Using mortality data for early detection of Classical Swine Fever in The Netherlands

Preventive Veterinary Medicine 99 (2011) 38–47

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Using mortality data for early detection of Classical Swine Fever in The Netherlands J.A. Backer a,∗ , H. Brouwer b , G. van Schaik b , H.J.W. van Roermund a a b

Department of Epidemiology, Crisis & Diagnostics, Central Veterinary Institute of Wageningen UR, Lelystad, The Netherlands Department of Diagnostics, Research & Epidemiology, GD Animal Health Service Ltd., Deventer, The Netherlands

a r t i c l e

i n f o

Article history: Received 5 November 2009 Received in revised form 14 October 2010 Accepted 16 October 2010 Keywords: Classical Swine Fever Early detection system Surveillance Stochastic simulation model

a b s t r a c t Early detection of the introduction of an infectious livestock disease is of great importance to limit the potential extent of an outbreak. Classical Swine Fever (CSF) often causes non-specific clinical signs, which can take considerable time to be detected. Currently, the disease can be detected by three main routes, that are all triggered by clinical signs. To improve the early detection of CSF an additional program, based on mortality data, aims to routinely perform PCR tests on ear notch samples from herds with a high(er) mortality. To assess the effectiveness of this new early detection system, we have developed a stochastic model that describes the virus transmission within a pig herd, the development of disease in infected animals and the different early detection programs. As virus transmission and mortality (by CSF and by other causes) are different for finishing pigs, piglets and sows, a distinction is made between these pig categories. The model is applied to an extensive database that contains all unique pig herds in The Netherlands, their herd sizes and their mortality reports over the CSF-free period 2001–2005. Results from the simulations suggest that the new early detection system is not effective in piglet sections, due to the high mortality from non-CSF causes, nor in sow sections, due to the low CSF-mortality. In finishing herds, the model predicts that the new early detection system can improve the detection time by two days, from 38 (27–53) days to 36 (24–51) days after virus introduction, when assuming a moderately virulent virus strain causing a 50% CSF mortality. For this result up to 5 ear notch samples per herd from 8 (0–13) finishing herds must be tested every workday. Detecting a source herd two days earlier could considerably reduce the number of initially infected herds. However, considering the variation in outcome and the uncertainty in some model assumptions, this two-day gain in detection time is too small to demonstrate a substantial effect of the new early detection system based on mortality data. But when the alertness of herd-owners and veterinarians diminishes during long CSF-free periods, the new early detection system might gain in effectiveness. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Currently, The Netherlands is free of Classical Swine Fever (CSF) but the Dutch pig population continues to be

∗ Corresponding author at: Department of Epidemiology, Crisis & Diagnostics, Central Veterinary Institute of Wageningen UR, PO Box 65, 8200 AB Lelystad, The Netherlands. Fax: +31 320238961. E-mail address: [email protected] (J.A. Backer). 0167-5877/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.prevetmed.2010.10.008

at risk of virus introduction from other countries. Such an introduction can cause a major outbreak in non-vaccinated pigs, which harms animal welfare, decreases economic revenues and unsettles society. The potential extent of an epidemic largely depends on the number of farms that have already been infected at the moment the disease is first diagnosed. A rapid detection is therefore of great importance to limit the size of an outbreak. Such a first detection is usually triggered by the clinical signs of diseased animals. However, CSF virus (CSFV) often causes non-specific signs

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Table 1 Parameters used in the Classical Swine Fever transmission model. The transmission rate is defined as the reproduction number divided by the infectious period. Parameter Within-pen transmission Latent period (days) Infectious period (days) Transmission rate (day−1 ) Reproduction number Mortality Between-pen transmission Number of animals per pen Between-pen reduction factor ε Between-pen reproduction number a

Finishing pigs

Piglets

Sows

4 13.5 (6.5–23)a 1.2 15.5 50%

4 13.5 (6.5–23)a 7.4 100 100%

4 13.5 (6.5–23)a 0.21 2.8 20%

10 0.018 2.8

10 2.8 × 10−3 2.8

1 1 2.8

Between brackets the 2.5%–97.5% interval of the infectious period distribution.

that can take considerable time to be detected, especially at the start of an outbreak (Floegel-Niesmann et al., 2003). As CSF can also cause a high mortality among infected pigs, an additional early detection system is proposed that uses the information from the Dutch rendering plant (Rendac) to signal abnormal increases in pig mortality. The advantages of such a system are firstly that the mortality data are readily available because all farmers are obliged to report their cadavers to a single rendering plant to have them removed. Secondly, the data form an objective information source, not dependent on the alertness of farmers and practitioners. The disadvantage however are the high costs of implementation, when a considerable number of alerts are generated by the system. Thus, before applying it in practice, the effectiveness of the new early detection system should be assessed. Here we develop a model to compare early detection by the current surveillance programs with and without the new system based on mortality data. The model combines virus transmission from animal to animal (Backer et al., 2009), the development of clinical disease (Klinkenberg et al., 2005) and the performance of the detection systems. The (sub)model of the current surveillance programs (Klinkenberg et al., 2005) is updated and extended with the new early detection system based on mortality data. A distinction is made between finishing herds, piglet sections and sow sections, because virus transmission, mortality and the criteria for the new detection system differ in these categories. The full model is applied to the actual mortality data recorded from 2001 to 2005 by the Dutch rendering plant. As this was a CSF-free period in The Netherlands, the data represent the ‘background’ mortality that is not related to CSF. By simulating CSF outbreaks on random farms at random times, we estimate when the current surveillance systems detect the infection and when the CSF-induced mortality will be noticed by the new detection system. In this way, we assess what the expected time gain of the new detection system is compared to the current systems. 2. Materials and methods

(Backer et al., 2009). In brief, the transmission of the virus between individual pigs in a pen is modeled with an SEIR model that assumes the pigs to be either in a susceptible (S), latently infected (E), infectious (I) or removed (R) state (Keeling and Rohani, 2008). The latent periods are assumed to be fixed at 4 days (Laevens et al., 1998; Dewulf et al., 2004) and the infectious periods to be gamma distributed with a mean of 13.5 days (Dewulf et al., 2001b). Finishing pigs, piglets and sows only differ from each other in the reproduction number of CSFV transmission. This number – expressing the number of secondary infections one infectious animal will cause in an infinite, fully susceptible population – is high for piglets (Laevens et al., 1998; Klinkenberg et al., 2002), intermediate for finishing pigs (Laevens et al., 1999; Klinkenberg et al., 2002), and relatively low for sows (Dewulf et al., 2001a; Stegeman et al., 1999). Between pens the virus transmission is described by a household model (Becker and Dietz, 1995), in which pens are assumed to mix randomly. The pen barrier reduces the virus transmission from one pen to the other, effectively lowering the reproduction number between animals with a factor ε times the number of animals per pen (Table 1). Analyses of the CSF epidemic in The Netherlands in 1997/1998 show that this effective within-herd reproduction number is 2.8 in finishing herds (Klinkenberg et al., 2003) and 2.9 in sow herds (Stegeman et al., 1999). Here we will use a value of 2.8 for all pig herds, and the average number of animals per pen that was common at that time. All parameters of the within-herd transmission model are summarized in Table 1. As stochastic effects are important in small (pen) populations, the within-herd model is formulated stochastically (Sellke, 1983). 2.2. CSF disease model The disease model describes which of the infected pigs will show non-specific, specific, severe and/or lethal clinical signs. The distinction between these types of signs is important as each of the early detection systems is triggered by a different type.

2.1. CSF transmission model To model the transmission of CSFV in a herd, the withinherd module of a more extensive simulation model is used

2.2.1. Clinical signs After infection with CSFV the course of the disease differs for each animal and virus strain. Three different levels

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J.A. Backer et al. / Preventive Veterinary Medicine 99 (2011) 38–47 a herd owner

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1 0.025

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Fig. 1. Schematic overview of Classical Swine Fever detection by (a) herd-owner and veterinarian, (b) gross pathology, (c) exclusion diagnostics and (d) mortality data; wdays denote working days (i.e. Monday to Friday); probabilities are denoted in bold.

of clinical signs are distinguished: • non-specific clinical signs: fever, diarrhea, dullness and loss of appetite; • specific clinical signs: skin haemorrhages, cyanotic ears, conjunctivitis and lameness; • severe clinical signs: a body temperature of at least 41 ◦ C, combined with at least four (non-)specific clinical signs for at least 3 days. Based on these definitions the probabilities of showing one or more of these disease levels have been estimated by Klinkenberg et al. (2005). Of the infected pigs 12% show no clinical signs, 44% only non-specific clinical signs, 7% non-specific and specific clinical signs, 7% non-specific and severe clinical signs and 30% non-specific, specific and severe clinical signs. The incubation period for non-specific disease is 7 days, for specific disease 12 days and for severe disease 13 days. For each infected animal in the simulations, the above probabilities are used to decide whether it will be non-specifically, specifically and/or severely diseased. 2.2.2. Mortality CSF-induced mortality can differ greatly, depending on the virulence of the virus strain and the pig age and breed. For the moderately virulent strain ‘souche Lorraine’ the mortality can be very high for piglets (Laevens et al., 1999; Dewulf et al., 2001b) and low for sows (Dewulf et al., 2001a). Here we will assume a mortality of 100% for piglets, 50% for finishing pigs and 20% for sows. Compared to the fraction of pigs that show specific and severe clinical signs, these are high mortality rates that would favour the mortality data program. A sensitivity analysis of the mortality rates is essential to interpret the results. The infectious period distribution is assumed not to be influenced by the

mortality, i.e. infected pigs die at the end of their infectious period. 2.3. CSF detection model The CSF detection model is based on the model formulation of Klinkenberg et al. (2005), in particular the detection systems that rely on the herd-owner and veterinarian and on gross pathology for detection of the disease. We have omitted monthly inspections from the model as they did not prove efficient for detecting CSF (Klinkenberg et al., 2005). Also, we have not included the leukocyte counts program, but included exclusion diagnostics instead, as this option has recently become available and has replaced the leukocyte counts program. Obviously, we have added the detection system based on mortality data. Two alterations have been adopted. First, the probability distributions that describe how the herd-owner and veterinarian react to the number of specifically and severely diseased animals are based on cumulative rather than present numbers. This should reflect that a herd-owner is well aware of the recent disease history of his farm. Also the chance of suspecting CSF increases when a veterinarian visits the farms multiple times. Secondly, until recently samples were tested for CSF with an immunofluorescense assay with a relatively low sensitivity of 78% (Bouma et al., 2001). In all surveillance programs, this test has been replaced by a real-time RT-PCR test with a higher sensitivity of 98% and an assumed specificity of 100%. A schematic overview in Fig. 1 shows the probabilities and delays for the four different detection systems. They are discussed in more detail below. 2.3.1. CSF detection by herd-owner and veterinarian When a herd-owner notices specific clinical signs, he will warn his veterinarian. The probability of this event

J.A. Backer et al. / Preventive Veterinary Medicine 99 (2011) 38–47

depends on the cumulative number of specifically diseased animals: • on the first day with 5 animals showing specific clinical signs there is a 20% probability of warning the veterinarian; • on each day with an additional animal showing specific clinical signs there is an additional probability of 10%. This means that on the day the 13th animal shows specific clinical signs, the herd-owner will certainly call the veterinarian, if he has not done so before. The veterinarian will visit the next working day and has a 32% probability of suspecting CSF (Klinkenberg et al., 2005). If not, he will return a week later to assess the situation again. Here we assume that the probability of the veterinarian suspecting CSF increases with the number of visits v as 0.32v, with certain detection at the fourth visit. When the herd is suspected of CSF, the Food and Consumer Product Safety Authority (VWA) is notified. On the same day specialists will take blood samples of 5 diseased animals that are all assumed to be infected by CSF. These samples are tested by PCR with a sensitivity of 98% and a specificity of 100% per sample, the result of which will be available the next day. 2.3.2. CSF detection by gross pathology When infected animals show severe clinical signs, they might not be recognized as CSF-infected by the herdowner. Instead his veterinarian can decide to submit a diseased pig to the Animal Health Service (AHS) for gross pathological examination. The probability of this event depends on the cumulative number of severely diseased animals: • on the first day with 5 animals showing severe clinical signs there is a 20% probability of submitting an animal to the AHS; • every subsequent day there is an additional probability of 10%. This means that on the 9th day after the first day with 5 severely diseased animals, the veterinarian will certainly submit an animal to the AHS, if he has not done so before. The following working day, the animal is examined, leading to a CSF suspicion with a probability of 50%. When this happens the same chain of events is followed as in the previous detection system: the VWA visits the herd on the same day and takes 5 blood samples of which the test results will be available the next day. If the pathological examination does not lead to a direct CSF suspicion, the tonsils of the section animal are sent to the Central Veterinary Institute (CVI) the next day. Here they will be tested for CSF on a routine basis, which will detect the disease with an assumed sensitivity of 98% and specificity of 100%. The results are available the next day. 2.3.3. CSF detection by exclusion diagnostics Since 2006, veterinarians have had the opportunity to submit blood samples for exclusion diagnostics without the disadvantages of an official CSF suspicion (Elbers et al., 2007). It is to be used when many animals show

41

non-specific clinical signs that might indicate the first stages of CSF and when antibiotic treatment is started. As this program has replaced the leukocyte counts program (Klinkenberg et al., 2005), we will retain the same probability for activation: • on the first day with 40 animals showing non-specific clinical signs there is a 100% probability of submitting blood samples to the CVI. The following working day the samples will arrive at the CVI. Because of other diseases that can cause non-specific clinical signs, we will assume that at least two of the submitted samples are from CSF infected animals. They are tested by PCR with a 98% sensitivity and a 100% specificity, and the results are available the next day. 2.3.4. CSF detection by mortality data As an addition to the current surveillance programs a new detection method was evaluated based on the mortality reports received by the Dutch rendering plant. Each mortality report consists of the (anonymized) identifier of the reporting farm, the reporting date and the number of cadavers (finishing pigs, piglets and/or sows/gilts/boars) to be collected. Per mortality report the mortality incidence rate  k,i,j is expressed as the number of dead pigs per 1000 pig-days at risk: k,i,j = 1000

2mk,i,j (tk,i,j − tk,i,j−1 )(Nk,i,j + Nk,i,j−1 )

,

(1)

where mk,i,j is the number of dead pigs in category k (finishing pigs, piglets or sows) in the jth report of herd i, tk,i,j is the time of the jth report of type k pigs in herd i and Nk,i,j is the number of pigs of type k present in herd i at the time of the jth report. Not only the incidence rate itself, but also the change compared with previous reports can be an indication of a high mortality disease and possibly CSF. That is why the absolute differences with previous incidence rates of one, two and four reports before were also calculated. For each report day, the top 1% of the herds with the highest mortality incidence rate (differences) are marked as CSF suspect. So for each report it is checked whether one of its four characteristics (one mortality incidence rate and three mortality incidence rate differences) is in the top 1% of that day. If so, it will be marked as an attention herd. Beside these relative thresholds, the four mortality incidence characteristics are also compared with four absolute thresholds (Table 2). These are determined from the 99.9th percentile of the herds with the highest mortality incidence rate (differences) over the period 2001–2005 and they serve as a safety net when pig mortality is elevated for all herds. These thresholds – relative as well as absolute – differ between the three pig categories (finishing pigs, piglets and sows) and between large and small herds. When a mortality report exceeds one of the thresholds, the driver of the rendering truck is notified. When he visits the farm two working days later to collect the cadavers, he will take ear notch samples from maximally 5 dead animals (less if there are less cadavers to collect). These samples will be tested by real-time RT-PCR that can detect CSF virus from four days after infection onwards (Kaden et al., 2007).

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Table 2 Absolute thresholds for mortality incidence rate (differences) in number of dead pigs per 1000 pig-days at risk for each pig and size category (where N denotes the population size), based on the 99.9th percentile of the herds with the highest mortality incidence rate (differences) over the period 2001–2005.

Finishing pigs Mortality incidence rate Difference in mortality incidence rate of jth and (j − 1th) report Difference in mortality incidence rate of jth and (j − 2th) report Difference in mortality incidence rate of jth and (j − 4th) report Piglets Mortality incidence rate Difference in mortality incidence rate of jth and (j − 1th) report Difference in mortality incidence rate of jth and (j − 2th) report Difference in mortality incidence rate of jth and (j − 4th) report Sows Mortality incidence rate Difference in mortality incidence rate of jth and (j − 1th) report Difference in mortality incidence rate of jth and (j − 2th) report Difference in mortality incidence rate of jth and (j − 4th) report

The sensitivity se for these samples is assumed to be 98% and the specificity to be 100%, identical to the assumed sensitivity for blood and tonsil samples. The exact detection probability Pdet can be calculated by summing over all probabilities of a negative test result, determined by the hypergeometric distribution:

 



Min[n,s]

Pdet = 1 −

i=0

s i



m−s n−i

m n





(1 − se)i ,

(2)

where m is the total report size. The number of animals that died from CSF is s and so m − s is the number of animals that died from other causes. From these m dead animals a sample of n = Min[m, 5] animals is taken to be tested. The result of the PCR testing is available one day after the collection of the cadavers. 2.4. Data and simulations To simulate CSF outbreaks and their detection, an extensive database is used that lists all commercial pig enterprises in The Netherlands in the period 2001–2005. Each enterprise has an (anonymized) unique herd identity number and the number of finishing pigs, sows/boars and gilts present in the herd at different times over the 2001–2005 period. The number of piglets is not explicitly known but is approximately 4.3 times higher than the number of sows that is present (assuming 2.4 farrows a year per sow, consisting of 10.5 viable piglets each, that stay on the premises for 63 days). This herd-size information is needed for the simulations with the current early detection systems (i.e. detection by herd-owner and veterinarian, by gross pathology and by exclusion diagnostics). The database also includes all background mortality reports of finishing pigs, piglets and sows/boars/gilts, that will be needed to calculate the detection time with the new system based on mortality data. The three pig categories (i.e. finishing pigs, piglets or sows) are simulated separately, as intercategory transmission terms are difficult to quantify. This approach allows the effect of the pig category on detection to be studied. For

Small herds

Large herds

N < 200 107 89 77 82 N < 400 97 67 73 85 N < 200 17 16 16 17

N ≥ 200 10 8 8 8 N ≥ 400 40 34 33 33 N ≥ 200 8 8 8 8

each simulation in one of the three pig categories, a random herd and a random time is taken from the pig herd database. In this herd at that time one CSF infected pig is introduced, so the chance of introduction is equal for all herds in that pig category, irrespective of herd size or location. The transmission and the clinical signs are simulated until one of the current surveillance programs detects the disease. For the new early detection system, pig mortality and mortality reports, from both CSF and non-CSF causes, must also be simulated. The mortality reports in the database are the actual data on pigs that did not die from CSF. In the simulation, the deaths of these pigs are evenly spaced in the reporting interval, with the last death on the reporting day. The CSF deaths follow from the transmission and disease model and are added to the non-CSF deaths to construct the simulated mortality reports. The new early detection system acts on these simulated mortality reports to detect the disease. Per pig category 500,000 CSF infections are simulated to cover all herds over the entire time period in the dataset.

3. Results 3.1. Current detection systems Over the period 2001–2005, 23,417 unique herds with at least 10 animals (finishing pigs, piglets or sows) were recorded in the database. Of these, 11,777 were finishing herds with an average size of 504 (16–2140, denoting the 95% interval) animals. The 5945 piglet sections were larger in size, with an average of 780 (18–2777) animals. The smallest were the 5695 sow sections with on average 227 (13–883) animals. When the current detection systems are in place, the model predicts that it will take 38 (27–53) days between introduction of the virus in a finishing herd and a CSF confirmation (Table 3). The variation in this outcome reflects the combined stochastic effects in the virus transmission, the development of clinical signs and the activation of the detection systems. As the virus transmission is higher in piglet sections – due to a higher within-pen reproductive

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Table 3 Simulated effect of the inclusion of the new early detection system based on mortality data (program d in Fig. 1) in the current early detection systems (programs a–c in Fig. 1) on the median detection time in days, on the percentage of outbreaks that are detected and on the median number of herds identified as CSF suspect per report day for each pig category in The Netherlands over the period 2001–2005. Current detection systems Detection time in days Finishing herd 38 (27–53)a Piglet section 32 (22–45) Sow section 42 (32–55) Percentage of outbreaks detected (by mortality) Finishing herd 96.4 (0)b Piglet section 98.1 (0) Sow section 89.4 (0) Number of attention herds per report-day Finishing herd – Piglet section – Sow section – a b

Current and new detection systems Top 0.5%

Top 1%

Top 2%

37 (25–52) 32 (22–45) 41 (32–55)

36 (24–51) 32 (22–44) 41 (32–55)

34 (23–51) 32 (22–44) 41 (32–55)

97.0 (14.7) 98.2 (0.5) 89.6 (1.8)

97.4 (25.5) 98.2 (1.3) 89.7 (4.4)

97.6 (38.1) 98.3 (3.0) 89.8 (8.3)

4 (0–7)a 2 (0–3) 1 (0–1)

8 (0–13) 4 (1–7) 2 (0–3)

16 (1–27) 8 (1–13) 3 (1–6)

Between brackets the 2.5%–97.5% interval. Between brackets the percentage of outbreaks detected by mortality program.

number – the disease will be detected sooner; according to the model 32 (22–45) days after introduction. The slowest detection time of 42 (32–55) days is predicted for sow sections, which agrees with the results of Engel et al. (2005) who analyzed the clinical reports of the 1997–1998 CSF outbreak in The Netherlands. The left panel of Fig. 2 shows the detection time distribution of the current systems in each herd type. For finishing herds and piglet sections, the detection by herd-owner and veterinarian and the detection by gross pathology are expected to act sooner than exclusion diagnostics, presumably because of the pen structure of these herds. For sow sections the difference in timing of the three current programs is not as clear. In all herd types a relatively small part of the outbreaks is detected by herd-owner and veterinarian, while the exclusion diagnostics program is expected to play an important role in the detection (Fig. 2). Not all outbreaks are detected; especially in sow sections nearly 10% of the outbreaks go undetected. In such minor outbreaks the infection fades out due to chance before any of the detection systems is activated. Still, they result in a (small) number of seropositive animals. 3.2. New detection system In the 2001–2005 database the mortality reporting behavior of each unique herd is registered. The reporting frequency and amounts differ largely between the herd types. Finishing herds report on average every 14 (2–62, denoting the 95% interval) days with an average report size of 2.7 (1–9) dead animals. Piglet sections have in general a higher mortality; they report as frequently as finishing herds, once every 14 (3–40) days, but the number of dead animals is higher, on average 44 (5–150) per report. Reports from sow sections are rarest; every 31 (2–144) days they register a report with an average size of 1.3 (1–3) dead animals. On each reporting day the new early detection system earmarks the top 1% of the herds with the highest mortality incidence rate (differences) for ear notch sampling and

PCR testing. This results in visiting a median number of 8 (0–13) finishing herds, 4 (1–7) piglet sections and 2 (0–3) sow sections per workday (Table 3, top 1%). These numbers can vary in time as the mortality of pigs fluctuates throughout the year. The inclusion of the new early detection system leads to a two-day gain in detection time on finishing herds. On average 36 (24–51) days after infection the CSF outbreaks are detected, of which approximately 26% by the new detection system based on mortality data (Table 3, top 1%). The new program clearly shifts the detection time distribution to earlier times (right panel of Fig. 2). On piglet sections the new system has no distinct effect. With a detection time of 32 (22–44) days only the upper bound has decreased by one day compared to the result of the current systems and the new system detects only 1.3% of the outbreaks. Apparently, the mortality from non-CSF causes is so high that the CSF-mortality is not noticed by the new detection system. Finally, the new detection system in sow sections only shifts the median detection time by one day to 41 (32–55) days, and accounts for 4.4% of the detected outbreaks. Even though the background mortality in sows is low, the CSF-induced mortality is too low to provide improvement over the current detection systems. 3.3. Sensitivity to number of attention herds The effectiveness of the mortality program depends on the number of herds that are identified as CSF suspect. When the relative thresholds are doubled (top 2%) or halved (top 0.5%), the number of herds that should be tested is doubled or halved and the median detection time in finishing herds decreases by two days or increases by one day (Table 3). However, the effect on the 97.5th percentiles of the detection time is less pronounced. When more herds are earmarked as attention herd, the share of the mortality program in detecting outbreaks increases, but the total percentage of outbreaks that is detected does not increase markedly.

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Fig. 2. Simulated detection time distributions (in days) for different herd types with and without the new early detection system based on mortality data (where the top 1% herds are identified as CSF suspect); detection by herd-owner and veterinarian (light grey), gross pathology (grey), exclusion diagnostics (dark grey) and mortality data darkest grey.

3.4. Sensitivity to CSF-induced mortality The results for the new early detection system are determined by the assumptions made for the CSF mortality. This depends on the virulence of the virus strain and the pig category (i.e. finishing pigs, piglets and sows). These different virulences are explored in a sensitivity analysis by varying the mortality from 0% to 100% (Fig. 3). Pigs infected with a strain of low virulence often survive the infection, on which the new early detection system has no effect. For moderately virulent strains a mortality of 50% for finishing pigs (Laevens et al., 1999; Dewulf et al., 2001b) leads to a two-day gain in detection time. For more virulent strains the mortality can reach 100% in finishing

pigs (Moormann et al., 2000; Bouma et al., 2000) which results in a maximally attainable detection time gain of 6 days (with a detection time of 32 (22–50) days). For piglets, detection is not improved by the new system, even with the highest possible mortality of 100%. The assumed mortality of 20% in sows is too low for the mortality program to improve the detection time. At higher mortalities, the new system would have an effect on the detection time, up to four days at an – unrealistically high – mortality of 100%. However, it should be kept in mind that when mortality increases, the percentage of pigs that will show severe and specific clinical signs also increases, leading to an earlier triggering of the current detection systems. So, a more vir-

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mortality fraction

(c) Sow section Fig. 3. Simulated detection time as a function of assumed CSF-induced mortality for (a) finishing herds, (b) piglet sections and (c) sow sections; median values (solid line) and 95% interval (shaded area).

ulent strain will improve detection in the current as well as in the new detection system. 4. Discussion We have developed a model to estimate the gain in detection time for CSF outbreaks when the current early detection systems are extended with a new early detection system based on mortality data. This study predicts that

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the new detection system based on mortality data would lead to a detection time of 36 (24–51) days in finishing pig herds, compared to 38 (27–53) days with the current detection systems, when assuming a moderately virulent virus strain causing a 50% CSF mortality in finishing pigs. For sow sections, detection times change from 42 (32–55) to 41 (32–55) days, when a CSF mortality of 20% in sows is assumed. In piglet sections the new system has no effect as only the upper bound of the detection time of 32 (22–45) days decreases by one day, even with a CSF mortality of piglets of 100%. The gain of two days in finishing pig herds is relatively small when considering the variation in outcome, caused by stochastic effects in virus transmission, the development of clinical signs and the activation of the detection systems. But when considering the total number of infected herds at the time of first detection in the country, this gain of two days could translate into a reduction of 18% in the number of infected herds. For this estimation we used data from the first phase of the CSF epidemic in The Netherlands in 1997/1998 (Stegeman et al., 1999). For each weekly interval we assumed that the number of cases follows a binomial distribution with S susceptible herds and an infection probability of 1 − exp(ˇI/N), where S, I and N are the number of susceptible, infectious and all herds. Maximizing the loglikelihood function leads to a value for the infection rate parameter ␤ between herds of 0.71 (0.51–0.95) per week. Each day that the disease is not yet detected the number of infected herds increases on average by a factor exp(ˇ/7) = 1.11. A smaller number of initially infected herds can considerably reduce the cost of controlling the epidemic. But in a cost-benefit analysis the period between CSF virus introductions should also be taken into account, during which the new detection system must be sustained economically (De Vos et al., 2004). The assumptions made in the model for the current detection systems (Section 2.3) are based on Klinkenberg et al. (2005), but some alterations have been made. For instance, we have defined three different pig categories in our model, because virus transmission and mortality (by CSF and by other causes) are different among these groups. Because we used the CSF disease model of (Klinkenberg et al., 2005) which is based on clinical observations of 41 finishing pigs, piglets and sows grouped together, we did not distinguish between pig categories regarding clinical signs and detection. The effect on model results is hard to estimate, especially because the detection probabilities are based on expert opinion and difficult to support quantitatively. It can be argued that although sows show less clinical signs (resulting in a later detection than predicted by the disease model) they are also monitored more closely (resulting in an earlier detection than predicted by the current detection models). This means that the errors made by not taking the pig categories into account in the disease and current detection models, at least have a counteracting effect. Another difference with Klinkenberg et al. (2005) is the inclusion of a pen structure for finishing pigs and piglets in our model. As the within-pen reproduction number is much higher than the within-herd reproduction number (for which the observed value of 2.8 is used in both studies), the virus will spread in a step-wise manner from pen to pen

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in our model. This caused more stochastic variation in the detection time results. In this study we have only considered the detection of outbreaks at the farm level in isolated populations of finishing pigs, piglets or sows. When between-herd transmission is also taken into account, the detection times could be earlier than reported here, because the first detected case is not necessarily the source herd (Klinkenberg et al., 2005). However, this applies to the current detection systems as well as the new system based on mortality data. So, it is not expected that this would change the relative performance of the detection systems. The detection systems based on clinical signs and on mortality data are independent of each other. Although this enables the comparison of the isolated systems, it has two improbable consequences. First, the dead pigs do not contribute to triggering the herd-owner to activate one of the current detection systems. Second, because of the independent probabilities of disease signs and mortality, pigs can die before showing any clinical signs. Assuming a 50% mortality (for finishers), the probability of a pig dying before showing aspecific signs is 0.005%, before showing specific signs is 4% and before showing severe signs is 7%. Both of these consequences favour the detection system based on mortality data. The results for the new early detection system are also determined by CSF-specific mortality assumptions. This depends on the virulence of the virus strain and the pig category. In the model, we chose a 100% mortality in piglets, 50% in finishing pigs and 20% in sows, based on moderately virulent strains (Laevens et al., 1999; Dewulf et al., 2001b). This leads to a two-day gain in detection time in finishing pigs and a one-day gain in sows. Pigs infected with a strain of low virulence often survive the infection, on which the new early detection system has no effect at all. However, clinical signs may not be as apparent, emphasizing the importance of constant careful clinical examination of pig herds. For more virulent strains the mortality can reach 100% in finishing pigs (Moormann et al., 2000; Bouma et al., 2000), which resulted in a maximally attainable gain in detection time of 6 days. For piglets, detection is never improved by the new system, even with the highest possible mortality of 100%. The estimated time to early detection also depends on the assumed time delays in the different detection systems (Fig. 1). The first two detection systems (by herd-owner/veterinarian and by gross pathology) are well established practices and the modeled delays are fairly accurate. Depending on timing and alertness a suspicion can even be resolved within 24 h. For the detection by exclusion diagnostics the delays are less certain, because not much experience has been obtained with this system in practice. The new early detection system based on mortality data has not been implemented and its delays are a first assumption of how the system could work. Especially the fact that this new detection system is now proposed as a routine system (with around 2000 alerts per year) in contrast to the current emergency systems (with around 10 suspicions and 20 blood sample submissions per year), might lower the sense of urgency and thus increase the delay of the detection process.

In conclusion, the model predicts that the new early detection system based on mortality data would improve the detection time in finishing herds by two days, that can lead to a considerably lower number of initially infected herds. However, this effect cannot be substantiated when we consider the uncertainty in some model assumptions, especially in the disease model, the mortality rate and the triggering of the current detection systems. For this reason it can not be concluded that the new early detection system based on mortality data provides a substantial improvement over the current detection systems. Conflict of interest None. Acknowledgments We thank Dr. W.L.A. Loeffen (CVI) for his contribution to the model and discussion of the results, W.A.J.M. Swart (AHS) for the statistical analysis of the mortality database and Dr. P.J. van der Wolf (AHS). This study was part of a GIQS (Grenzüberschreitende Integrierte Qualitätssicherung e.V.) project called “Risico’s beheersen/Risiken beherrschen” and funded by the EU INTERREG IIIA program of the Euregios Rhine Maas North and Rhine Waal, the Northrhine Westfalian Ministry of Economy and Dutch Ministry of Agriculture, Nature and Food Quality as well as the Province of Limburg. References Backer, J.A., Hagenaars, T.J., van Roermund, H.J.W., de Jong, M.C.M., 2009. Modelling the effectiveness and risks of vaccination strategies to control classical swine fever epidemics. J. R. Soc. Interface 6, 849–861, doi:10.1098/rsif.2008.0408. Becker, N.G., Dietz, K., 1995. The effect of household distribution on transmission and control of highly infectious diseases. Math. Biosci. 127, 207–219, doi:10.1016/0025-5564(94)00055-5. Bouma, A., de Smit, A.J., de Jong, M.C.M., de Kluijver, E.P., Moormann, R.J.M., 2000. Determination of the onset of the herd-immunity induced by the E2 sub-unit vaccine against classical swine fever virus. Vaccine 18, 1374–1381, doi:10.1016/S0264-410X(99)00398-9. Bouma, A., Stegeman, J.A., Engel, B., de Kluijver, E.P., Elbers, A.R.W., Jong, M.C.M.D., 2001. Evaluation of diagnostic tests for the detection of classical swine fever in the field without a gold standard. J. Vet. Diagn. Invest. 13, 383–388. De Vos, C.J., Saatkamp, H.W., Nielen, M., Huirne, R.B.M., 2004. Scenario tree modeling to analyze the probability of Classical Swine Fever virus introduction into the member states of the European Union. Risk Anal. 24, 237–253, doi:10.1111/j.0272-4332.2004.00426.x. Dewulf, J., Laevens, H., Koenen, F., de Kruif, A., 2001a. An experimental infection with classical swine fever virus in pregnant sows: transmission of the virus, course of the disease, antibody response and effect on gestation. J. Vet. Med. B 48, 583–591. Dewulf, J., Laevens, H., Koenen, F., Vanderhallen, H., Mintiens, K., Deluyker, H., de Kruif, A., 2001b. An experimental infection with classical swine fever in E2 subunit marker-vaccine vaccinated and in non-vaccinated pigs. Vaccine 19, 475–482, doi:10.1016/S0264-410X(00)00189-4. Dewulf, J., Laevens, H., Koenen, F., Mintiens, K., de Kruif, A., 2004. Efficacy of E2-sub-unit marker and C-strain vaccines in reducing horizontal transmission of classical swine fever virus in weaner pigs. Prev. Vet. Med. 65, 121–133, doi:10.1016/j.prevetmed.2004.05.010. Elbers, A.R.W., Gorgievski-Duijvesteijn, M.J., van der Velder, P.G., Loeffen, W.L.A., 2007. Aspecific clinical signs in pigs and use of exclusion diagnostics for classical swine fever: a survey among pig farmers and veterinary practitioners. Tijdschr. Diergeneeskd. 132, 340–345. Engel, B., Bouma, A., Stegeman, A., Buist, W., Elbers, A., Kogut, J., Döpfer, D., de Jong, M.C.M., 2005. When can a veterinarian be

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