apparent seroprevalence (30%) was lower than expected when considering data set 1 (>90%); such a gap might correspond to a lower sensitivity of the serological ELISA test when using filter papers (used for dataset 1) instead of sera (used for dataset 2). The effect of domestic pig presence in hunting areas on wild boar seroprevalence suggests that inter-transmission does occur at the wild/domestic interface, possibly through environmental contamination and common attractors (e.g., contaminated water or food sources), as suggested by previous studies (16, 17). Nevertheless, the precise environmental risk factors (such as the role of water sources) for HEV transmission are still unclear and should be explored further in the future (42). Recent studies have suggested that hybrid wild boar populations might be more susceptible to certain infectious diseases than pure specimens (43, 44), and this association was also observed in Corsica when the prevalence of HEV was compared in populations of pure and hybrids wild boars (17). This potential role of hybrids was not confirmed by our analysis: once the use of natural habitats was taken into account, the hybrid status of wild boars was no longer significant, and we did not observe an additional risk in the domestic crossbred animals sampled in farms. These results suggest a confounding effect between animal population sympatry and the occurrence of hybrid individuals (these two factors being highly correlated) rather than a potential role of hybrids in HEV transmission between both populations. However, our data were based on phenotypical classification of hybrids which might not be precise enough to discern accurately

between hybrid and pure individuals. Further studies using appropriate genetic methods to determine hybridization (45, 46) are needed to assess the hypothetical link between hybridization and disease susceptibility in Corsica.

Our results suggest that ADV and HEV infections can occur simultaneously in domestic pigs and wild boars. To the best of our knowledge, this potential co-infection has never been investigated in domestic or wild pigs. Recent studies have shown that a co-infection between HEV and other immunosuppressive porcine viruses such as PRSS can influence HEV infection dynamics concerning times of excretion or maintenance of the virus in the liver (47, 48). Since ADV can also affect the liver, a co-infection with both viruses could potentially increase liver damage, but further studies are needed to explore the pathogenic effects of co-infection with both viruses. Since no synergic mechanisms have been described between these two swine pathogens to date, we rather hypothesize a possible effect of local aggregations factors (e.g., family groups, water, or food sources) that might facilitate the transmission of both contagious diseases in wild boar [such as described by Acevedo et al. (36) and Vicente et al. (49) in other Mediterranean areas].

It is important to mention that, as our results are only exploratory since they are based on small samples and exposed to biases, they should be taken with caution; it is obvious that other non-considered factors unrelated to the wild/domestic interface might influence wild boar and domestic pig exposure to diseases. The effect of the year 2014, while seroprevalence in wild boar was stable all over the other years for both diseases, could be due to such uncontrolled factors or a sampling bias (50).

Finally, if the exploratory character of our study shows interesting results, it is essential to formulate research perspectives to confirm and improve our findings. We focused on one microregion only (Boziu-Verde), as a first step to test our hypothesis, and a comparative approach with other microregions, with different characteristics (microregions without pig farming activities, for example, or with a different distribution of technically advanced farms, or with less numerous wild board presence, etc.), is strongly needed. Another perspective is to combine this type of study with molecular approaches to identify pathogen strains in wild boar and pig populations. In the case of HEV, several recent studies have shown that several strains are shared between pigs, wild boars, pork products, and humans (16, 51). Such approaches can be relevant to address the question of environmental contamination (52). Concerning ADV, a major issue is the technical difficulty to collect samples containing the virus, as Aujeszky's disease symptoms are difficult to notice in free ranging farming systems.

## CONCLUSION

To the best of our knowledge, this study represents the very first attempt to assess the use of disease seroprevalence values as indicators of the importance of wild boar/domestic pig interactions and the potential efficacy of disease management strategies for preventing disease maintenance and spread in an extensive pig farming environment such as the one occurring in Corsica. It provides evidence of the strong protective effect of ADV vaccination,
which should encourage farmers to commit to future vaccination programs. Moreover, we provide evidence of the benefits of reproductive management of sows (i.e., spaying or mating before release them into natural habitats). In the future, the cost-effectiveness of such measures on farm productivity (e.g., average litter size or pig growth rate) needs to be quantified. Our study highlighted a limited protective effect of pig farm biosecurity measures toward both diseases, suggesting that the wild/domestic interactions are possibly not the only one and nor the most important factor explaining disease dynamics in Corsican pigs. Our study provides evidence of the connection between wild and domestic disease cycles on traditional free ranging farms and the usefulness of promoting seasonal partial segregation of the swine populations. However, awareness needs to be raised among farmers and hunters on the likely presence of other unknown/uncontrolled factors unrelated to the wild/domestic pig interface. Therefore, further analysis and confirmation of these identified trends is recommended to explore the potential impact of other factors affecting the transmission of those pathogens and to better understand their dynamics and the impact of management measures.

## ETHICS STATEMENT

The study was exempt of authorization because the serological analysis were carried out on blood samples collected either on live pigs by veterinarian technicians at the farm level or at the slaughterhouse as part of a surveillance scheme, or on shot wild boars by hunters.

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## AUTHOR CONTRIBUTIONS

The two first authors contributed equally: FCharrier and SR. Conceived and designed the seroprevalence studies: FCharrier, OM, and FCasabianca. Performed the studies: FCharrier, FJ, OM, and FCasabianca. Analyzed the data: FCharrier, SR, FJ, CR, and FCasabianca. Carried out the statistical analyses: SR. Contributed reagents/materials/analysis tools: FCharrier, OM, FCasabianca, NP, M-FL, and JJ. Wrote the paper: SR and FCharrier. Critically read the manuscript: FJ, CR, CD, NP, and M-FL. Read and approved the final manuscript: FCharrier, SR, FJ, OM, CR, FCasabianca, CD, NP, and M-FL.

## ACKNOWLEDGMENTS

The authors thank Marie Pellerin and Aurélie Oger for technical assistance with HEV and Aujeszky's disease analysis. The authors also thank the farmers and hunters for their help in sampling operations and for their participation in interviews.

## FUNDING

This work was partly funded by the INRA-GISA Metaprogramme (Integrated Management of Animal Health, PaCMan) and by the European Union Seventh Framework Programme under grant agreement no. 311931 (ASFORCE).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Charrier, Rossi, Jori, Maestrini, Richomme, Casabianca, Ducrot, Jouve, Pavio and Le Potier. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
## 2. L'étude longitudinale de dispositifs et de situations de gestion

2.1. Applying participatory approaches in the evaluation of surveillance systems: A pilot study on African swine fever surveillance in Corsica. [#4]

**Référence:** [#4]

Calba, C., Charrier, F., Antoine-Moussiaux, N., Hendrikx, P., Saegerman, C., Peyre, M., Goutard, F. (2015). Applying participatory approaches in the evaluation of surveillance systems: A pilot study on African swine fever surveillance in Corsica. *Preventive Veterinary Medicine*, *122* (4), 389-398.
<figure><img src="image_72.png" /></figure>

# Applying participatory approaches in the evaluation of surveillance systems: A pilot study on African swine fever surveillance in Corsica

Clémentine Calba<sup>b,*</sup>, Nicolas Antoine-Moussiaux<sup>c</sup>, François Charrier<sup>d</sup>, Pascal Hendrikx<sup>e</sup>,
Claude Saegerman<sup>b</sup>, Marisa Peyre<sup>a</sup>, Flavie L. Goutard<sup>a</sup>

## ARTICLE INFO

**Article history:**

Received 15 January 2015

Received in revised form

22 September 2015

Accepted 1 October 2015

**Keywords:**

Participatory epidemiology

Surveillance

Evaluation

Acceptability

Non-monetary benefits

Corsica

## ABSTRACT

The implementation of regular and relevant evaluations of surveillance systems is critical in improving their effectiveness and their relevance whilst limiting their cost. The complex nature of these systems and the variable contexts in which they are implemented call for the development of flexible evaluation tools. Within this scope, participatory tools have been developed and implemented for the African swine fever (ASF) surveillance system in Corsica (France). The objectives of this pilot study were, firstly, to assess the applicability of participatory approaches within a developed environment involving various stakeholders and, secondly, to define and test methods developed to assess evaluation attributes. Two evaluation attributes were targeted: the acceptability of the surveillance system and its the non-monetary benefits. Individual semi-structured interviews and focus groups were implemented with representatives from every level of the system. Diagramming and scoring tools were used to assess the different elements that compose the definition of acceptability. A contingent valuation method, associated with proportional piling, was used to assess the non-monetary benefits, i.e., the value of sanitary information. Sixteen stakeholders were involved in the process, through 3 focus groups and 8 individual semi-structured interviews. Stakeholders were selected according to their role in the system and to their availability. Results highlighted a moderate acceptability of the system for farmers and hunters and a high acceptability for other representatives (e.g., private veterinarians, local laboratories). Out of the 5 farmers involved in assessing the non-monetary benefits, 3 were interested in sanitary information on ASF. The data collected via participatory approaches enable relevant recommendations to be made, based on the Corsican context, to improve the current surveillance system.

© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

## 1. Introduction

The regular and relevant evaluation of surveillance systems is essential to estimate the usefulness and the correct application of the data generated, and to ensure that limited resources are

used effectively to provide the evidence required for protecting animal and human health (Hendrikx et al., 2011; Drewe et al., 2015). According to the Health Systems Strengthening Glossary developed by the World Health Organisation (WHO), evaluation refers to 'the systematic and objective assessment of the relevance, adequacy, progress, efficiency, effectiveness and impact of a course of actions, in relation to objectives and taking into account the resources and facilities that have been deployed' (WHO, undated). Applied to surveillance, this includes the assessment of a series of evaluation attributes such as sensitivity, acceptability and timeliness, using qualitative, semi-quantitative or quantitative methods and tools (Drewe et al., 2012). The complexity of surveillance systems, and

<footnote><sup>a</sup> Centre de Coopération Internationale en Recherche Agronomique Pour le Développement (CIRAD), Département ES, UPR AGIRs, TA C22/E, Campus International de Baillarguet, 34398 Montpellier Cedex 5, France</footnote>

<footnote><sup>b</sup> Research Unit of Epidemiology and Risk Analysis applied to Veterinary Sciences (UREAR-ULg), Fundamental and Applied Research for Animal and Health (FARAH), Faculty of Veterinary Medicine, University of Liege, Quartier Vallée 2, Avenue de Cureghem, B-4000 Liege, Belgium</footnote>

<footnote><sup>c</sup> Tropical Veterinary Institute, Faculty of Veterinary Medicine, University of Liege, Quartier Vallée 2, Avenue de Cureghem, B-4000 Liege, Belgium</footnote>

<footnote><sup>d</sup> Institut National de la Recherche Agronomique (INRA), Laboratoire de Recherches sur le Développement de Lélevage (LRDE), Quartier Grosseti, BP 8, 20250 Corte, France</footnote>

<footnote><sup>e</sup> French Agency for Food, Environmental and Occupational Health Safety (ANSES), 31 Avenue Tony Garnier, 69394 Lyon Cedex 07, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: clementine.calba@cirad.fr (C. Calba), nantoine@ulg.ac.be (N. Antoine-Moussiaux), charrier@corse.inra.fr (F. Charrier), pascal.hendrikx@anses.fr (P. Hendrikx), claude.saegerman@ulg.ac.be (C. Saegerman), marisa.peyre@cirad.fr (M. Peyre), flavie.goutard@cirad.fr (F.L. Goutard).</footnote>
the variable context in which they are implemented, entail the need for flexible evaluation tools designed to take into account the opinion of each stakeholder. This can be achieved by using flexible and adaptable methods based on participatory approaches within the evaluation process.

Participatory approaches refer to a range of methods and tools that enable stakeholders, to a variable extent, to play an active role in the definition and in the analysis of the problems they may encounter, and in their solution (Pretty, 1995; Pretty et al., 1995; Johnson et al., 2004; Mariner et al., 2011; Peyre et al., 2014). Indeed, the use of visualization tools through participatory approaches leads to open discussion between stakeholders and encourages a wide participation (Bradley et al., 2002). By taking stakeholders' perceptions, needs and expectations into consideration, these approaches could help us to achieve a better understanding of the system (Hoischen-Taubner et al., 2014). These methods make it possible to capture locking points in the system, such as communication and coordination between stakeholders, which can go unnoticed when using classical evaluation tools. The use of these tools should give rise to realistic and context-adapted recommendations. More importantly, these tools lead to enhanced acceptability of the evaluation, to an improved feeling of belonging to the system, and to even ownership of the evaluation outputs (Pahl-Wostl, 2002).

Factors used to assess the quality of system implementation (e.g., acceptability, communication), or the non-monetary costs and benefits of surveillance, are rarely considered despite their importance for decision makers and their impact on system performance (Calba et al., 2015; Peyre et al., 2014). Acceptability refers to the willingness of persons and organizations to participate in the surveillance system, and to the degree to which each of these users is involved in the surveillance (Hoinville et al., 2013); it has been listed by the Centers for Disease Control and Prevention (CDC) as one of the main qualities of surveillance (German et al., 2001). The decision to report a suspected event is a critical function of an emerging infectious disease surveillance system (Tsai et al., 2009). In order to limit the under-reporting of suspected cases and to identify the best ways to improve the current surveillance system, it is crucial to assess the stakeholders' willingness to participate in this system (Bronner et al., 2014). Non-monetary benefits refer to the positive direct and indirect consequences produced by the surveillance system and help to assess whether users are satisfied that their requirements have been met (definition developed by the RISKSUR<fnref n="1" /> Consortium). The objective of this work was to develop methods and tools based on sociology, economics and participatory approaches to assess the acceptability of animal health surveillance systems and their non-monetary benefits through an estimation of the perceived economic value of sanitary information.

A pilot study was implemented in Corsica in order to test the applicability of these methods and tools in a developed context. The case of African swine fever (ASF) surveillance in Corsica was chosen for two main reasons. Firstly, current farming practices are mainly based on a traditional forest-pastoral system (outdoor free-range breeding) (Casabianca et al., 1989), and only a small number of rural private veterinarians work on the island (personal communication, Oscar Maestrini, INRA). Secondly, Corsican breeding systems are threatened by the endemic presence of ASF in Sardinia; this questions the current surveillance system faced with increased risk of introduction, spread and maintenance of ASF through Corsica (Desvaux et al., 2014; European Commission, 2011; Mur et al., 2014a). Indeed, ASF has been recognized to be among the most devastating of pig diseases with severe socio-economic consequences

<figure><img src="image_73.png" /></figure>

* GDS: Animal health grouping

<caption>**Fig. 1.** Graphical representation of the African swine fever (ASF) surveillance system in Corsica (France).</caption>

(Moennig, 2000; Costard et al., 2013; Torre et al., 2013; Mur et al., 2014b).

Originally, the surveillance system targeted both ASF and Classical swine fever (CSF) but, due to the increasing threat, public authorities decided to redirect surveillance to target principally ASF. The objective of this system is to ensure the early detection of both diseases by using a passive surveillance approach based on clinical findings within the entire population of domestic pigs and wild boars. The system thus relies on the willingness of stakeholders to report suspicions, particularly given the fact that it is impossible to regularly assess the health of each animal (Sawford, 2011).

## 2. Material and methods

### 2.1. Description of the surveillance system and target population

Our first approach consisted of identifying stakeholders involved in the surveillance system. These were then divided into three levels (Fig. 1). Level 1 included farmers and hunters, who are on the front line of passive surveillance. In the event of a suspected case of ASF in farm animals, or among the wild animal population, they are supposed to contact the next level in the surveillance network (level 2) which can be composed of private veterinarians, of "Groupements de Défense Sanitaire" animal health groups (GDS, association of farmers addressing health issues, officially recognized by French law (Bronner et al., 2014)), of local laboratories, or of wildlife organizations (hunters' federations, for example). Any suspicions must be declared to the Veterinary Services, at local, regional, and national levels. These stakeholders represent the third level in the surveillance system (level 3). They are in direct contact with the authorities in charge of animal health surveillance coordination, the Directorate General for Food (DGAL), which is supervised by the French Ministry of Agriculture, Agribusiness and Forest (MAAF).

<footnote n="1">¹ Risk-based animal health surveillance systems, EU project (www.fp7-risksur.eu).</footnote>
Participants were thus selected according to their role in the surveillance system (i.e., according to the level to which they belonged), and also according to their availability and willingness to participate. Using a contact list provided by the National Institute for Agricultural Research (INRA), stakeholders were identified and individually contacted by phone.

Participants were interviewed using focus groups or individual semi-structured interviews. Focus groups are designed to expose a group of people to common stimuli (Pahl-Wostl, 2002). They are particularly important in assessing complex issues through the analysis of social processes and discussions (Pahl-Wostl, 2002). The data collection process relied on interviewing representatives at every level of the surveillance system. Indeed, it is common in qualitative approaches to rely on 'purposive sampling' to maximize the diversity of the data collected (i.e., perceptions and point of views) (Glaser and Strauss, 1967; Corbin and Strauss, 1990). The quality of the sample is therefore considered to be more important than the sample size in such approaches (Côte and Turgeon, 2002). Another objective was to reach theoretical saturation which has become the gold standard for health science research (Guest et al., 2006) and which refers to the point at which no new information is observed in the data (Guest et al., 2006).

The intention was to implement focus groups with (i) ten farmers (2 groups of 5 participants), and (ii) 5 hunters (one group) for level 1; (iii) 5 private veterinarians (one group), and (iv) 3 GDS technicians (one group) for level 2. For other stakeholders, the intention was to implement individual semi-structured interviews: with representatives from each local laboratory (two in Corsica), and one representative of a wildlife organization for level 2; two representatives of Veterinary Services at the local level, and one at the regional level for level 3.

Interviews were conducted between April and June 2014 by a team of 2–3 evaluators: one was in charge of leading the discussion, and the others were responsible for observing participant behavior and taking notes. All of the interviews were recorded with the participants consent and were subsequently transcribed into text format using Microsoft Word software (Microsoft Office 2010, Redmond, WA 98052-7329, USA).

### 3. Assessment of acceptability

Acceptability is relevant to different aspects of the surveillance system. It first refers to the actors' acceptance of the system's objectives and of the way it is operates. The acceptance of the way the system operates refers to (i) the role of each actor and the representation of their own utility, (ii) the consequences of the flow of information for each actor (i.e., changes in their activity and in their relations following a suspicion), (iii) the perception by each actor of the importance and recognition of their own role relative to that of other actors, and (iv) the relations between stakeholders. Trust is another essential element of acceptability; trust in the system and also trust in other stakeholders involved in the system. These elements were assessed using a combination of participatory diagraming and scoring tools, both of which were developed for, and adapted to, this specific context. Three main tools were implemented: (i) relational diagrams, (ii) flow diagrams (associated with proportional piling), and (iii) impact diagrams (associated with proportional piling). These tools were implemented with all participants, either through focus groups or through individual semi-structured interviews.

#### 3.1. Relational diagrams

Relational diagrams were developed and used to identify professional networks and interactions among stakeholders. The

participants' status or organization was placed in the middle of a flip chart. Facilitators then asked them to list the stakeholders and organizations with which they interacted and to describe these interactions (i.e., frequency and reciprocity).

##### 3.1.1. Flow diagrams and proportional piling

Flow diagrams were developed and used to assess the participants' knowledge of the information flow in the case of suspected ASF and to identify how the information circulated. The diagrams were developed beginning with a representation of level 1 stakeholders (i.e., farmers or hunters) for whom participants were asked to show the customary flow of information within the system, i.e., to which stakeholder, or organization, the suspicion would be reported. Once the participants considered the diagram to be complete, proportional piling was performed to quantify the level of trust they had in the system (providing a percentage) and in the other stakeholders involved. This technique allowed participants to give relative scores to a number of different items or categories according to one criterion (Hendrickx et al., 2011). The method was based on visualization, but results were recorded numerically (Catley et al., 2012). Facilitators asked the participants to divide 100 counters into two parts, one representing their confidence in the system and their lack of confidence. The counters allocated to confidence were then used to specify the level of confidence in the actors and organizations represented in the diagram.

##### 3.1.2. Impact diagrams and proportional piling

Impact diagrams, adapted to assess both positive and negative impacts of a specific event, are useful to document the consequences as experienced directly and indirectly by stakeholders (Kariuki and Njuki, 2013). In this pilot study, the specific event was a suspicion of ASF in Corsica. Facilitators asked the participants to list and explain the positive and negative impacts of a suspicion in their own work, organization and relations. Proportional piling was then implemented on the diagram by first dividing the 100 counters between positive and negative impacts according to their weights, and then by splitting the counters across the identified impacts to assess their probability of occurrence.

### 4. OASIS flash evaluation

OASIS is a standardized semi-quantitative assessment tool which was developed for the assessment of zoonotic and animal disease surveillance systems (Hendrikx et al., 2011). This tool is based on a detailed questionnaire used to collect information to describe the operation of the system under evaluation. The information collected is synthesized according a list of criteria (78 in total), for which participants provide scores (from 0 to 3) following a scoring guide.

There are two ways of implementing an OASIS evaluation. One way is to complete the questionnaire directly with stakeholders through interviews; another way ('OASIS flash') is to complete the questionnaire based on the available documentation. Due to time constraints, it was decided to implement an OASIS flash evaluation.

### 5. Assessment of non-monetary benefits

The economic value of sanitary information was assessed through a contingent valuation method (CVM) using proportional piling and was implemented through individual semi-structured interviews with farmers. This method has been used by economists to value changes in natural resources and environments, and it is somewhat similar to methods used in marketing to evaluate new concepts for goods and products (Louviere et al., 2003). It has recently been adapted to the evaluation of animal health
surveillance in South East Asia (Delabouglise et al., 2015). This method consists of direct interviews during which facilitators ask individuals what they would be willing to pay for a change (Louviere et al., 2003); in the present study, they were asked what they would be willing to pay for sanitary information related to ASF.

As presented in Fig. 2, the first step of the process was for farmers to identify and to draw up a list of the main expenditure items for their farms. Facilitators asked them to give an average cost of these expenditures for one year. Proportional piling was then used for these expenditures in order to represent their costs with 100 counters. The second step was to highlight which information on ASF was of interest to the interviewee: which type of sanitary information and at which geographical level (e.g., village, commune, region). This information was then added to the list of expenditures; the facilitator asked participants to divide the counters used for the first step so as to represent their interest in this information and then to explain their choice.

# 6. Data analysis

## 6.1. Assessment of acceptability

Each element of acceptability was assessed by analyzing the pictures of the diagrams and also by using the transcribed discussions as stated in Table 1. The discussions were transcribed using Microsoft Word software. The acceptability of the objective of the surveillance system was assessed using the qualitative data collected during the elaboration of the impact diagrams (i.e., discussions). The acceptability of the way the system operated was assessed using all three diagrams (relation diagrams, flow diagrams, and impact diagrams) and using the qualitative data collected whilst they were being drawn (Table 1). The trust in the system as a whole and in other stakeholders was analyzed on the basis of the proportional piling implemented on flow diagrams, and by analyzing the qualitative data collected during the implementation.

Following this first analysis, and in order to be able to compare results obtained for each level, qualitative data were converted into semi-quantitative data. Thus, evaluation criteria were developed for each element. Each criterion was assigned a score as follows: low (-1), medium (0), or high (+1). This scale from -1 to +1 was selected in order to facilitate the representation of the results, using 0 as a central value.

The first step of the analysis was implemented at the interview level (i.e., focus group or individual semi-structured interview) and the scores obtained were used to calculate the arithmetic mean for each level using Microsoft Excel software (Microsoft Office 2010, Redmond, WA 98052-7329, USA). According to the mean value, the acceptability of each element was defined, at each level, as low (-1 to -0.33), medium (-0.32 to +0.33), or high (+0.34 to +1). These intervals were chosen with the objective of dividing the total distribution space into three equal parts.

## 6.2. Assessment of non-monetary benefits

Farmers were asked to provide a list of the main expenditures with their associated costs representing their production costs in the farm for the last year. Proportional piling was implemented on expenditures and the economic value of each counter was calculated. This value was then used to estimate the economic value of sanitary information and the willingness of participants to pay for it.

## 6.3. Comparison with the OASIS flash evaluation

Seven stakeholders were invited to join the scoring process: four representatives of the Veterinary Services (one from the local level, one from the national level and two from the regional level), one representative of the animal health association, one representative of the local laboratory and one private veterinarian.

The assessment of acceptability was based on 20 criteria according to the OASIS flash method, which can be grouped into 8 main categories: the organization of the surveillance system (e.g., existence of a charter), its animation (e.g., meetings frequencies), and organization (e.g., integration of laboratories in the system), the human and material resources, feedback to stakeholders, consequences of a suspicion, training provided, partnerships and stakeholder sensitization.

# 7. Results

## 7.1. Demographics of the interviews

A total of 16 actors were included, of which 3 were women and 13 were men. Eight stakeholders were involved through focus groups, and 8 through individual semi-structured interviews (Table 3). Three focus groups were held: one with 3 farmers, one with 3 representatives of the GDS (including one woman), and another one with two representatives of the Veterinary Services at the regional level (including one woman). Eight individual semi-structured interviews were implemented: 2 farmers/hunters, 3 hunters, one private veterinarian, one representative of the local laboratory, and one representative of the local Veterinary Services (woman). Focus groups lasted between 2 and 3 h while individual semi-structured interviews lasted 2 h on average. In addition, a total of 5 individual semi-structured interviews targeting the non-monetary benefits were implemented with farmers (men), each lasting 1 h.

## 7.2. Acceptability

### 7.2.1. Implementation of the tools

Relational diagrams were easily implemented with most stakeholders, and were mostly well-understood. This tool was a good way to introduce the process. It allowed participants to discuss their work and the relations they have with other stakeholders. The implementation of this tool was more complicated with 'isolated' participants (some hunters and farmers) due to their poor/inexistent professional network.

Flow diagrams allowed the collection of information relative to participants' knowledge about the system and the identification of the formal and informal pathways for transmission of suspicion information within the system. The implementation of flow diagrams was also more difficult with 'isolated' participants. The implementation of proportional piling was initially complex for participants to understand but all of them gained a clear understanding of the approach. Moreover, participants spontaneously explained their choices in the number of counters allocated to each stakeholder during the course of the activities. Nonetheless, this tool could not be implemented during the farmers' focus group. Indeed, they were reluctant to 'evaluate' the identified stakeholders through the proportional piling.

Impact diagrams were problematic, and not easily understood by participants. They had trouble identifying positive impacts following a suspicion, mostly due to the fact that they were focusing more on outbreaks rather than on suspected cases. Regarding the proportional piling implemented on these diagrams, the first step of the process (i.e., dividing the counters between the positive and
<figure><img src="image_74.png" /><figcaption>**Fig. 2.** Contingency valuation method associated with proportional piling to assess the economic value of the information of interest. 1st step—proportional piling was implemented on expenditures and the economic value of each counter was calculated. 2nd step—the participants were asked to represent their willingness to pay for sanitary information by tacking counters from the already listed expenditures items to a circle representing information.</figcaption></figure>

<caption>**Table 1**</caption>

Participatory methods and tools used to assess the acceptability of animal health surveillance systems.

<table><thead><tr><th>Acceptability elements</th><th>Associated questions</th><th>Associated participatory methods and tools</th></tr></thead><tbody><tr><td>Objective</td><td>Is the objective(s) of the surveillance system in the line with the stakeholders' expected objective(s)?</td><td>Impact diagram</td></tr><tr><td>Operation<br>Role of each actor and representation of its own utility</td><td>Are stakeholders satisfied with their duty?</td><td>Flow diagram</td></tr><tr><td>Consequences of information flow</td><td>Are stakeholders satisfied with the consequences of information flow?</td><td>Impact diagram associated with proportional piling</td></tr><tr><td>Perception by each actor of its own role relative to other actors'</td><td>What is the perception of each actor of its own role relative to other actors'?</td><td>Flow diagram</td></tr><tr><td>Relations between stakeholders</td><td>Are stakeholders satisfied with the relations they have with other stakeholders?</td><td>Relational diagram</td></tr><tr><td>Trust<br>In the system</td><td>Do stakeholders trust the system to fulfil its surveillance objective(s)?</td><td>Flow diagram associated with proportional piling</td></tr><tr><td>In other stakeholders involved in the system</td><td>Do stakeholders trust the other stakeholders to fulfil their role in the system?</td><td>Flow diagram associated with proportional piling</td></tr></tbody></table>

negative impacts) was easily implemented; whereas the second step (i.e., dividing the counters between the different identified impacts) was more confusing for some participants and it took more time for them to understand the process.

### 7.2.2. Scoring criteria

Based on the analysis of the qualitative data gathered during the discussions, and the analysis of the diagrams and proportional piling, scoring criteria for each element of acceptability were developed (Table 2).

Information provided by relational diagrams was converted into quantitative data. To measure the frequency level, each arrow was associated to a numerical value: 0 for very rare, 2 for rare, 4 for regular and 6 for very common (Table 2). The same process was implemented for reciprocity: 0 when there was no relation, 2 when it was one-sided and 4 when the relation was mutual (Table 2).

Nonetheless, 'the perception by each actor of the importance and recognition of their own role relative to other actors' could not be assessed using the collected data due to the fact that this element did not appear spontaneously in a sufficient number of interviews. Therefore it has been left out from the present analysis.

<caption>**Table 3**</caption>

Demographics of the interviews implemented for the participatory approaches and for the OASIS flash evaluation tool in the scope of the assessment of the African swine fever (ASF) surveillance system acceptability in Corsica.

<table><thead><tr><th>Evaluation process</th><th>Participants</th><th>Number Interview type</th></tr></thead><tbody><tr><td rowspan="6">OASIS</td><td>VS—National level</td><td>1</td></tr><tr><td>VS—Regional level</td><td>1</td></tr><tr><td>VS—Local level</td><td>1</td></tr><tr><td>GDS</td><td>1</td></tr><tr><td>Total</td><td>4</td></tr><tr><td>Participatory approaches</td><td></td><td></td></tr><tr><td></td><td>Farmers</td><td>3</td></tr><tr><td></td><td>Farmers/hunters</td><td>2</td></tr><tr><td></td><td>Hunters</td><td>3</td></tr><tr><td></td><td>Private veterinarian</td><td>1</td></tr><tr><td></td><td>GDS</td><td>3</td></tr><tr><td></td><td>Laboratory</td><td>1</td></tr><tr><td></td><td>VS—Local level</td><td>1</td></tr><tr><td></td><td>VS—Regional level</td><td>2</td></tr><tr><td></td><td>Total</td><td>16</td></tr></tbody></table>

### 7.2.3. Participatory assessment

Elements of acceptability were scored according to the criteria developed. These results are summarized in Fig. 3.