W L A Loeffen

K Zarafshani

Armin R W Elbers
email: armin.elbers@wur.nl

M J Gorgievski-Duijvesteijn

P G Van Der Velden

PhD Msc Mscag

A socio-psychological investigation into limitations and incentives concerning reporting a clinically suspect situation aimed at improving early detection of Classical Swine Fever outbreaks

Keywords:

A socio-psychological investigation into limitations and incentives concerning reporting a clinically suspect situation aimed at improving early detection of Classical

Swine Fever outbreaks

ABSTRACT

The aim of this study was to identify limitations and incentives in reporting clinically suspect situations, possibly caused by Classical Swine Fever (CSF), to veterinary authorities with the ultimate aim to facilitate early detection of CSF outbreaks. Focus group sessions were held with policy makers from the veterinary authorities, and representatives of veterinary practitioners and pig farmer unions. Personal interviews with a small group of pig farmers and practitioners were held to check limitations raised and solutions proposed during the focus group sessions. An electronic questionnaire was mailed to pig farmers and practitioners to investigate perceptions and attitudes with respect to clinically suspect situations possibly caused by CSF. After triangulating the responses of veterinary authorities, veterinary practitioners and farmers, six themes emerged across all groups: 1) lack of knowledge on the early signs of CSF; 2) guilt, shame and prejudice; 3) negative opinion on control measures; 4) dissatisfaction with post-reporting procedures; 5) lack of trust in government bodies; 6) uncertainty and lack of transparency of reporting procedures.

The following solutions to facilitate early detection of CSF were put forward: a) development of a clinical decision support system for vets and farmers, in order to get faster diagnosis and detection of CSF; b) possibility to submit blood samples directly to the reference laboratory to exclude CSF in a clinical situation with non-specific clinical signs, without isolation of the farm and free of charge for the individual farmer; c) decrease social and economic consequences of reporting CSF, for example by improving the public opinion on first reports; d) better schooling of veterinary officers to deal with emotions and insecurity of farmers in the process after reporting; e) better communication of rules and regulations, where to report, what will happen next; f) up-to-date website with information and visual material of the clinical signs of CSF.

A c c e p t e d M a n u s c r i p t

INTRODUCTION

Outbreaks of notifiable contagious animal diseases (NADs), such as foot-and-mouth disease (FMD), avian influenza (AI) and classical swine fever (CSF), have large societal and personal consequences. Livestock farmers and veterinary practitioners are at the frontline of surveillance, and hence it is widely recognized that they play a key role in detecting first occurrences of NADs. In theory, notification of contagious livestock diseases by farmers to the veterinary authorities can be an effective early detection tool. Therefore, formal rules for reporting clinically suspect situations in livestock by farmers and veterinary practitioners are laid down in national and international legislation all over the world. In the Netherlands it all started with the Dutch Cattle Act, which was officially put into force in the year 1870 [START_REF] Wester | The fight against contagious animal diseases between 1870 -1900[END_REF]. It consisted of a list of contagious diseases like rinderpest, anthrax, rabies and FMD. Furthermore, it contained regulations for reporting of affected and suspect livestock to the mayor of the municipality by farmers; obligatory reporting by veterinary practitioners; isolation and prohibition of transport of sick and suspect livestock, etc. In essence, not much has changed with respect to the reporting and eradication process of NADs since those early days. Yet, we cannot conclude that the regulations produce a desired effect, because in spite of strict rules and regulations, experience has shown that the time between the first clinical appearance of a NAD and the actual reporting of farmers of clinically suspect situations to the veterinary authorities is often too long, resulting in extensive spread of the disease to other farms [START_REF] Capua | The avian influenza epidemic in Italy, 1999-2000[END_REF]CFIA, 2004;[START_REF] Elbers | The classical swine fever epidemic 1997-1998 in the Netherlands: descriptive epidemiology[END_REF][START_REF] Elbers | The highly pathogenic Avian Influenza A (H7N7) virus epidemic in the Netherlands: lessons learned from the first five outbreaks[END_REF][START_REF] Gibbens | Descriptive epidemiology of the 2001 foot-and-mouth disease epidemic in Great Britain: the first five months[END_REF].

Investigation of the scarce empirical evidence to date on issues concerning delayed reporting and underreporting of clinically suspect situations shows that the problem thus far has mostly been approached as a veterinary-technical problem [START_REF] Elbers | Substantial improvement of early detection of notifiable animal diseases: a call for unorthodox changes[END_REF]. If livestock farmers and veterinary practitioners are familiar with the clinical signs of a NAD, they are in the best A c c e p t e d M a n u s c r i p t 4 position to detect NAD suspects. However, often these diseases have not been in the country for many years or sometimes even decades, and farmers and some veterinary practitioners do not recognize the associated clinical signs any more [START_REF] Elbers | Quantitative assessment of the quality of clinical signs for the detection of classical swine fever outbreaks[END_REF]. Furthermore, many endemic animal diseases cause clinical signs similar to NADs. After a considerable period of freedom from NADs in a country, farmers and vets will have a tendency to think that clinical signs observed are caused by an endemic disease and not by a NAD. As a result, farmers fail to recognize the need to report these early clinical signs of NADs, which implies that the time needed for ultimate detection of a new infection would provide time for the disease agent to spread. For instance, many case reports indicated CSF was suspected only after prolonged medication had failed to produce desired results [START_REF] Young | The use of supplemental tests in the diagnosis and eradication of Hog Cholera[END_REF][START_REF] Elbers | The classical swine fever epidemic 1997-1998 in the Netherlands: descriptive epidemiology[END_REF]. Laboratory confirmation would be necessary in order to exclude NADs being the cause of the clinical problems observed. However, laboratory confirmation is in many national regulations only allowed after reporting to the veterinary authorities. Hence, asking for laboratory confirmation may lead to control measures, such as isolation of the farm, until the results of diagnostic testing are available. Moreover, isolation of the farm, especially if this happens for several days, may have negative economic consequences for the farmer.

Only recently, socio-psychological factors have become the focus of interest as possible predictors of delayed reporting of clinically suspect situations by farmers and veterinary practitioners. Results of a qualitative study among Australian sheep farmers on implementing biosecurity measures [START_REF] Palmer | No worries mate: West Australian farmers' attitudes to infectious livestock diseases[END_REF] showed that one of the basic issues that may underlie the problem of not reporting clinically suspect situations to either the local agricultural department office or even a veterinarian may be a low level of trust in the government as well as agricultural extension agents. This lack of trust in government bodies also appeared as an important factor why farmers do not trust government information on improving biosecurity measures [START_REF] Heffernan | An exploration of the drivers to biosecurity collective action among a sample of UK cattle and sheep farmers[END_REF]. This lack of trust is based on showing vigilance in reporting scrapie-associated clinical signs [START_REF] Hopp | Norwegian farmers' vigilance in reporting sheep showing scrapie-associated signs[END_REF], indicated that reporting was dependent on both economic and non-economic values. Among the economic values considered important by farmers were being offered free examination of NAD suspects. Knowledge of disease-associated clinical signs by farmers and worries about blaming oneself for experiencing the disease ranked high among the non-economic values.

Increasing the reporting rate and shortening the delay time for reporting is crucial, but it is complicated by the fact that little is currently known about the way farmers behave in possible clinically suspect situations, more specifically, their perception and appraisal of the situation, the decision process that follows, and the intentions and behaviors that flow from these perceptions and decisions.

The purpose of our study was to identify limitations and incentives in reporting clinically suspect situations possibly caused by CSF as perceived by veterinary authorities, pig farmers and veterinary practitioners, with the ultimate aim of improving early detection of CSF outbreaks.

METHODS

To learn more about why farmers decide to report or not to report clinically suspect situations of NADs, our study combined a qualitative and a quantitative research design. For the qualitative part of our study, focus group sessions were held with a group of policy makers of the Ministry of Agriculture (4 persons) that were among others responsible for animal health policy and regulation; the Food and Consumer Protection Authority (2 persons from the head office responsible for disease eradication) that is responsible for the actual emergency response when a suspicion is reported or an outbreak is detected; Board members of several A c c e p t e d M a n u s c r i p t 6 livestock sections from the Royal Dutch Veterinary Association (6 persons: livestock practitioners themselves, but with an interest in veterinary policy making) and with Board members of all three pig farmer unions present in the Netherlands (3 persons, also pig farmers themselves) to detect patterns and trends. Subsequently, personal in-depth interviews with 12 pig farmers (randomly selected from a registry of all Dutch pig farmers) and 5 veterinary practitioners (with pig farms in their practice, selected to be more or less representative for different geographical areas within the Netherlands) were held to check if there might be other limitations, solutions and incentives with respect to reporting clinically suspect situations as suggested in the focus group meetings. Most of them had experience with a CSF outbreak in their neighbourhood in the past (not necessarily on their own farm), some had experience with respect to a visit by the veterinary authorities to their farm due to reporting of a suspect clinical situation identified at the slaughterhouse. Based on the results of the qualitative research, an electronic questionnaire was sent via an e-mail newsletter to members of a large pig farmer organization and posted for three weeks on the website of the Royal Dutch Veterinary Association. The questionnaire was subdivided into four sections. Section a) asked when and under what conditions one would report a clinically suspect situation. Section b) asked about feelings and (economic) consequences one expected after reporting a clinical suspicion. These questions were formulated both for the case that, retrospectively, clinical signs would indeed turn out to be caused by CSF (true positive), as well as the situation that in retrospect it would become clear that this was not caused by CSF (false positive). Section c) asked about barriers for reporting; and d) about opinions on national regulation explaining when and how to report a clinically suspect situation. Finally, the questionnaire did not just probe into possible limitations, but also possible solutions to break down the barriers.

A c c e p t e d M a n u s c r i p t 7 A grounded theory approach [START_REF] Strauss | Basics of qualitative research: Techniques and procedures for developing grounded theory[END_REF] was used to analyze the content of focus group and in-depth interviews. Each discrete incident, idea, or event was given a name or code word that represented the concept underlying the observation. Coded data were then isolated, reviewed, and interpreted line by line, to form categories and sub-categories until theoretical saturation was assumed [START_REF] Patton | Qualitative evaluation and research methods, 3rd Edition[END_REF]. Finally, categories and sub-categories were integrated to form substantive themes. Overall, six themes emerged from the data.

With respect to the electronic questionnaire, relative differences in opinions and attitudes between veterinary practitioners and pig farmers were tested with a χ 2 statistic [START_REF] Statistix | Statistix for Windows, version 7.0, Analytical Software[END_REF].

RESULTS

Focus groups

A summary of items indicated as limitations for reporting clinically suspect situations possibly caused by a notifiable pig disease by the veterinary authorities, veterinary practitioners and pig farmers is shown in Table 1. In Table 2, a summary of possible solutions put forward by the focus groups is shown. After triangulating the responses of veterinary authorities, veterinary practitioners, representatives of farmers organizations and pig farmers in the focus groups and in-depth interviews, six themes emerged across all groups.

Theme 1. Lack of knowledge on the clinical signs of CSF

During group discussions, government officials expressed their concern that farmers and perhaps even veterinary practitioners might be unfamiliar with clinical signs associated with CSF. Outbreaks of some diseases have been more than ten years ago, and this might be a major cause of not reporting clinically suspect situations possibly caused by CSF. Farmers in The Netherlands have more freedom for diagnosing and treating the animals themselves. A A c c e p t e d M a n u s c r i p t 8 large proportion of pig farms (in majority sow farms) are visited every month by a veterinary practitioner in the framework of an Integrated Quality Control Agreement. Within this framework, pig farmers are allowed to have a stockpile of antibiotics enabling treatment of pigs for the period between visits of the veterinary practitioner and they are allowed to treat their animals with these antibiotics without notifying their veterinary practitioner. Small pig finishing farms might see their veterinary practitioner once every two months. During the visit by the veterinary practitioner, the use of the antibiotics is discussed with the farmer. There is an obligation by law in the Netherlands (MANFQ, 2005) that pig farmers submit blood samples (to exclude CSF) to the laboratory within 24 hours after a group of pigs with clinical signs of an infectious disease are treated with medicines. Right after the large CSF-epidemic in 1997-1998 in the Netherlands, a considerable number of farmers complied to this regulation, but in the years after it trickled down to less than a 50-100 submissions annually (FSA, 2003) In order to make an accurate clinical diagnosis, knowledge about clinical signs associated with pig diseases is an important job competency for pig farmers. Overall, farmers agreed that the first risk assessment is made by the farmer himself. Several farmers admitted they might not be able to recognize certain NADs. For example, when presented with written cases of pigs with early clinical signs of CSF, many farmers responded that they saw these symptoms at least once a week, but did not think it could be caused by CSF. In reality, half of these cases were real life cases of CSF.

Theme 2. Guilt, Shame and Prejudice

Public opinion and social norms were identified by farmers as significantly influencing their practice of biosecurity. Farmers interviewed in our study felt that if they reported, and especially if they would be the first farmer to report, other farmers might think they had done something wrong. This relates to Theme 1, namely many people have erroneous opinions on A c c e p t e d M a n u s c r i p t 9 how diseases spread. A reflection from the focus group meeting: …."Many people would agree that farmers with poor hygiene who have illegal practices run a higher risk to introduce an animal disease on their farm. Hence people who admit they may have an animal disease on the farm are afraid others may think they are unhygienic and have illegal practices". A farmer commented that farmers who are the first to report "should be made heroes in the public opinion instead of criminals." Connected to this theme, many farmers expressed their dissatisfaction with the obtrusive "circus", as they called the procedures after reporting. They referred to the visits of the specialist-team of veterinarians and governmental officials that investigate the seriousness of the report. In order to prevent eventual spread of the NAD, these officials park their cars outside the premises, and walk to the farm wearing white protective suites and carrying red suitcases with tests and instruments. In the densely populated agricultural areas in The Netherlands, where you can sometimes easily spot at least half a dozen farm houses in the flat scenery, this means that the village knows about a possible suspect situation within no time.

Individual farmers accused each other of giving the company interest priority over sector interest. "When farmers suspect animal diseases, they just quickly sell their suspect animals to the slaughter house and wait at least a few days before reporting, so that they can effectuate important deliveries before a possible isolation of the farm." During group discussions, several individual farmers also admitted that they sold clinically suspect animals to the slaughter house, but not to other farmers. In sum, fear of destruction of personal image and being looked upon as a criminal, and fear of deteriorating social networks were among other reasons for not reporting possible early cases of a NAD.

. Negative Opinion on Control Measures

Farmers held the opinion that the control measures applied by government officials in The Netherlands are long and tedious. After notification, in some cases farms may be isolated in the case animals are sampled to exclude a NAD until test results are announced. This normally takes less than 48 hours. However, in some occasions it may last longer, and the majority of farmers who had not had any experience with reporting NADs seemed all to know these exceptions from hearsay, and they were surprised to hear that in most cases isolation of the farm does not last very long. Dutch farmers do not receive compensation for losses suffered during this period of examination after the notification, although farmers said that the financial "reward" of notifying NADs as quickly as possible is that the financial compensation for further consequences in case there is indeed a NAD, such as eradication, may be higher: healthy animals are fully compensated, sick animals are compensated for 50% and dead animals are not compensated.

Theme 4. Dissatisfaction with Post-Notification Procedures

Several farmers who had had experiences with notifying the authorities about clinically suspect situations were not satisfied with post-notification procedures. After notification, a team of three veterinarians visits the farm. These include the veterinary practitioner of the farmer, a veterinarian of the Animal Health Service, and a State veterinarian. In some instances in the past, governmental veterinarians had made a bad impression by showing lack of branch-specific knowledge when visiting a pig farm. In addition, farmers were dissatisfied if officials had displayed "detached and arrogant attitudes", and spend most of the time writing instead of personally talking to the farmers. Although in many cases farmers praised the professionalism and attitude of the specialist team, in some cases specialists were A c c e p t e d M a n u s c r i p t 11 perceived by farmers as people with limited knowledge on animal disease control. These experiences had de-motivating rather than a stimulating effect on farmers to report a next possible case of a NAD.

Theme 5. Lack of Trust in Government Bodies

Farmers not only know the governmental veterinarians from notification procedures, but also from other contacts, such as commodity inspections and eradication campaigns. Most officials currently are aware of the sensitive nature of the procedure after reporting, and are especially trained to deal with farmers' uncertainties and emotions while performing their duties. Their attitudes during commodity inspections, however, may be totally different, which relates to the different role they are fulfilling during these activities. Farmers do sometimes not discriminate between these roles, and know governmental officials only in their corrective role of commodity inspectors.

In addition, the results indicate that farmers have concerns about earlier animal disease interventions by government bodies. Farmers felt that during past NAD eradication campaigns, they were pushed aside and they were not in control of their business anymore.

Moreover, common to all the farmers was the belief that disease prevention measures launched by government authorities were not consistent and hence not fair. They felt that the government was often giving priority to trade and economic interests. A pig farmer made it clear by saying: "pig farms were the first to close down during an outbreak of FMD in the dairy sector, while dairy farms were allowed to sell their milk." Likewise, dairy farmers complained that pig farmers are allowed to continue long distance international transports, whilst they believe these transports are the major cause of infection. Overall, many farmers currently have a lack of trust in government officials and as a result find it difficult to accept A c c e p t e d M a n u s c r i p t 12 that government could or would work together with farmers to control NADs. This will be a challenge for both the government and farmers.

Theme 6. Uncertainty and Lack of Transparency of Notification Procedures

Farmers lacked insight into reporting procedures and, perhaps more importantly, the process that would follow after a notification. The uncertainty about how long the farm might be closed, already mentioned under Theme 3, is but one example of the uncertainty about possible consequences of a notification. Tension caused by uncertainty starts with the fear of the actual result. Farmers hope for negative test results, but once they have notified the authorities, they often expect that the test results will be positive. Farmers expressed the need for a web-site that you could regularly visit to check the progress of the notification, or a phone number that you could call. Uncertainty also reflects in complaints about the specific steps in the notification procedure, such as making the first telephone call to the veterinary authorities. In all cases the farmers felt that the person answering the calls needed to be an expert with whom they could discuss the seriousness of their report. However, in practice the person answering the phone is an administrator and not an animal disease expert.

Transparency and confidence in the information that is presented are prerequisites for controlling animal disease outbreaks. Currently, farmers were hesitant in using formal channels because they felt that these sources of information were not up to date or reliable. As one pig farmer put it: "you can find more information through informal channels than through formal ones". 3.

Although both farmers and veterinarians were reluctant to report false alarms, this tendency was stronger for farmers than veterinarians. For example, farmers wanted more certainty before reporting to the authorities. When asked: "if you think that a clinical problem on your farm might be caused by CSF, how certain do you want to be before you report to the authorities?" 36% of farmers as compared to 20% of vets (χ 2 statistic, P=0.003) needed more than 80% certainty, while 15% of farmers as compared to 7% of vets (P=0.03) needed more than 90% certainty before they would report. In addition, a total of 40% of farmers and 49% of vets indicated that reporting a suspect situation, when retrospectively this was false alarm, had a (very) negative consequence for the financial situation of the farm. Such situations are expected to affect the relationship between farmer and vet: 5% of farmers and 23% of vets (P < 0.001) indicated that such an event would have a (very) negative influence on the relationship between farmer and vet. More farmers (57%) than vets (13%) indicate that they would report (much) faster a suspicious clinical situation when there is a strong relationship between a farmer and his vet (P< 0.001). Farmers (29%) and vets (31%) indicate that the fuss linked to reporting a suspect situation is often a reason for not reporting such a situation. The threat of paying a possible penalty for negligence is perceived as an important reason to report a suspicious clinical situation by a large minority of farmers (45%) and vets (31%) (P=0.02). Farmers (36%) and vets (20%) feel it is more terrible to report a suspect situation, when retrospectively this was false alarm, than to have missed a possible case of CSF (P=0.003). Guidelines in the legislation and regulations explaining when and how to report a clinical suspect situation

DISCUSSION

A response of 75 pig farmers with respect to the electronic questionnaire was considered low (estimate of response rate around 5%), the response of 334 veterinary practitioners was considered good (estimate of response rate around 40%). The subject of reporting clinically suspect situations possibly caused by NADs to the veterinary authorities is considered a sensitive item within the livestock industry, and this might be an important reason why not many pig farmers have taken the time to respond. Nevertheless, results of the quantitative study underscored the result of the qualitative studies and we are therefore confident that we have captured what is felt by farmers and practitioners in the field.

If a pig farmer is familiar with the clinical signs of CSF, he is in the best position to detect this disease, because he is on the frontline of animal disease identification and responsible for biosecurity measures. Our qualitative findings demonstrated that biosecurity behavior (practices employed on farms to prevent and/or control disease) are influenced by levels of awareness or knowledge about biosecurity, which is in line with previous research [START_REF] Delabbio | Characteristics of the recirculation sector of finfish aquaculture in the United States and Canada[END_REF][START_REF] Delabbio | An assessment of biosecurity utilization in the recirculation sector of finfish aquaculture in the United States and Canada[END_REF][START_REF] Delabbio | Fish disease and biosecurity: Attitudes, beliefs and perceptions of managers/owners of finfish recirculation facilities in the United States and Canada[END_REF][START_REF] Lawson | Reported treatment incidence rate profile of main production diseases of dairy farms in Denmark 1992-1999[END_REF][START_REF] Heffernan | An exploration of the drivers to biosecurity collective action among a sample of UK cattle and sheep farmers[END_REF]. Farmers' knowledge and awareness of the disease and their willingness to report the disease, was called vigilance towards disease by [START_REF] Hopp | Norwegian farmers' vigilance in reporting sheep showing scrapie-associated signs[END_REF]. A recent study by [START_REF] Elbers | Non-specific clinical signs in pigs and use of exclusion diagnostics for classical swine fever: a survey among pig farmers and veterinary practitioners[END_REF] revealed that Dutch pig farmers have a rather limited knowledge on clinical signs of CSF: 33% of pig farmers could mention maximally three clinical signs associated with CSF (all of them late in the disease process) and 7% of pig farmers was not able to mention one single clinical sign of CSF and said they were entirely dependent on the veterinary practitioners' ability to judge a clinically suspect situation. The results of the present study also support the A c c e p t e d M a n u s c r i p t 15 impression that a considerable proportion of pig farmers put the responsibility for judging a clinically suspect situation completely in the hands of their practitioner. It should be noted that in the course of the decision process to report a clinically suspect situation, the pig farmer is still the first person to recognize that something is wrong with his pigs and that he is in need of the judgment of his practitioner. As mentioned during the focus-group meetings, pig farmers in the Netherlands have the authority to treat their animals with antibiotics out of a stockpile (four weeks worth of treatment) obtained from their veterinary practitioner.

Therefore, there is a risk that pigs with early clinical signs of CSF are treated first (with antibiotics) for several days, and when the treatment does not have the desired effect, finally a practitioner is consulted for his judgment.

Linked to this item is the call from pig farmers and veterinary practitioners for internet-based information with up-dated photo-and video-material of clinical signs of NADs in pigs of different ages. Summarizing, there is a need for continuous training of pig farmers and veterinary practitioners with respect to recognizing (early) clinical signs of CSF. This can partly be facilitated by offering web-based information.

The results of our present study indicate that both farmers and veterinary practitioners would report a clinically suspect situation much quicker, if clinical signs of CSF would be more specific. However, the lack of specificity of clinical signs of CSF to detect an outbreak, especially in the early stage of the disease process, is an important barrier for early detection.

To supply veterinary practitioners with an additional tool for identifying CSF-suspect situations as early as possible, a clinical decision-support system (CDSS) for use on-site is being developed [START_REF] Geenen | Development of a probabilistic network for clinical detection of classical swine fever[END_REF][START_REF] Gaag | Validation of decision-support system for early detection of classical swine fever[END_REF]. When a pig farm with clinical problems is visited, the CDSS leads the veterinary practitioner through a list of questions related to the clinical diagnosis of CSF. The answers are entered into a probabilistic network, which returns the probability of the pigs having a CSF infection together with an It is without discussion that if there are disease-specific clinical signs or other not-tomiss signs like progressive and exponential mortality, there should be immediate reporting to the veterinary authorities. However, in practice these black-and-white situations do not often occur. In-between the black-and-white situations that clearly there is -or there is no -clinical indication for a suspicion of a NAD, there is a large grey area where a farmer and veterinary practitioner can not totally rule-out a notifiable disease solely on the basis of a clinical inspection. And this will be more the case if you are looking at the beginning of the disease process, when non-specific signs will gradually show in a few animals. The possibility to submit samples from selected animals of pig farms by veterinary practitioners to a reference A c c e p t e d M a n u s c r i p t 17 laboratory in the case of non-specific clinical signs, to rule-out disease caused by a CSF, without involvement of the authorities and without isolation of the farm, might be a solution to increase the probability of early detection [START_REF] Elbers | Non-specific clinical signs in pigs and use of exclusion diagnostics for classical swine fever: a survey among pig farmers and veterinary practitioners[END_REF]. The alternative is that farmers will wait for several days, use medication for an extended period of time to solve the increasing problem, until one realizes too late that one is hit by a catastrophe because the clinical problems have accumulated exponentially. This tool is in operation in the Netherlands, and was started when there was a direct threat due to CSF-outbreaks in Germany close to the border with the Netherlands in the Spring of 2006. However, after the direct threat had disappeared in 2006, the use of the tool became very limited. This is a bit surprising, because it can be imagined that there would be several situations in which one would like to rule out CSF in a situation in which CSF is not the first on the differential diagnosis list to think of as a possible cause for the clinical problems seen in a pig farm. Farmers and veterinarians indicated that the costs for the individual farmer to use this tool (taking blood samples, veterinarian visits, sending samples by mail to reference laboratory) are still too high. They propose to have these costs being covered by the central emergency fund, because their use of exclusion diagnostics is to protect the sector for a disaster, and there should not be financial barriers for the individual farmer to do so. The veterinary authorities indicate that they have gone far to facilitate the use of this additional tool and are not willing to go any further. The impasse has to be broken to make new progress, communication and building trust between veterinary authorities and farmers should be a first start.

There seems to be a gap between what the authorities expect from pig farmers and veterinary practitioners regarding reporting a clinically suspect situation and what pig farmers and vets really feel as their responsibility. There is a common belief among pig farmers and farmer unions that NADs are the primary responsibility of the government. Changing such an attitude and thinking will take a huge effort in communication preparation and time.

A c c e p t e d M a n u s c r i p t 18 Important requirements to achieve that goal are: a credible communicator, a high level of similarity between the audience (farmers) and the communicator, and finally the message and the communicator must be perceived as trustworthy [START_REF] Heffernan | An exploration of the drivers to biosecurity collective action among a sample of UK cattle and sheep farmers[END_REF]. Since government bodies are not perceived as highly credible and/or trustworthy by livestock farmers [START_REF] Bennet | Control of bovine TB: preferences of farmers who suffered a TB breakdown[END_REF][START_REF] Hood | Psychosocial investigation of individual and community responses to the experience of Ovine Johne's Disease in rural Victoria[END_REF]Poortinga ety al., 2004;[START_REF] Van Haaften | The psychological impact of the Foot and mouth disease crisis on Dutch dairy farmers[END_REF]), there is a specific need for a figurehead arising from the pig industry to take on that challenge.

It appears that the relationship between farmer and practitioner plays a role in the willingness to report a suspect situation, and that there is also an area of tension between farmer and vet if it comes to reporting (retrospectively) a false alarm: "do I (farmer) trust the competence of my vet?" and "Am I (vet) loosing a client (farmer) if my reporting is a false alarm?" Our present study indicates that vets have a much more negative image of the consequences of a false alarm for the relationship between farmer and vet than the farmer has. This would call for recalibration of the relationship between vets and farmers by the vets.

RECOMMENDATIONS

From our present study, the following recommendations are made to facilitate early detection of CSF: a) development of a clinical decision support system for veterinary practitioners and Legislation is often arbitrary, authorities only awake when there is a need for "excluding CSF as cause of problems". Where is the right cut-off ? Do we have to be 5%, 20% or 80% certain that we have the disease before we want to exclude CSF ? Farmer is always blamed, but he is the least competent to take the decision. During the Foot and Mouth Disease outbreak in 2001, there was no FMD in pigs, but pig farms were isolated for a long period, while dairy farms could continue to ship off milk from their farms

  with the authorities, such as the way the government had responded to cases of infectious diseases in the past. A study into Norwegian sheep farmers'

QuestionnaireA

  total of 75 pig farmers and 334 veterinary practitioners responded to the electronic questionnaire. The quantitative study covered topics related to Themes 1 to 6 of the A c c e p t e d M a n u s c r i p t 13 qualitative study. Results of the quantitative study underscored the qualitative results. However, pig farmers and veterinary practitioners differed significantly concerning opinions and attitudes towards reporting clinically suspect situations. Highlights are shown in Table

  CSF, are perceived as: 1) clear by only 34% of farmers and 33% of vets; and well thought-out by 24% of farmers 24% of vets.

  act. The network includes over 40 variables, of which more than half of them can be observed upon clinical investigation, more than 80 relations between these variables and over 2000 conditional probabilities linking events within the network. The variables capture processes in the underlying pathogenesis, risk factors, relevant clinical signs and various alternative explanations for these signs.Farmers lacked insight into reporting procedures and, perhaps more importantly, the process that would follow after a notification. A high level of transparency of the notification process, and what to expect after notification would help to decrease the uncertainty farmers feel. Furthermore, transparency with respect to the notification process will help to build-up trust of the farmer community in the veterinary authorities, and trust in each other might prove to be a key issue in trying to improve early detection of NADs. A clear explanation of the National Guidelines explaining when, what and how to report a clinical suspect situation, and a transparent decision-tree on what to expect in time after the notification up to the final decision to clear the farm of suspicion or to isolate the farm because of a laboratory confirmation of an infection with a NAD, would be helpful. This can be facilitated by the veterinary authorities by means of offering web-based information.

  farmers, in order to get faster diagnosis and detection of CSF; b) possibility to submit blood samples directly to the reference laboratory to exclude CSF in a clinical situation with nonspecific clinical signs, without isolation of the farm and completely free of charge for the individual farmer (group interest paid by communal funds); c) decrease social and economic consequences of reporting CSF, for example by improving the public opinion on first reports; d) better training of governmental employees to deal with emotions and insecurity of farmers in the process after reporting; e) better communication of rules and regulations, where to

  will happen next (decrease insecurity, increase sense of control); f) up-to-date website with information and visual material of the clinical signs of CSF.

Table 1 . Items indicated by focus groups as limitations with respect to reporting a clinically suspect situation on a pig farm possibly caused by Classical Swine Fever (CSF)

 1

	Item	Veterinary authorities	Veterinary Practitioners	Pig farmers
	1. Difficulties with	Unfamiliarity with clinical signs of CSF,	There is a large grey area, e.g. increased mortality	You make your own risk assessment of the
	risk assessment	especially with farmers and veterinary	combined with non-specific clinical signs. There	probability that the clinical problems on your farm
		practitioners with only few pig farms as clients.	are several (non-defined) factors playing a role in	are caused by CSF, and you do not call in a second
		More problematic if last outbreak is some time	the decision process to report a suspicious	opinion in order to prevent negative (financial)
		ago. Farmer perceives risk of having CSF higher	situation. Even with very high mortality, some	consequences
		for neighbor than for his own farm	farmers do not think there is a real problem.
			Related problem: farmers applying medication (no
			supervision and correction by veterinary
			practitioner)
	2. Characteristics of		The higher the probability of infection, the faster	There is a high probability that clinical problems
	disease		one would report. When there are no outbreaks in	are not caused by CSF and therefore it is difficult
			neighboring countries, the probability of infection	to report such a situation. You want to prevent
			is estimated to be low	raising a false alarm. If you are very sure it is CSF,
				you want to report as soon as possible
	3. Negative	Both for farmers as for veterinary practitioners.	Especially the social consequences are high when	Consequences will play a role on the background.
	consequences	They are both vulnerable due to specialization.	a false alarm is raised (strangers on the premises).	It is felt as very negative that others (authorities)
		When there is a real outbreak, limitation of	Farmers are not willing to spend money on	take over the farm during an investigation of a
		movement or stamping-out policy applied in	medical treatment of their animals and super-	suspicion, you are not your own master anymore
		neighborhood may lead to the question of guilt	vision, especially if it concerns the protection of	on your own farm
			the interests of the pig industry as a whole when
			they think it is not in their own personal interest
	4. Guilt, shame and	Farmers having a CSF outbreak are perceived as	Farmers do not want to have strangers with	The farmer that is reporting should be treated as a
	prejudice	being non-hygienic, and have illegal businesses	unfamiliar cars on their premises to be seen by the	hero, he takes responsibility for the pig industry as
			neighbors or have the major of the town visiting	a whole. In practice he is looked upon as a
			them in the evening for a serious talk	criminal
	5. Earlier experience	Association between earlier negative experiences	If you have reported once, the next time will be	A considerable number of negative examples are
	with reporting	and tendency to not to report too quickly	easier, you know what will happen	known in the farmer community. Experiences will
				not be motivating, rather demotivating
	6. Negative image	Farmers and veterinary practitioners have a	Perceived unfairness with respect to the reporting	The specialist team visiting the farm are
	and mistrust of	negative image of the veterinary authorities due	station. There is no possibility to discuss with the	sometimes arrogant, there is too much focus on
	veterinary	to experiences with procedural mistakes and	reporting station to come to a mutual conclusion.	external appearances. Farmers feel intimidated,
	authorities	lack of expertise of veterinary officers	The person answering the phone has no veterinary	they feel not to be one's own master anymore on
			knowledge. The officer visiting the farm is	their own farm, you are not involved (as an equal
			sometimes not competent. State veterinary officers	partner) in the discussions on what disease could
			sometimes have a bad hygienic consciousness	have caused the clinical problems on the farm

Table 2 . Solutions for limitations with respect to reporting a clinically suspect situation on a pig farm possibly caused by Classical Swine Fever (CSF), as indicated by focus groups

 2 Make it possible to have a soft reporting: exclusion diagnostics in a situation with nonspecific clinical signs, without isolation of farm Make soft reporting possible: exclusion diagnose-tics in a situation with non-specific clinical signs, without isolation of farm; consultation with farmer on test result; a more coaching role for the vet, not only telling that it is not CSF, but also telling what is causing the problems; procedures of a reporting should be trans-parent, you should be able to discuss your intention to report with a competent person from the authorities before you really report; Diagnostic testing, which is in the interest of the industry as a whole, should be made available without costs for the individual farmer Communication skills of state veterinary officers have to be improved. The obligations and rights of the farmer during a visit by a specialist-team should not be handed over in the form of a written statement, but should be addressed orally. A complaints service should be made available with respect to handling a report of a suspicion by the authorities6. EducationState veterinary officers have to learn to deal with emotions of a farmer during a visit by specialist team; officers have to have more knowledge about animal husbandry and animal diseases

	Solution	Veterinary authorities	Veterinary practitioners	Pig farmers
	1. Shortening of	Is already accomplished for CSF due to	A procedure for fast exclusion of a possible	Is already reasonably short, about 24 hours is feasible for
	isolation period	use of PCR test	NAD causing a suspect but non-specific	many pig farmers to wait
			situation must be made available
	2. Reducing social		A minimum of publicity, no external signs	No cars near the farm with man wearing "space outfits" and
	consequences			carrying red suitcases.
	3. Improved
	procedures
	4. Better tests	A more accurate and reliable diagnostic	A fast diagnostic result after testing; testing	A fast, accurate and reliable diagnostic test that is 100%
		test, not sure if this should be made	done by practitioners themselves; testing to	accurate during reporting; make a second-opinion available
		available on-site ?	exclude a "possible not situation"; availability	for farmers
			of on-site tests
	5. Better	In the winter period or because of a	More frequent consultation between
	communication	higher awareness due to outbreaks in	authorities and the Royal Netherlands' Society
	between authorities	neighboring countries, have presentations	of Veterinary Science about specific reports of
	and farmers	about notifiable diseases	clinically suspect situations (debriefing) and
			protocols
	7. Costs of testing	Start a campaign to promote use of PCR		Costs are not necessarily the problem, but it is a matter of
	not for individual	test by veterinary practitioners and		principle
	farmer	farmers

ACKNOWLEDGEMENTS

The authors like to thank the veterinary practitioners and the pig farmers that responded to the electronic questionnaire. Furthermore, we like to thank the policy makers from the veterinary authorities, and representatives of veterinary practitioners and pig farmer unions and pig farmers for their opinions and ideas during the focus group sessions. We thank the student assistants Ebru Duman and Meriam Tsang for their help during data collection. This study was funded by the Department of Food Quality and Animal Health, Dutch Ministry of Agriculture, Nature and Food Quality (WOT project # 01-001-004-01).

The funding body had no involvement in the study design, collection, analysis and interpretation of the data, nor in writing of the manuscript and decision to submit the manuscript for publication.

CONFLICT OF INTEREST STATEMENT

Transparent and unequivocal protocols used by veterinary authorities; easy to be found by people who need them (via internet)

A c c e p t e d M a n u s c r i p t There is a need for more information (website: photos, video) on CSF characteristics (clinical signs, transmission routes etc.). 25 60 < 0.001 # n.s. : not significant (P > 0.10)


Aurélie Courcoul
email: courcoul@vet-nantes.fr

Pauline Ezanno
email: ezanno@vet-nantes.fr

Modelling the spread of Bovine Viral Diarrhoea Virus (BVDV) in a managed metapopulation of cattle herds

Keywords: contact network, structured population, epidemic model, migration, pestivirus, livestock

In numerous epidemiological models developed within a metapopulation framework, it is assumed that a single infected individual introduced into a patch infects the whole patch and that the proportion of infected individuals into infected patches is consistent over time and among patches. If this approach is relevant for rapidly-spreading pathogens, it is less appropriate for moderately-spreading pathogens, like the Bovine Viral Diarrhoea Virus (BVDV), characterized by a variability in within-patch prevalence. Our objective is to study the respective influence of neighbouring relationships and animal movements on the spread of BVDV in a managed metapopulation of 100 cattle herds. Infection dynamics is represented by two coupled stochastic compartmental models in discrete time: a withinherd and a between-herd models. Animal movements are mechanistically modelled. They largely influence the BVDV persistence, the prevalence in infected herds and the epidemic size. Neighbouring relationships only influence epidemic size. Whatever the neighbouring relationships, the infection does not persist in the metapopulation without animal movement between herds. The proposed model can be easily adapted for different herd contact structures.

A c c e p t e d M a n u s c r i p t 3

Introduction

Bovine Viral Diarrhoea Virus (BVDV) is a pathogen of major concern in Europe. BVDV infection is widespread among cattle herds [START_REF] Houe | Epidemiological features and economical importance of bovine virus diarrhoea virus (BVDV) infections[END_REF]. Of considerable economic impact [START_REF] Houe | Economic impact of BVDV infection in dairies[END_REF], it is characterized by abortions or congenital deformities, induces milk production losses and growth delays, and increases occurrence of other diseases. As an example, the total annual losses were estimated in Denmark to US$ 20 millions in 1992 [START_REF] Houe | Epidemiological features and economical importance of bovine virus diarrhoea virus (BVDV) infections[END_REF], while the annual incidence of acute infections was estimated to 34%. To limit BVDV spread and its consequences, control strategies are implemented in several regions in Europe [START_REF] Greiser-Wilke | Bovine viral diarrhoea eradication and control programmes in Europe[END_REF][START_REF] Lindberg | The control of bovine viral diarrhoea virus in Europe: today and in the future[END_REF]. However, their efficiency is hard to evaluate or to compare to each other by field observations because no reference situations (without control) is generally available, and because observations on the long term should be performed, which is costly and sometimes unfeasible. Modelling is then a convenient approach to evaluate control strategies of BVDV spread. However, at a regional level, i.e. taking into account interactions between herds, no model of BVDV spread between cattle herds is available yet.

Between herds, BVDV spreads mainly through purchases of animals carrying the virus (equivalent to migration or movement in unmanaged systems) and through neighbouring relationships between infected and BVDV-free herds [START_REF] Lindberg | Principles for eradication of bovine viral diarrhoea virus (BVDV) infections in cattle populations[END_REF]. There are three types of animals carrying the virus. Two types are infectious animals: persistently infected (PI) immunotolerant animals shed the virus in large amounts during their whole life, whereas transiently infected (TI) animals shed the virus in low amounts during a few days [START_REF] Tremblay | Transmission of bovine viral diarrhea virus[END_REF][START_REF] Houe | Epidemiological features and economical importance of bovine virus diarrhoea virus (BVDV) infections[END_REF]. The third type consists of pregnant cows that have been infected and that may carry a PI foetus due to vertical transmission [START_REF] Fray | The effects of bovine viral diarrhoea virus on cattle reproduction in relation to disease control[END_REF].

A c c e p t e d M a n u s c r i p t 4 Such cows are immune and thus hard to detect by diagnostic tests [START_REF] Lindberg | Validation of a test for dams carrying foetuses persistently infected with bovine viral diarrhoea virus based on determination of antibody levels in late pregnancy[END_REF].

The probability to purchase an animal carrying the virus is directly related to the withinherd prevalence in the source herd and to the characteristics of the control strategies implemented (if some are implemented), such as detection tests which can be performed at purchases. If the characteristics of such tests are well known [START_REF] Beaudeau | Evaluation of a blocking ELISA for the detection of bovine viral diarrhoea virus (BVDV) antibodies in serum and milk[END_REF]Baxi et al., 2006), the within-herd prevalence of infected animals is variable over time and among infected herds. Moreover, introducing an infected animal into a herd does not necessarily lead the infection to spread in that herd [START_REF] Ezanno | Effect of herd structure and type of virus introduction on the spread of bovine viral diarrhoea virus (BVDV) within a dairy herd[END_REF]. Hence, infected herds heterogeneously contribute to the BVDV regional spread. In terms of control, it may lead to specific issues, as strategies efficiency might be largely influenced by the within-herd prevalence and its variability. At a regional scale, a population of cattle herds forms a metapopulation, defined as a set of unstable local populations living each on discrete patches of habitat connected by migrants [START_REF] Hanski | Metapopulation dynamics[END_REF]. A metapopulation dynamics is based on a balance between patch extinction and subsequent patch recolonisation by dispersing individuals. This concept is well-known in ecology and genetics to study dynamics of fragmented populations and gene flow in heterogeneous landscapes [START_REF] Grenfell | Meta)population dynamics of infectious diseases[END_REF]. Each metapopulation has its own contact structure. Four types of contacts can be distinguished: migrations or dispersal (an individual moves from one patch to another and does not come back to the initial patch), neighbourhood (individuals in patch i have contacts with individuals in patch k but no individual leaves its own patch), visits (an individual leaves its patch, reaches another one Recently, epidemiological models have been developed in this framework, taking into account infection dynamics of patches in addition to an extinction-recolonisation balance.

In such an approach, each patch is a potential localization for a group of hosts. [START_REF] Hess | Disease in metapopulation models : implications for conservation[END_REF] was a precursor by conceptualising an epidemiological model based on the Levin's model: migrations of infected individuals were a source of infection for healthy populations. With such a model, the persistence of a metapopulation in the presence of a pathogen can be studied [START_REF] Hess | Disease in metapopulation models : implications for conservation[END_REF][START_REF] Gog | Disease in endangered metapopulations : the importance of alternative hosts[END_REF][START_REF] Mccallum | Disease, habitat fragmentation and conservation[END_REF]. Currently, this kind of models is often used to study pathogen spread and persistence in connected populations, particularly for rapidly-spreading animal diseases such as Avian Influenza (Le [START_REF] Menach | Key strategies for reducing spread of avian influenza among commercial poultry holdings: lessons for transmission to humans[END_REF]. In this case, the infection dynamics is represented at the metapopulation level in a deterministic way without care about what exactly happens within each patch. It is thus assumed that introducing one infected individual into a patch is sufficient to lead to infecting this patch and that the prevalence of infection (proportion of infected individuals) in infected patches is consistent over time (the equilibrium is immediately reached) and among infected patches. If this argument seems adequate for rapidly-spreading pathogens, we can wonder what happens for moderately-spreading pathogens. For example, an infected individual with bovine tuberculosis can disappear long before infecting congenerics. In such a case, infectious statuses have to be defined at the individual level and the within-patch infection dynamics has to be modelled [START_REF] Jesse | A fully coupled, mechanistic model for infectious disease dynamics in a metapopulation: movements and epidemic duration[END_REF]. This approach is all the more relevant when heterogeneity in the within-patch infection process is assumed, either over time or among patches [START_REF] Hess | Disease in metapopulation models : implications for conservation[END_REF][START_REF] Park | Extinction times for closed epidemics: the effects of host spatial structure[END_REF][START_REF] Cross | Duelling timescales of host movement and disease recovery determine invasion of disease in structured populations[END_REF] A c c e p t e d M a n u s c r i p t 6 2005). If this kind of models better reflects the variety of situations, it is far more complex.

Nevertheless, a lot of major animal pathogens spread moderately, such as BVDV. When studying the spread of such a pathogen, well-adapted epidemiological models have to be developed.

The objective of this paper is to represent the spread of BVDV in an initially fully susceptible metapopulation of cattle herds. The respective influence of animal movements and neighbouring relationships on the regional BVDV spread and persistence is evaluated using a modelling approach. Such an approach allows to better understand the dynamics of this moderately-spreading, to test a wide range of situations and to evaluate the influence of unobservable parameters.

The modelling approach

We consider a metapopulation of n small animal populations, each inhabiting a distinct habitat patch. Patches are assumed to be connected by migrations of animals and by neighbouring contacts between patches. Space is assumed to be homogeneous and populations to all have the same number of neighbours.

Here we assumed that the within-patch pathogen spread varies among infected patches.

Actually, a newly infected animal can quickly disappear (mortality, recovery, migration to another patch) or persist for a long time in a given patch. To take into account this heterogeneity in the within-patch force of infection and the potential effect it may have on the global infection dynamics at the metapopulation level, a within-patch model is coupled with a between-patch model (Fig. 1).

A c c e p t e d M a n u s c r i p t 7 Within a patch, a population can be structured into different groups with different contact structure. For example, young individuals of an age-structured population can be assumed neither to migrate nor to have neighbouring contacts. Such a population structure influences the within-population spread of pathogens [START_REF] Keeling | Modeling infectious diseases in humans and animals[END_REF] and should be accounted for to model the within-patch infection dynamics.

As populations are small, the model is stochastic. The resulting model is a patch-based model in discrete-time, the state of each patch at time t being defined by the number of animals per health status and per group at this time in the patch. For example, for a SIR (susceptible / infected / recovered) within-patch model in a population structured into groups, the local probability of infection in patch (or population) X in group l at time t depends on the within-group infection process and on the between-group probability of transmission. Assuming a frequency-dependent infection function, this can be written as:

( ) ( ) ( ) ( ) ( ) ( ) ⎟ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + - - = ∑ ∈ ≠ x g l g X l X g X g b X l X l w X l t N t N t I t N t I ∆ t p , exp 1 β β , ( 1
)
with β w the within-group transmission rate of the infection between an infectious and a susceptible animal, β b the between-group transmission rate, ∆ the length of the time step, g a group other than group l also belonging to population X, ( )

t I X g
the number of infectious animals in group g in population X at time t, ( ) t N X g the total number of animals in group g in population X at time t. The between-group transmission function takes into account both the influenced and the influencing populations. This is convenient in order to model indirect transmission, by contaminated material for example. In such a case, only a fraction of population encounters the contaminated material. Hence, the probability of infection per [START_REF] Ögren | Vaccination strategies for epidemics in highly mobile populations[END_REF], as well as for modelling the spread of a pathogen in a cattle herd [START_REF] Ezanno | Sensitivity analysis to identify keyparameters in modelling the spread of bovine viral diarrhoea virus in a dairy herd[END_REF], which is typically structured into groups (feedlots, reproductive groups, etc.).

Between patches, the pathogen can locally spread by neighbouring contacts. This could be the case when patches of habitat are contiguous or when a common resource (such as a temporary pool) is used by several populations. The probability of infection of patch X by neighbouring relationships is assumed to depend on the prevalence of the infection in neighbouring populations and to be frequency-dependent: ) and the probability for a susceptible animal in herd l to be in contact with it (

( ) ( ) ( ) ( ) ⎟ ⎟ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎜ ⎜ ⎝ ⎛ ∆ - - = ∑ ∑ ∈ ∈ X X Y Y X Y Y n X n t N t N t I t P ζ ζ β exp 1 , (2)
l t n N k 1 β ).
Moreover, a global spread of the pathogen between patches is due to migrations of individuals. Here, explicit movements are modelled using a complete random network, i.e. animals from a patch can go to any other patches. Actually, in a small patch infected with a moderately-transmitted pathogen, the prevalence of infection can be low. The movement of a single individual can thus influence the infection dynamics in both source and destination patches, especially if the moving individual is the only infected one in its source patch. We assume that there is no infection during the migration process, i.e. migrants form a pool of individuals without any contact with other individuals or with each other (Fig. 1).

Application to BVDV spread between cattle herds

Main characteristics of BVDV infection

BVDV is a pestivirus which does not survive well in the environment [START_REF] Tremblay | Transmission of bovine viral diarrhea virus[END_REF].

Direct contacts between animals are then required for transmission of the infection. Thus, animal purchases and contacts at pasture between animals of neighbouring herds (contact above the fence or pasture on common land areas) are the most important ways of introducing BVDV into a herd [START_REF] Lindberg | Principles for eradication of bovine viral diarrhoea virus (BVDV) infections in cattle populations[END_REF]. Potential introductions by people or equipment, inoculations during vaccination, artificial reproduction (sperm or embryos) or insects are assumed to have a negligible impact [START_REF] Tremblay | Transmission of bovine viral diarrhea virus[END_REF].

A c c e p t e d M a n u s c r i p t 10

The virus can also be vertically transmitted. Foetuses infected in utero by their mother infected between 40 and 125 days of pregnancy become persistently infected (PI) animals.

They shed the virus in large amounts during their whole life [START_REF] Tremblay | Transmission of bovine viral diarrhea virus[END_REF]. Moreover, PI dams give birth to PI calves. On the contrary, transiently infected (TI) animals have been horizontally infected. They shed the virus in low amounts during a few days [START_REF] Houe | Epidemiological features and economical importance of bovine virus diarrhoea virus (BVDV) infections[END_REF].

Afterwards, they are considered resistant for their whole life [START_REF] Brownlie | Pathogenesis and epidemiology of bovine virus diarrhoea virus infection of cattle[END_REF]. Their epidemiological importance is generally considered to be weak except that transient infection occurring during the first third of gestation can lead to the future birth of a PI calf, the dam certainly becoming resistant.

Within-herd model

The within-herd model is described in details in [START_REF] Ezanno | Sensitivity analysis to identify keyparameters in modelling the spread of bovine viral diarrhoea virus in a dairy herd[END_REF]. It is a stochastic compartmental model in discrete-time that represents the BVDV spread in a dairy herd.

The herd is structured in 5 groups of animals (calves, young heifers, heifers, lactating cows and dry cows). Animals change groups function of their age and their physiological state.

The two kinds of infectious individuals are represented : transiently infected (T) and persistently infected (P). A diagram of this model as well as the related equations are published elsewhere [START_REF] Ezanno | Sensitivity analysis to identify keyparameters in modelling the spread of bovine viral diarrhoea virus in a dairy herd[END_REF].

Direct horizontal transmission occurs between PI or TI animals and susceptible ones of the same group and between PI and susceptible animals of different groups. By extending equation (1) to model the local probability of BVDV infection in population X for group l, we have:

A c c e p t e d M a n u s c r i p t 11 ⎟ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + + ∆ - - = ∑ ∈ ≠ x g l g X l X g X g P b X l X l T w X l X l P w X l t N t N t P t N t T t N t P t P , ) ( ) ( ) ( ) ( ) ( ) ( ) ( exp 1 ) ( β β β
considering the same notations as in equation ( 1) and with

Between-herd model

We consider a metapopulation of 100 dairy herds assimilated to patches with two ways of contacts between those patches: neighbouring contacts and animal purchases (comparable to between-patch migrations).

As observed in the field [START_REF] Ezanno | Between-herd movements of cattle in Bretagne and risk of introducing infected animals[END_REF], each dairy herd buy a given number of heifers per year, the source herd being chosen randomly for each purchase. Animal purchases lead to a random network structure.

The neighbouring contact structure is stated initially and is kept identical over time and for all realizations of a given scenario. It is a symmetric structure (if A is B neighbour, then B is A neighbour). For each herd, neighbours are selected among all herds, without accounting for between-herd distances. As only a small region with a few herds is modelled, distance is not highly influential. Actually, pastures of a given herd can be anywhere in the modelled area and thus can be in the neighbourhood of any other herd. As a result, due to such a fragmented farming landscape, pastures of a given herd can be in the Neighbouring contacts are assumed to occur from mid-March to mid-November. Only dry cows, heifers and young heifers over 6 months are grazing and thus may have neighbouring contacts. We assume that only PI animals shed the virus in sufficient amount to give rise to infection of their neighbours at pasture. By extending equation ( 2) to model the probability of infection of patch X by neighbouring relationships, we have: the same for all herds. They are chosen to represent typical Holstein dairy herds (see [START_REF] Ezanno | Sensitivity analysis to identify keyparameters in modelling the spread of bovine viral diarrhoea virus in a dairy herd[END_REF] for more details). For sake of simplicity, we assume that all herds introduce the same number of heifers per year and have the same number of neighbours.

( ) ( ) ( ) ( ) ⎟ ⎟ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎜ ⎜ ⎝ ⎛ ∆ - - = ∑ ∑ ∈ ∈ X X Y Y X Y Y o n X n t O t O t P t P
Because no information is available to rationalize the value taken for the rate of BVDV transmission at pasture, it is taken to be equal to the between-group transmission rate within a herd because both events are closely related. As a result, the within-group within-herd transmission rates are equal to 0.50 and 0.03 per day for the PI and TI animals, respectively. The between-group and between-herd transmission rates are equal to 0.10 per day (transmission only du to PI animals).

BVDV is initially introduced in a fully susceptible metapopulation by the purchase of a PI heifer in one herd. No re-introduction is further allowed.

Scenarii

Three neighbouring contact structures (0, 5 or 10 neighbours per herd) and five purchase intensities (0, 2, 5, 7 or 10 heifers yearly purchased per herd) are modelled to study their relative influence on the regional spread of BVDV, leading to a total of fifteen scenarii. The reference scenario is '5 neighbours per herd and 2 purchases per year per herd'.

For each scenario, 150 realizations are run, which is enough to stabilize model outputs (results not shown). We simulate the evolution of the system over a 15-year period. It has to be noted that studying such a managed system on a longer term will not be appropriate because herd structure and management obviously change on such a time scale. Second, we analyse the probability of persistence of the metapopulation infection (equal for each scenario at any time interval to the proportion of realizations for which the virus is still present in the metapopulation), the distribution of the metapopulation infection duration and the related probability of pathogen endemicity (assumed when the pathogen persists more than 10 years in the metapopulation), the mean number of infected herds over time in a metapopulation still infected, and the distribution over all realizations of the epidemic size in infected herds (cumulated number of herds that have been infected for at least one time interval).

Results

As the model is stochastic, the infection dynamics is variable both over time and among realizations for a given scenario. The infection can disappear in a few years or persist over a long time with a high number of persistently infected animals in the metapopulation (as shown for example for the reference scenario in Fig. 2). In the reference scenario, the prevalence of PI animals in the metapopulation reaches a pseudo-equilibrium equal to 8%.

The infection does not persist in the metapopulation when no animals are purchased (Fig. 3), whereas the probability of persistence is high and maintains over time when animals are purchased. The number of animals that are purchased does not influence the probability of Animal purchases also highly influence the duration of the metapopulation infection (Fig. 4a). This duration is shorter without purchases (on average 4.6 years) than with purchases (on average 10.8 years), whatever the number of neighbours. The median duration increases with the number of purchased animals until reaching a maximum duration (equal to the simulated period) after which increasing further the number of purchased animals slightly decreases the median duration (here obtained for 10 purchases/year/herd). On the contrary, the neighbouring contacts do almost not influence this output. The related probability of pathogen endemicity is lower than 6% when no animal is purchased, whereas it is larger than 50% when animals are purchased (Fig. 4b), whatever the number of animals purchased and the number of neighbours per herd.

Without purchase, the mean number of infected herds in an infected metapopulation decreases to reach extinction on average in 10 years, without any influence of neighbouring contacts (Fig. 5). On the contrary when animals are purchased the average number of infected herds in an infected metapopulation reaches an pseudo-equilibrium, the herd level prevalence being almost equal to 5%. The presence of neighbours slightly increases this prevalence (Fig. 5).

Both increasing neighbouring contacts and increasing purchases increase the herd epidemic size (Fig. 6). The median epidemic size only slightly increases with the number of neighbours per herd. However, the number of realizations showing an extreme epidemic size (queue of the distribution) increases with the number of neighbours per herd (Fig. 6).

Moreover, until 7 animals are purchased per herd per year, the more animals are purchased, A c c e p t e d M a n u s c r i p t 16 the larger is the epidemic size. As the median infection duration, the epidemic size slightly decreases for a large number of purchases (10 purchases/year/herd).

Discussion

The modelling approach

A stochastic compartmental model in discrete time is proposed, which couples within-and between-patch infection dynamics in a metapopulation framework. Because the model is stochastic, different evolutions of the system over time for a given scenario are possible: the infection can early disappear or persist with a variable number of infected patches and infected individuals. This type of dynamics is important for pathogens which spread moderately in a heterogeneous way, especially in small-size patches in which stochastic events have a major impact [START_REF] Jesse | A fully coupled, mechanistic model for infectious disease dynamics in a metapopulation: movements and epidemic duration[END_REF]. However, for such realistic a model, the running time becomes important. Concerning BVDV, it has been shown that the herd structure largely influences the pathogen spread within a herd [START_REF] Ezanno | Effect of herd structure and type of virus introduction on the spread of bovine viral diarrhoea virus (BVDV) within a dairy herd[END_REF].

Therefore, the within-herd infection and population dynamics should be explicitly modelled. Furthermore, the between-patch contact structure is related to both neighbouring relationships and mechanistically modelled individual migrations. In order to clearly evaluate the effect of both these types of between-patch contact on pathogen spread and persistence, independently of other possible causes of persistence, no reintroduction of the virus in the metapopulation is allowed. Accounting for an external source of infection would have largely contributed to the pathogen persistence (e.g. [START_REF] Fromont | Disease propagation in connected host populations with density-dependent dynamics : the case of the feline leukemia virus[END_REF][START_REF] Gog | Disease in endangered metapopulations : the importance of alternative hosts[END_REF], and would have been appropriate if a pathogen surviving in the environment had been modelled. Actually, for such indirectly transmitted pathogens, a delay may appear , with β ij the transmission rate between subpopulation i and j, I j the number of infected individuals in subpopulation j and N j the total number of individuals in subpopulation j. If such a function has the convenient property of simplifying in case of just one subpopulation to the function of the unstructured model, it seems inappropriate when subpopulations greatly differ in size (Ogren and Martin, 2002). In case of travelling between subpopulations, the fraction of travellers from the source population has to be taken into account as well as the proportion of infected individuals in the destination population. Ogren and Martin (2002) propose the following function:

∑ = = = n j j j j i ij i N I N 1 1 β λ , with ij ij M β β =
, M ij being the number of travellers between subpopulations i and j. Such a function takes into account the sizes of both the influenced and the influencing populations. In case of managed herds, herd sizes vary among herds.

Moreover, not all the animals from a herd are influenced by infected animals from neighbouring herds at each time period. Hence, such a function appears to be appropriate to model the between-herd transmission when direct contacts between animals are involved.

A c c e p t e d M a n u s c r i p t 18

In the absence of knowledge on the intensity of contacts over the fence at pasture, the between-group transmission rate is assumed to be the same within-herd and between-herd.

However, within a herd, between-group transmission involves indirect contacts like contaminated equipments, whereas between herds, direct contacts are concerned during which transmission is frequent. This can lead to underestimate the probability of infection at pasture.

The neighbouring contact structure is a symmetric structure but not a transitive one (even if A is the neighbour of B and C, B and C have not a higher probability of being neighbours).

It is kept identical over time and for all realizations of a given scenario. As only a small region with a few herds is modelled, distance can be assumed not to be highly influential.

In addition, in farms, pastures are often scattered in the countryside and sometimes located far from the farm. Hence, even if A has a pasture contiguous to one of farm B and another contiguous to one of farm C, B and C are likely not to have neighbouring pastures. As a result, we do not represent space as continuous or as a grid made of contiguous cells of habitat, but place our modelling work in the metapopulation framework. In such a framework, moreover, no edge effect are encountered as space is not explicitly modelled. In contrast, in ecology, patches can be spatially aggregated in clusters [START_REF] Matthews | Neighbourhood control policies and the spread of infectious diseases[END_REF][START_REF] Favier | Influence of spatial heterogeneity on an emerging infectious disease: the case of dengue epidemics[END_REF], and distance may play a major role in pathogens spread [START_REF] Keeling | Dynamics of the 2001 UK Foot and Mouth Epidemic: Stochastic Dispersal in a Heterogeneous Landscape[END_REF], as for example in vector-borne diseases [START_REF] Tran | On the dynamics of dengue epidemics from large-scale information[END_REF]. Therefore, for areas of larger sizes or for a biological system explicitly related to space, the proposed model should be extended to represent geographical clusters, i.e. sets of patches in which neighbourhood contacts are more frequent, which could be more spatially realistic. Such an In our approach, we consider a homogeneous exposure to infection at pasture due to neighbouring relationships. We assume that all individuals from a given patch are consistently exposed to infectious individuals of all neighbours. This assumption is appropriate if contacts between individuals from neighbouring patches occur because individuals all use a common resource such as water, food or cover, because of random local movements of individuals among neighbouring patches, or because of frequent changes in neighbours. Here, we are in this latter case, pasture turn-over leading to homogenizing infection exposure at pasture. However, in some other cases, the contact related to neighbouring relationships may be heterogeneous, some specific groups of individuals from a patch meeting some specific groups from other patches. This is particularly true concerning wild structured populations, for example because of sexual segregation such as in deer [START_REF] Bonenfant | Multiple causes of sexual segregation in European red deer: enlightenments from varying breeding phenology at high and low latitude[END_REF] which leads to a heterogeneous contact structure between males and females.

Like in the island model of [START_REF] Hess | Disease in metapopulation models : implications for conservation[END_REF], animal movements are random in the proposed model, such that each patch can be reach from any other patch. This assumption is realistic here as more than 90% of the pairs between dairy herds which do not have any fattening unit on a given year are not renewed the next year [START_REF] Ezanno | Between-herd movements of cattle in Bretagne and risk of introducing infected animals[END_REF]. Hence, there is no preference in animal exchanges. However, in some managed populations, herds can sell (buy) animals preferentially to (in) given herds. Some authors thus consider that individuals migrate between patches function of the localization [START_REF] Matthews | Neighbourhood control policies and the spread of infectious diseases[END_REF] or the carrying A c c e p t e d M a n u s c r i p t 20 capacity [START_REF] Fulford | The metapopulation dynamics of an infectious disease : tuberculosis in possums[END_REF] of patches. Such a random pattern in animal movements can be adapted to more specific networks by specifying the purchase / movement matrix: for each herd, a set of herds is defined in which animals can be purchased. This would improve the model realism when animal movements are not random [START_REF] Keeling | Networks and epidemic models[END_REF].

BVDV spread and persistence

The model has been applied to modelling the spread of BVDV in a bovine metapopulation in order to study the persistence of such a directly transmitted and moderately-spreading pathogen. When only neighbouring relationships are involved in the between-herd contact structure, the infection does not persist within a metapopulation of 100 dairy herds without fattening units. Purchases are required. However, even with few animal movements and assuming no further reintroduction of the pathogen after the initial contamination of the metapopulation, the infection highly persists over time, which is coherent with field observations. In Brittany (France) for example, BVDV infection is assumed to be endemic and even dairy herds introducing almost no animal may be infected (Joly, pers. comm. 2007).

A non linear relationships between the median epidemic duration and animal movements has been recently highlighted using a stochastic SIR model in metapopulation [START_REF] Jesse | A fully coupled, mechanistic model for infectious disease dynamics in a metapopulation: movements and epidemic duration[END_REF]. The median epidemic duration first increases with an increasing movement rate, then decreases for intermediate values until the movement rate becomes large enough to permit an endemic behaviour of the infection. Here, we observe the same tendency using a far more complex model applied to a realistic biological system and pathogen, even for only very few between-herd animal movements. In the model, the prevalence of infected herds within the metapopulation is lower than 6%. This is consistent with field observations made in Brittany (France): before control strategies have been implemented, around 10% of the dairy herds (with and without fattening units) had at least one persistent infected animal (Joly, pers. comm. 2007).

Moreover, in most infected herds, the prevalence of infected individuals is low, which could have consequences on virus screening. A large number of animals should be tested before ensuring an area to be BVDV-free. In the model, if the average herd level and individual level prevalences are low, they highly vary among realizations of a given scenario. This is consistent with literature: from 37 to 100% of the herds of a country could contain seropositive animals [START_REF] Houe | Epidemiological features and economical importance of bovine virus diarrhoea virus (BVDV) infections[END_REF]. Hence, our model catches the heterogeneity in the BVDV dynamics in a realistic way.

Application to BVDV control

We study how neighbouring contacts and animal movements may influence the dynamics of a moderately spreading pathogen in a metapopulation, applied to BVDV spread among dairy herds. Our model shows that animal movements highly contribute to the persistence of the infection and to the herd level prevalence. Without any purchase, the average number of infected herds in an infected metapopulation decreases and extinction may be reached in less than 10 years, whereas only few animal movements are needed to lead the infection to persist. However, animals of any health states are allowed to be exchanged between herds, whereas control strategies may be implemented in the field to reduce the risk of introducing infected animals within uninfected herds. In fact, preventing re-introduction of the infection in free herds because of movements of animals is one of the central elements of control A c c e p t e d M a n u s c r i p t 22 strategies [START_REF] Lindberg | The control of bovine viral diarrhoea virus in Europe: today and in the future[END_REF]. This can be achieved by defining herd status towards BVDV and by introducing trade regulations for such certified herds: the latter have to buy animals only in herds of equivalent or better status. Testing individuals before purchase is another way to prevent from introducing PI animals. It seems therefore valuable to further evaluate such strategies using a modelling approach, especially in the case of endemic infection. In particular, whereas neighbouring relationships have been shown here to only slightly influence BVDV epidemic size, we can wonder if their influence will change when certification of herds or tests on purchased animals will be systematically implemented.

Acknowledgments

This work was carried out with the financial support of the « ANR-Agence Nationale de la Recherche -The French National Research Agency » under the « Programme Agriculture et Développement Durable », project « ANR-05-PADD-014, ACDUQ ». The authors would like to thank F. Beaudeau and H. Seegers for valuable comments on this paper.

Conflict of interest statement

None.

  back to its initial patch) and indirect contacts (individuals do not meet, the pathogen being transmitted by an inert or animal vector or by the air).

  the size of the influenced population. Such an infection function has been proposed to model epidemics in highly mobile populations

  with β n the transmission rate between an infectious animal and a susceptible one belonging to neighbouring patches, X ζ the set of neighbours of patch X, I Y (t) the number of infectious animals in patch Y at time t, and N Y (t) the total number of animals in patch Y at time t. As for the between-group within-population transmission, the proposed function takes into account the sizes of both the influenced and the influencing populations. Here again, only a fraction of the animals from a herd are influenced by infected animals from neighbouring herds at each time period. Contacts between neighbouring herds may involve for example uncontrolled movements of susceptible animals (escape) or contacts above the fence at pasture influenced by the available space. Hence, the function has to combine the

β

  the within-group transmission rate for PI animals, T w β the within-group transmission rate for TI animals, P b β the between-group transmission rate only due to PI animals, animals of status Z in group g in population X at time t, and of animals in group g in population X at time t.

  A c c e p t e d M a n u s c r i p t 12 neighbourhood of pastures of two herds which have no contact with each other (A is the neighbour of B and C, without B and C being neighbours). Hence, the chosen structure is not transitive. However, the model can be easily adapted to other contact structures, including a distance-dependent or transitive network structure, by initially specifying the matrix of neighbouring contacts.

  notation as in equation 2 and with β n the transmission rate between a PI animal and a susceptible one belonging to neighbouring patches, animals at pasture in patch Y at time t, and O Y (t) the total number of animals at pasture in patch Y at time t.3.4. Parameters and initial conditionsInitially, every herds are of the same size, being composed each of 85 animals (45 cows, 20 heifers and the associated young stock). The size varies over time but each herd cannot have more than 60 cows and/or 25 heifers, numbers above which cows or heifers randomly A c c e p t e d M a n u s c r i p t 13 leave the herd. Demographic parameters (birth, mortality, slaughter, achievement in AI) are

  observe the influence of stochastic events on the modelled system by comparing two almost extreme realizations of the reference scenario and the global average of the number PI animals over time in the metapopulation.

  is almost the same for 2 to 10 animals yearly purchased per herd (results not shown).

  and subsequent animal infection, which can occur even if no infected animal is present. However, in case of directly transmitted pathogens, it would have led to quite an artificial persistence of the pathogen. For the model to be extended to the case of indirectly transmitted pathogens, the transmission function should be modified and a compartment stating the level of environment contamination over time should be added (e.g. in livestock pathogens within a managed herd,[START_REF] Belloc | Modelling Salmonella spread within a farrow-to-finish pig herd[END_REF].In addition, the way subpopulations influence each other is generally modelled with a frequency-dependent function of the form

  space can be easily incorporated in our model by modifying the neighbouring matrix.

  Baxi M.,McRae D., Baxi S., Greiser-Wilke I., Vilcek S., Amoako K., Deregt D. 2006. A one-step multiplex real-time RT-PCR for detection and typing of bovine viral diarrhea viruses. Vet.Microbiol. 116,[37][38][39][40][41][42][43][44]

Figure 1 :

 1 Figure legends

Figure 2 :

 2 Figure 2: Influence of stochastic events on the number of persistently-infected (PI) animals

Figure 3 :

 3 Figure 3: Probability of infection persistence in the metapopulation according to the

Figure 4 :

 4 Figure 4: Infection duration of the metapopulation: (a) distribution of the duration (in

Figure 5 :

 5 Figure 5: Average number of infected herds in a metapopulation still infected according to

Figure 6 :

 6 Figure 6: Distribution of the herd epidemic size (cumulative number of infected herds over

  Figure 1