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The role of backyard poultry flocks in the epidemic of highly pathogenic avian influenza virus (H7N7) in the Netherlands in 2003
Preventive Veterinary Medicine 88 (2009) 247–254
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The role of backyard poultry flocks in the epidemic of highly pathogenic avian influenza virus (H7N7) in the Netherlands in 2003§ V. Bavinck a, A. Bouma a,*, M. van Boven b,1, M.E.H. Bos a, E. Stassen c, J.A. Stegeman a a
Faculty of Veterinary Medicine, Department of Farm Animal Health, Utrecht University, The Netherlands Animal Sciences Group, Wageningen University and Research Centre, Lelystad, The Netherlands c Wageningen University and Research Centre, Wageningen, The Netherlands b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 9 April 2008 Received in revised form 14 October 2008 Accepted 16 October 2008
In recent years, outbreaks of highly pathogenic avian influenza (HPAI) viruses have caused the death of millions of poultry and of more than 200 humans worldwide. A proper understanding of the transmission dynamics and risk factors for epidemic spread of these viruses is key to devising effective control strategies. The aim of this study was to quantify the epidemiological contributions of backyard flocks using data from the H7N7 HPAI epidemic in the Netherlands in 2003. A dataset was constructed in which flocks in the affected area were classified as susceptible (S), infected but not yet infectious (E), infectious (I), and removed (R). The analyses were based on a two-type SEIR epidemic model, with the two types representing commercial poultry farms and backyard poultry flocks. The analyses were aimed at estimation of the susceptibility (g) and infectiousness (f) of backyard flocks relative to commercial farms. The results show that backyard flocks were considerably less susceptible to infection than commercial farms (gˆ ¼ 0:014; 95%CI ¼ 0:0071 0:023), while estimates of the relative infectiousness of backyard flocks varied widely (0 fˆ 5). Our results indicate that, from an epidemiological perspective, backyard flocks played a marginal role in the outbreak of highly pathogenic avian influenza in the Netherlands in 2003. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Avian influenza Backyard poultry Transmission Reproduction number SEIR model
1. Introduction Highly pathogenic avian influenza (HPAI) is a devastating disease for poultry, causing high mortality rates and huge economic damage due to production losses and export bans (Alexander, 2000, 2003; Henzler et al., 2003). Moreover, since the start of outbreaks of subtype H5N1 in South East Asia in 1997, the fear of a human influenza pandemic has
§ This study was financed by the Netherlands Organisation for Scientific Research (NWO). * Corresponding author at: Marburglaan 2, 3584 CN Utrecht, The Netherlands. Tel.: +31 30 2531013; fax: +31 30 2521887. E-mail address: [email protected] (A. Bouma). 1 Current address: Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
0167-5877/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.prevetmed.2008.10.007
arisen (e.g. Webby and Webster, 2003). Although the focus is currently on HPAI H5N1, outbreaks of other subtypes, mainly H7, have occurred in recent years (Alexander, 2007), for example in Italy (Capua et al., 2003), the Americas (OIE, 2003; Max et al., 2007), the Netherlands (Stegeman et al., 2004) and South Africa (OIE, 2003; Bowes et al., 2004). Outbreaks of HPAI are usually controlled by measures such as culling of infected flocks to reduce virus output and pre-emptive depopulation of contiguous flocks, as for example during the H7N7 epidemic in The Netherlands in 2003 (Stegeman et al., 2004). This epidemic was successfully controlled, but only after the killing of 30 million birds and infection of 89 humans (Koopmans et al., 2004; Fouchier et al., 2004). The measures were applied to commercial holdings as well as to non-commercial backyard poultry flocks, which are generally assumed to be at risk for AI virus introduction from migratory birds or spill
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over from contiguous commercial flocks as hypothesized for example for Europe (e.g. Stegeman et al., 2004; Gilbert et al., 2006; Terregino et al., 2007), Nigeria (Ducatez et al., 2006), and Indonesia (Sedyaningsih et al., 2007). Public opinion, however, turned against the massive pre-emptive culling of often healthy-poultry (Thomas et al., 2005). Especially the owners of small backyard flocks did not consider their poultry an important factor in the spread of HPAI virus and were strongly opposed preemptive culling of their flocks, which they considered to be in the interest of the commercial poultry sector only. They argued that because the size of backyard flocks was much smaller than commercial flocks the risk of virus introduction would also be much smaller (Refregier-Petton et al., 2001; Akey, 2003). Moreover, they mentioned that the housing systems of backyard poultry often did not have a forced ventilation system, and that there would be hardly any contact with commercial holdings (Thomas et al., 2005). Some even argued that backyard poultry would be intrinsically less susceptible than commercial poultry as the backyard flocks and commercial farms generally contain different breeds. In contrast to the Dutch situation, backyard poultry is considered being an important source of spread and persistence of HPAI H5N1 in, for example, South East Asia. In Thailand, for instance, 83% of the total outbreaks concerned backyard chickens and free-grazing ducks (Tiensin et al., 2005). As the animal husbandry systems differ substantially between countries, the question is whether this may also be valid for the Netherlands or other European countries. During the Dutch H7N7 epidemic in 2003 unique data were collected for epidemiological studies about evaluation of control measures. So far, the analyses were restricted to data from commercial holdings (Stegeman et al., 2004; Thomas et al., 2005; Boender et al., 2007), whereas poultry on many premises were held in backyards. To gain more insight in the epidemiology of HPAI and to improve the control strategy for a future epidemic we provide a retrospective analysis of the epidemic of HPAI H7N7 in the Netherlands in 2003, with the goal to determine the actual role of backyard flocks in that epidemic. We carried out statistical analyses based on a stochastic epidemic model, which yield quantitative estimates of epidemiological parameters such as the transmissibility of HPAI virus, and the relative susceptibility and infectiousness of backyard flocks. 2. Methods 2.1. Study population In the Netherlands, backyard hobby flocks were mainly kept outdoors in a confined space. The Dutch Ministry of Agriculture, Nature and Food Quality (LNV) defined a flock as hobby or backyard flock, when they consisted of fewer than 500 birds or did not have a unique farm number (LNV, 2003). The first outbreak with the H7N7 strain occurred in the central part of the Netherlands (the Gelderse Vallei), and later, the virus had spread to the province of Limburg (Stegeman et al., 2004). In the Gelderse Vallei H7N7 HPAI
virus was isolated from samples collected on 183 commercial and 12 backyard flocks. In Limburg, virus was detected on 32 commercial flocks and 2 backyard flocks (Stegeman et al., 2004). The current study was based on data obtained from outbreaks in the Gelderse Vallei only. The total number of commercial farms in this area was 984, and the number of backyard flocks was estimated to be 3601 (LNV, 2003). Data from Limburg were not used as the number of infected flocks was too low to obtain estimates with reasonable precision. Premises were defined as having been infected if HPAI virus strain H7N7 was isolated by RT-PCR on trachea or cloacal swabs taken from birds with signs of AI (OIE, 2003; Stegeman et al., 2004). The days of notification and depopulation of all infected flocks were recorded; the depopulation day only was recorded for all pre-emptively culled flocks (LNV, 2003; see also Stegeman et al., 2004; Thomas et al., 2005). All pre-emptively culled commercial flocks were tested either by PCR on tracheal swabs (if signs of AI were noticed) or, if no AI-like signs were seen, serologically by testing 20 samples per farm. Trachea swabs were taken from clinically affected birds; serum samples were taken from randomly selected birds in a shed. Sample size was based on the Council Directive 92/ 40/EEC (1992): if a flock was smaller than 20 heads, all birds were supposed to be sampled; if a flock was larger, 20 birds per flock should be sampled. If the tests on these samples were negative, the flocks were considered not infected (Thomas et al., 2005). The records were used to construct a working dataset with daily numbers of notifications, infected flock depopulations and uninfected flock depopulations. 2.2. Data Estimation of the transmission parameter required knowledge of the number of susceptible and infectious flocks over time, and the number of new cases (infected flocks) per period. A dataset was constructed in which flocks were classified as susceptible (S), infected but not yet infectious (E), infectious (I), and removed (R). In the analyses presented here flocks were assumed identical in all other aspects. We assumed that, from the moment of introduction of the virus, a flock would have been latently infected for about 2 days (Savill et al., 2006; Van der Goot et al., 2007) and then become infectious after which the infection would have been detected due to occurrence of clinical signs or increased mortality. The infectious period ended at the day of depopulation. A different length of the infectious period was estimated for the first five infected flocks, because the day of notification was most likely later than it would have been during the epidemic, due to poor recognition of AI at the start of the epidemic (Elbers et al., 2004). Based on these estimates a dataset was constructed in which farms were assumed to have been infected 8–12 days (first five infected farms) or 4 days (remaining cases) before notification (Stegeman et al., 2004). The number of susceptible farms during the epidemic decreased over time due to infection and culling. We refer Stegeman et al. (2004) and Boender et al. (2007)
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Table 1 Overview of the model scenarios. The full model containing four parameters (bij, i, j = 1, 2) is labeled scenario M0. A number of simplified scenarios are also considered based on M1 (see Table for description of the various scenarios). Model scenario
Estimated parameters
Description
M0 M1 M2 M3 M4 M5
b11, b12, b21, b22 b, f, g b, g b, g b, f b
M6
b
Full model Simplified model assuming proportionate mixing (default scenario) As M1 but assuming that backyard flocks are not infectious (f = 0) As M1 but assuming that commercial and backyard flocks are equally As M1 but assuming that commercial and backyard flocks are equally As M1 but assuming that commercial and backyard farms are equally and that back yard farms are not infectious (f = 0, g = 1) As M1 but assuming that commercial and backyard farms are equally and equally infectious (f = 1, g = 1)
for details on the construction of the dataset. Table 1 gives an overview of all model scenarios.
infectious (f = 1) susceptible (g = 1) susceptible, susceptible
susceptibility of type j farms, respectively (Diekmann and Heesterbeek, 2000). For our purposes the set of simplified models is conveniently parameterized as follows:
2.3. Epidemic model
b11 ¼ b; The analyses were based on a two-type SEIR epidemic model, with the two types representing commercial flocks (type 1) and backyard flocks (type 2). The forces of infection l1(t) and l2(t), which represent the infection hazards on type 1 and type 2 susceptible holdings, hold centre stage in the analyses, and are given by weighted sums of the number of infectious commercial and backyard farms, i.e.
l j ðtÞ ¼ b1 j I1 ðtÞ þ b2 j I2 ðtÞ
(1)
(j = 1, 2). In Eq. (1), I1(t) and I2(t) represent the numbers of infectious type 1 and type 2 farms at time t, and bij (i, j = 1, 2) are the between flock transmission rate parameters (Diekmann and Heesterbeek, 2006). By standard reasoning, the probabilities of infection in a small time interval (t, t + Dt] are given by p j ðtÞ ¼ 1 el j ðtÞDt
(2)
(j = 1, 2). Now, given that there are Sj(t) susceptible farms of type j at time t, the probabilities qj(t) of observing Cj(t, t + Dt) new infections in the interval (t, t + Dt] are binomially distributed with parameters pj(t) and binomial totals Sj(t): S j ðtÞ q j ðtÞ ¼ (3) p j ðtÞC j ðtÞ ð1 p j ðtÞÞðS j ðtÞC j ðtÞÞ C j ðtÞ (j = 1, 2). The likelihood of the data is given by the product of the probabilities of infection over the different types of farms and over all observation intervals. The log-likelihood, which is computationally more convenient to work with than the likelihood, is given by ‘¼
t last X
ðlog q1 ðtÞ þ log q2 ðtÞÞ;
(4)
t¼t first
where the summation runs across all observation intervals. In all our analyses we have taken Dt = 1 day. The model formulation in Eq. (1) puts no constraints on the four transmission parameters bij (i, j = 1, 2). In the following we will focus in detail on a suite of simplified models that are based on the assumption of proportionate mixing, i.e. which assumes that the transmission parameters can be written in the form bij = aibj, where ai and bj represent the (absolute) infectiousness of type i and (absolute)
b12 ¼ bg;
b21 ¼ b f ;
b22 ¼ b fg:
(5)
In Eqs. (1)–(5) the parameter b represents the transmission rate parameter determining transmission between commercial farms, and the parameters f and g represent the infectiousness and susceptibility of backyard farms relative to that of commercial farms. Hence, the transmission hazard in a population of commercial farms only is given by l1(t) = bI1(t), and it is given by l2(t) = bfgI2(t) in a population of backyard flocks only. The goal is to obtain estimates of the three parameters b, f, and g. Notice that the full model contains four parameters, while the simplified model specified by Eq. (5) contains three parameters. 2.4. Statistical analysis The maximum likelihood estimates of the parameters of interest are readily obtained by maximization of the loglikelihood in Eq. (4). Confidence intervals are calculated on the basis of profile likelihoods. To be able to compare and choose between models of different complexity we have made use of Akaike’s Information Criterion (Burnham and Anderson, 2002). Mathematica 6.0 was used for the data processing and statistical analyses. 2.5. Sensitivity analyses In the following the full model containing four parameters (bij, i, j = 1, 2) is labeled scenario M0. In our default scenario containing three parameters (labeled M1) we assumed proportionate mixing, and estimated three parameters (b, f, and g). To investigate the robustness of the results obtained using scenarios M0–M1, and to be able to determine the optimal model complexity, we considered a number of simplified scenarios based on M1, but with f = 0 (backyard farms are not infectious at all) (scenario M2), with f = 1 (commercial and backyard farms are equally infectious) (scenario M3), with g = 1 (commercial and backyard farms are equally susceptible) (scenario M4), with f = 0 and g = 1 (commercial and backyard farms are equally susceptible but backyard farms are not infectious) (scenario M5), and with f =g = 1 (commercial and backyard farms are equally susceptible and infectious) (scenario M6). Table 1 gives an overview of the different model scenarios.
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In addition to the model scenarios M0–M6, we assumed in our default scenario (M1) that moment of infection was 12 days before notification for the first five cases, and 7 days before notification of increased mortality or signs of AI for all other cases (Stegeman et al., 2004). A sensitivity analysis with respect to this assumption was performed by back-calculation of the moment of virus introduction based on within-flock mortality (Bos et al., 2007).
3. Results 3.1. Descriptive statistics During and after the epidemic, 30 million birds were killed of which approximately 180,000 were kept in backyard flocks. The number of poultry in backyard flocks varied between 4 and 99 birds (LNV, 2003). Fig. 1 shows a map of the Gelderse Vallei and the approximate location of
Fig. 1. . Location of commercial and backyard poultry premises in the Gelderse Vallei during the H7N7 HPAI epidemic in the Netherlands in 2003. Grey dots: uninfected commercial farms; black dots: infected commercial farms; red dots: infected backyard poultry (kindly provided by Dr. Gert-Jan Boender, Animal Sciences Group, Lelystad, The Netherlands).
V. Bavinck et al. / Preventive Veterinary Medicine 88 (2009) 247–254
all commercial farms (n = 984), all infected commercial farms (n = 183), and all infected backyard flocks (n = 12) in the area. All premises were defined as being infected when HPAI virus strain H7N7 was isolated by RT-PCR. Six of the infected backyard flocks contained different chicken breeds and/or different bird species, such as ducks, ostriches, peacocks, pheasants, swans or geese. Ten backyard poultry keepers reported a sudden increase of mortality, in nine cases mortality concerned chicken only (LNV, 2003). All infected backyard flocks were located within close vicinity of infected commercial flocks. As for all infected flocks, back tracing of contacts between infected flocks was carried out to find a possible virus source, according to the regulations of the European Union (Council Directive 92/40/EEC, 1992). Only one contact (a co-worker) between a commercial farm and a backyard poultry flock was found, and it could not be demonstrated whether this contact was really the source of the introduction. No other specified virus transmission routes were found. 3.2. Parameter estimates ˆ (i, j = 1, 2) of the full model The parameter estimates b ij M0 containing four parameters had a lower support than either of the models M1–M3 that assume proportionate mixing and in which at least the parameter b describing transmission between commercial farms and the parameter g describing the relative susceptibility of backyard farms are estimated (Table 2). Therefore, we will focus in the following on models that assume proportionate mixing and contain two or three parameters. Table 2 shows the parameter estimates of the models. Model M2 in which backyard farms were assumed to be non-infectious (f = 0) had the highest support, although models M1 and M3 (f = 1) also had high support. All these models yielded consistently low estimates of the relative susceptibility of backyard flocks relative to commercial farms. In fact, in all these models g was estimated to be 0.013–0.014, and all three models yielded 95% confidence intervals ranging from 0.007 to 0.023. This indicates that backyard flocks are less susceptible to infection than commercial farms (since g < 1). With regard to the relative infectiousness of backyard flocks not much can be said. Model scenario M1 yielded an estimate of the relative infectiousness of fˆ ¼ 0:37, but with a broad confidence interval (95%CI = 0–5.0). In addition,
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both models scenarios M2 and M3 which assumed either f = 0 or f = 1 had higher support than model M1, indicating that the data did not allow precise estimates of the parameter f to be made. Finally, the remaining models (M4–M6) in which commercial and backyard flocks were assumed to be equally susceptible (g = 1) had very low overall support, and can safely be excluded from consideration. Apparently, these models are unable to give a satisfactory description of the data. Estimates of the transmission parameter b were similar for all model scenarios M0–M3 that had high support, and show that the rate at which an infected commercial farm infects a specific susceptible farm in a population consisting almost exclusively of susceptible farms is about 1.7 104 day1 farm1. The estimated total between-farm transmission rate is given by the ˆ and the total number of commercial farms, product of b i.e. 1.7 104 984 = 0.17 day1. This implies that an infected commercial farm is expected to infect, on average, 0.17 other commercial farms per day over the course of the epidemic. Likewise, if we denote by S1 and S2 the total number of commercial and backyard flocks, an infected commercial ˆ gS ˆ 2 ¼ 1:7 farm is expected to infect on average of b 104 0:014 3601 ¼ 8:6 103 backyard flocks per day in the early stages of the epidemic. In the default analysis, the mean infectious periods of commercial and backyard flocks were Tˆ1 ¼ 7:8 day and Tˆ2 ¼ 7:1 day, respectively (data not shown). Hence, it follows that the expected total number of type j infections caused by a type i infected flock, Rˆ i j , is given by ! bˆ Tˆ1 S1 bˆ gˆTˆ1 S2 Rˆ 11 Rˆ 12 ¼ Rˆ 21 Rˆ 22 bˆ fˆTˆ2 S1 bˆ fˆgˆTˆ2 S2 1:3 0:067 ¼ : (6) 0:44 0:023 An estimate of the overall reproduction number R is given by the largest eigenvalue of the above matrix of individual reproduction numbers. For the parameter estimates of Eq. (6) (which are based on model M1) this yields Rˆ ¼ 1:33. It should be noted that the above analysis neglects the fact that, during the Dutch epidemic, there were substantial differences in the transmission rates and infectious periods in the early versus late stages of the
Table 2 Estimates of the parameters b (between flock transmission rate), fˆ (infectiousness), and gˆ (susceptibility) for H7N7 avian influenza in commercial and backyard flocks in various model scenarios. Model scenario
Parameter estimates (95%CI)
bˆ ðday1 farm1 Þ M0 M1 M2 M3 M4 M5 M6 a b c
(full model) (default scenario) (f = 0) (f = 1) (g = 1) (f = 0, g = 1) (f = g = 1)
1.7 104 1.7 104 1.7 104 1.7 104 3.1 105 3.1 105 3.0 105
ˆ ¼b ˆ ) (1.3 104–2.0 104) (b 11 (1.3 104–2.0 104) (1.5 104–2.0 104) (1.4 104–1.9 104) (2.7 105–3.6 105) (2.7 105–3.6 105) (2.6 105–3.4 105)
AIC = Akaike’s Information Criterion (AIC). Corresponding model supports. NA: not applicable.
fˆ
gˆ
NAc 0.37 (0–5.0) – – 0 (0–1.3) – –
NA 0.014 (0.0071–0.023) 0.014 (0.0071–0.023) 0.013 (0.0071–0.023) – – –
AICa
Supportb
288.15 287.36 285.44 285.52 843.84 841.84 844.79
0.10 0.15 0.38 0.37 0 0 0
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epidemic (Stegeman et al., 2004). As a consequence, Eq. (6) is better suited to get an impression of the relative contributions of backyard flocks versus commercial farms than to get a reliable estimate of the absolute magnitude of the reproduction numbers of commercial farms and backyard flocks in the early stage of the epidemic. The sensitivity analysis with respect to changes in day of introduction into the flocks yielded results that were ˆ ¼ 1:0 104 ð95%CI ¼ 1:0 similar to those in Table 2 ðb 104 1:4 104 Þ; fˆ ¼ 0ð95%CI ¼ 01:5Þ; gˆ ¼ 0:014ð95%CI ¼ 0:000740:024ÞÞ: 4. Discussion Backyard poultry is considered a risk for becoming infected with AI virus (e.g. Sedyaningsih et al., 2007; Gilbert et al., 2006; Ducatez et al., 2006; Saad et al., 2007; Gall-Recule´ et al., 2008; Lee et al., 2008), but their role in the spread of the virus during the epidemic in the Netherlands in 2003 was disputed. The aim of this study was to estimate the relative susceptibility and infectiousness of these flocks in comparison to commercial poultry farms. The relative susceptibility of backyard poultry flocks was estimated to be 1.4% of the susceptibility of a commercial farm (95%CI = 0.00074–0.024). Thus, the probability of infection is much smaller for backyard flocks than for commercial farms. Retrospectively, it is possible to get an intuitive understanding of why the susceptibility of backyard flocks is much lower than the susceptibility of commercial farms. In general, as long as the infection hazard is small and the number of susceptible farms remains approximately constant, the expected number of infected flocks, DSi, can be approximated by DSi = liSiDt. Since l1 = bI1 + bfI2 and l2 = g(bI1 + bfI2), it follows that g = l2/l1, and gˆ ðDS2 =S2 Þ=ðDS1 =S1 Þ in our models with high support (M1–M3). Hence, a crude estimate of the relative susceptibility of backyard flocks is given by the fraction of infected backyard flocks divided by the fraction of infected commercial farms, i.e. gˆ ð12=3601Þ=ð183=984Þ ¼ 0:018. However, in the Dutch epidemic significant depletion of susceptible commercial farms occurred, through infection and culling. This results in an underestimation of the infection hazard on commercial farms in the above reasoning, and an overestimation of the relative susceptibility of backyard flocks. At this point we would like to stress that although the aforementioned line of reasoning provides an intuitive grasp of why backyard flocks are estimated to be much less susceptible to infection than commercial farms, it cannot replace the formal analyses which set limits to the parameters of interest and determine which models have high support. It is tempting to speculate that differences in the susceptibility are related to the fact that commercial farms usually are at least an order of magnitude larger than backyard flocks. However, we consider it unlikely that a simple linear relation exists between the number of animals and the probability of infection. Rather, the probability of infection is most likely determined by a complex combination of determinants as the number of animals, the type of species or breeds present, the number
and type of contacts between flocks, and the sanitary measures that are put in place. The number of backyard flocks was based on information from the Dutch Ministry of Agriculture, but exact numbers were unknown. Changing the ‘true’ number of backyard flocks in the analysis, would, however, not affect the conclusions from the analysis. We made assumptions about the moment of virus introduction and the latent period, as the exact routes remained unclear. A change of the dates, however, did not change the parameter estimations significantly, which means that the model was fairly robust. We also assumed that each flock type (commercial or backyard) was equally infectious or susceptible. However, since no additional information was available, we did not have an alternative. Moreover, it is also questionable whether it would have affected the outcome of the model, as the outcomes were rather distinct. Due to the small number of infected backyard flocks it was not possible to estimate the relative infectiousness of backyard flocks with sufficient precision. However, considering the smaller size of backyard flocks, the general absence of forced ventilation in backyard flocks, and the fact that backyard flocks in the Netherlands probably have fewer contacts with other flocks/farms than commercial farms, one could argue that backyard flocks are expected to be less infectious than commercial farms (see for example Van Nes et al., 1998). On the other hand, it is also conceivable that backyard flocks are in effect more infectious than commercial farms, owing to the fact that animals are more often kept outdoors in backyard flocks, and that certain species regularly kept in backyard flocks (especially ducks and teals) can be infected subclinically (Van der Goot et al., 2007), thereby increasing the effective infectious period. In addition to this, it might be possible that small outbreaks of H7N7 infection have been missed despite the serological sampling at culling. The sample size is not sufficient to detect small outbreaks in flocks larger than 20 heads. It is, however, not likely that an infection with this H7N7 strain would have run a subclinical course in backyard flocks, considering the fact that the most common species in these flocks is chicken and the mortality rates in chicken were very high (Elbers et al., 2004). Overall, our analyses indicate that backyard poultry played a marginal role in the Dutch epidemic of highly pathogenic avian influenza. This is mainly due to the fact that the relative susceptibility of hobby flocks was low and could be estimated with much precision. While the relative infectiousness of hobby flocks could not be estimated with great precision, and indeed the upper limit of the 95% confidence interval allowed backyard flocks to be five times more infectious than commercial farms, this still is not enough to compensate for the very low relative susceptibility of backyard flocks. In fact, if we take the upper limits of 95% confidence intervals of both the relative susceptibility and relative infectiousness of hobby flocks (0.023 and 5.0) and insert both figures in Eq. (6) we get an upper bound of the reproduction number of transmission between hobby flocks of R22 = 0.50. This is substantially below the threshold value of 1 for epidemic spread, and rules out the possibility of sustained transmission among backyard flocks alone.
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In contrast to our results concerning H7N7 in the Netherlands, backyard poultry is considered being an important source of spread and persistence of HPAI H5N1 in for example South East Asia (Tiensin et al., 2005). Explanations for this difference are the differences in animal husbandry systems, bio-security measures, farm density, and between flock contact structures between the Netherlands and Thailand. Moreover, as already mentioned, virus strains, bird species involved, and other factors may differ between countries, but also may be different if a new outbreak occurred in the Netherlands. These differences may complicate extrapolations from our results to other situations or countries. As birds are kept outdoors, backyard flocks may be more at risk for introductions of AI strains of either high or low pathogenicity. The epidemic in The Netherlands in 2003 was controlled by measures such as culling of infected flocks, and pre-emptive depopulation of contiguous flocks. The objectives of pre-emptive culling in general are removal of farms that have been infected but not yet detected, and depletion of farms with susceptible animals in the region (Stegeman et al., 2004; Matthews et al., 2003). It has been shown that pre-emptive culling significantly contributed to the reduction of virus transmission between farms, although it may not be sufficient to efficiently stop virus spread in regions with a high density of poultry flocks (e.g. Stegeman et al., 2004; Boender et al., 2007; Truscott et al., 2007). If, in a future epidemic, backyard flocks appear to be less susceptible than commercial flocks, as shown in our study, preemptive culling might not be necessarily applied to backyard poultry flocks, as the probability of becoming infected appears to be much lower. Taking into account the effort needed to track and cull these backyard flocks, and the risk owners might take to hide their animals, there may be reason to reconsider the pre-emptive culling strategy for backyard flocks that are not clinically affected. However, more data are needed on rural or backyard farms in geographic areas with high poultry density, regarding potentially infectious contacts with commercial farms, spatial distribution and density. Using this type of information during a future epidemic may help policy makers to make decisions on pre-emptive slaughter of rural farms in the case of a HPAI epidemic. Conflict of interest statement The authors do not have commercial or other relationships that might pose a conflict of interest. References Akey, B.L., 2003. Low-pathogenicity H7N2 avian influenza outbreak in Virginia during 2002. Avian Dis. 47, 1099–1103. Alexander, D.J., 2000. A review of avian influenza in different bird species. Vet. Microbiol. 74, 3–13. Alexander, D.J., 2003. Should we change the definition of avian influenza for eradication purposes? Avian Dis. 47, 976–981. Alexander, D.J., 2007. Summary of avian influenza activity in Europe, Asia, Africa, and Australasia, 2002–2006. Avian Dis. 51, 161–166. Boender, G.J., Hagenaars, T.J., Bouma, A., Nodelijk, G., Elbers, A.R.W., De Jong, M.C.M., Van Boven, M., 2007. Risk maps for the spread of highly pathogenic avian influenza in poultry. PLoS Comput. Biol. 3, e71.
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Preventive Veterinary Medicine journal homepage: www.elsevier.com/locate/prevetmed
The role of backyard poultry flocks in the epidemic of highly pathogenic avian influenza virus (H7N7) in the Netherlands in 2003§ V. Bavinck a, A. Bouma a,*, M. van Boven b,1, M.E.H. Bos a, E. Stassen c, J.A. Stegeman a a
Faculty of Veterinary Medicine, Department of Farm Animal Health, Utrecht University, The Netherlands Animal Sciences Group, Wageningen University and Research Centre, Lelystad, The Netherlands c Wageningen University and Research Centre, Wageningen, The Netherlands b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 9 April 2008 Received in revised form 14 October 2008 Accepted 16 October 2008
In recent years, outbreaks of highly pathogenic avian influenza (HPAI) viruses have caused the death of millions of poultry and of more than 200 humans worldwide. A proper understanding of the transmission dynamics and risk factors for epidemic spread of these viruses is key to devising effective control strategies. The aim of this study was to quantify the epidemiological contributions of backyard flocks using data from the H7N7 HPAI epidemic in the Netherlands in 2003. A dataset was constructed in which flocks in the affected area were classified as susceptible (S), infected but not yet infectious (E), infectious (I), and removed (R). The analyses were based on a two-type SEIR epidemic model, with the two types representing commercial poultry farms and backyard poultry flocks. The analyses were aimed at estimation of the susceptibility (g) and infectiousness (f) of backyard flocks relative to commercial farms. The results show that backyard flocks were considerably less susceptible to infection than commercial farms (gˆ ¼ 0:014; 95%CI ¼ 0:0071 0:023), while estimates of the relative infectiousness of backyard flocks varied widely (0 fˆ 5). Our results indicate that, from an epidemiological perspective, backyard flocks played a marginal role in the outbreak of highly pathogenic avian influenza in the Netherlands in 2003. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Avian influenza Backyard poultry Transmission Reproduction number SEIR model
1. Introduction Highly pathogenic avian influenza (HPAI) is a devastating disease for poultry, causing high mortality rates and huge economic damage due to production losses and export bans (Alexander, 2000, 2003; Henzler et al., 2003). Moreover, since the start of outbreaks of subtype H5N1 in South East Asia in 1997, the fear of a human influenza pandemic has
§ This study was financed by the Netherlands Organisation for Scientific Research (NWO). * Corresponding author at: Marburglaan 2, 3584 CN Utrecht, The Netherlands. Tel.: +31 30 2531013; fax: +31 30 2521887. E-mail address: [email protected] (A. Bouma). 1 Current address: Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
0167-5877/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.prevetmed.2008.10.007
arisen (e.g. Webby and Webster, 2003). Although the focus is currently on HPAI H5N1, outbreaks of other subtypes, mainly H7, have occurred in recent years (Alexander, 2007), for example in Italy (Capua et al., 2003), the Americas (OIE, 2003; Max et al., 2007), the Netherlands (Stegeman et al., 2004) and South Africa (OIE, 2003; Bowes et al., 2004). Outbreaks of HPAI are usually controlled by measures such as culling of infected flocks to reduce virus output and pre-emptive depopulation of contiguous flocks, as for example during the H7N7 epidemic in The Netherlands in 2003 (Stegeman et al., 2004). This epidemic was successfully controlled, but only after the killing of 30 million birds and infection of 89 humans (Koopmans et al., 2004; Fouchier et al., 2004). The measures were applied to commercial holdings as well as to non-commercial backyard poultry flocks, which are generally assumed to be at risk for AI virus introduction from migratory birds or spill
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over from contiguous commercial flocks as hypothesized for example for Europe (e.g. Stegeman et al., 2004; Gilbert et al., 2006; Terregino et al., 2007), Nigeria (Ducatez et al., 2006), and Indonesia (Sedyaningsih et al., 2007). Public opinion, however, turned against the massive pre-emptive culling of often healthy-poultry (Thomas et al., 2005). Especially the owners of small backyard flocks did not consider their poultry an important factor in the spread of HPAI virus and were strongly opposed preemptive culling of their flocks, which they considered to be in the interest of the commercial poultry sector only. They argued that because the size of backyard flocks was much smaller than commercial flocks the risk of virus introduction would also be much smaller (Refregier-Petton et al., 2001; Akey, 2003). Moreover, they mentioned that the housing systems of backyard poultry often did not have a forced ventilation system, and that there would be hardly any contact with commercial holdings (Thomas et al., 2005). Some even argued that backyard poultry would be intrinsically less susceptible than commercial poultry as the backyard flocks and commercial farms generally contain different breeds. In contrast to the Dutch situation, backyard poultry is considered being an important source of spread and persistence of HPAI H5N1 in, for example, South East Asia. In Thailand, for instance, 83% of the total outbreaks concerned backyard chickens and free-grazing ducks (Tiensin et al., 2005). As the animal husbandry systems differ substantially between countries, the question is whether this may also be valid for the Netherlands or other European countries. During the Dutch H7N7 epidemic in 2003 unique data were collected for epidemiological studies about evaluation of control measures. So far, the analyses were restricted to data from commercial holdings (Stegeman et al., 2004; Thomas et al., 2005; Boender et al., 2007), whereas poultry on many premises were held in backyards. To gain more insight in the epidemiology of HPAI and to improve the control strategy for a future epidemic we provide a retrospective analysis of the epidemic of HPAI H7N7 in the Netherlands in 2003, with the goal to determine the actual role of backyard flocks in that epidemic. We carried out statistical analyses based on a stochastic epidemic model, which yield quantitative estimates of epidemiological parameters such as the transmissibility of HPAI virus, and the relative susceptibility and infectiousness of backyard flocks. 2. Methods 2.1. Study population In the Netherlands, backyard hobby flocks were mainly kept outdoors in a confined space. The Dutch Ministry of Agriculture, Nature and Food Quality (LNV) defined a flock as hobby or backyard flock, when they consisted of fewer than 500 birds or did not have a unique farm number (LNV, 2003). The first outbreak with the H7N7 strain occurred in the central part of the Netherlands (the Gelderse Vallei), and later, the virus had spread to the province of Limburg (Stegeman et al., 2004). In the Gelderse Vallei H7N7 HPAI
virus was isolated from samples collected on 183 commercial and 12 backyard flocks. In Limburg, virus was detected on 32 commercial flocks and 2 backyard flocks (Stegeman et al., 2004). The current study was based on data obtained from outbreaks in the Gelderse Vallei only. The total number of commercial farms in this area was 984, and the number of backyard flocks was estimated to be 3601 (LNV, 2003). Data from Limburg were not used as the number of infected flocks was too low to obtain estimates with reasonable precision. Premises were defined as having been infected if HPAI virus strain H7N7 was isolated by RT-PCR on trachea or cloacal swabs taken from birds with signs of AI (OIE, 2003; Stegeman et al., 2004). The days of notification and depopulation of all infected flocks were recorded; the depopulation day only was recorded for all pre-emptively culled flocks (LNV, 2003; see also Stegeman et al., 2004; Thomas et al., 2005). All pre-emptively culled commercial flocks were tested either by PCR on tracheal swabs (if signs of AI were noticed) or, if no AI-like signs were seen, serologically by testing 20 samples per farm. Trachea swabs were taken from clinically affected birds; serum samples were taken from randomly selected birds in a shed. Sample size was based on the Council Directive 92/ 40/EEC (1992): if a flock was smaller than 20 heads, all birds were supposed to be sampled; if a flock was larger, 20 birds per flock should be sampled. If the tests on these samples were negative, the flocks were considered not infected (Thomas et al., 2005). The records were used to construct a working dataset with daily numbers of notifications, infected flock depopulations and uninfected flock depopulations. 2.2. Data Estimation of the transmission parameter required knowledge of the number of susceptible and infectious flocks over time, and the number of new cases (infected flocks) per period. A dataset was constructed in which flocks were classified as susceptible (S), infected but not yet infectious (E), infectious (I), and removed (R). In the analyses presented here flocks were assumed identical in all other aspects. We assumed that, from the moment of introduction of the virus, a flock would have been latently infected for about 2 days (Savill et al., 2006; Van der Goot et al., 2007) and then become infectious after which the infection would have been detected due to occurrence of clinical signs or increased mortality. The infectious period ended at the day of depopulation. A different length of the infectious period was estimated for the first five infected flocks, because the day of notification was most likely later than it would have been during the epidemic, due to poor recognition of AI at the start of the epidemic (Elbers et al., 2004). Based on these estimates a dataset was constructed in which farms were assumed to have been infected 8–12 days (first five infected farms) or 4 days (remaining cases) before notification (Stegeman et al., 2004). The number of susceptible farms during the epidemic decreased over time due to infection and culling. We refer Stegeman et al. (2004) and Boender et al. (2007)
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Table 1 Overview of the model scenarios. The full model containing four parameters (bij, i, j = 1, 2) is labeled scenario M0. A number of simplified scenarios are also considered based on M1 (see Table for description of the various scenarios). Model scenario
Estimated parameters
Description
M0 M1 M2 M3 M4 M5
b11, b12, b21, b22 b, f, g b, g b, g b, f b
M6
b
Full model Simplified model assuming proportionate mixing (default scenario) As M1 but assuming that backyard flocks are not infectious (f = 0) As M1 but assuming that commercial and backyard flocks are equally As M1 but assuming that commercial and backyard flocks are equally As M1 but assuming that commercial and backyard farms are equally and that back yard farms are not infectious (f = 0, g = 1) As M1 but assuming that commercial and backyard farms are equally and equally infectious (f = 1, g = 1)
for details on the construction of the dataset. Table 1 gives an overview of all model scenarios.
infectious (f = 1) susceptible (g = 1) susceptible, susceptible
susceptibility of type j farms, respectively (Diekmann and Heesterbeek, 2000). For our purposes the set of simplified models is conveniently parameterized as follows:
2.3. Epidemic model
b11 ¼ b; The analyses were based on a two-type SEIR epidemic model, with the two types representing commercial flocks (type 1) and backyard flocks (type 2). The forces of infection l1(t) and l2(t), which represent the infection hazards on type 1 and type 2 susceptible holdings, hold centre stage in the analyses, and are given by weighted sums of the number of infectious commercial and backyard farms, i.e.
l j ðtÞ ¼ b1 j I1 ðtÞ þ b2 j I2 ðtÞ
(1)
(j = 1, 2). In Eq. (1), I1(t) and I2(t) represent the numbers of infectious type 1 and type 2 farms at time t, and bij (i, j = 1, 2) are the between flock transmission rate parameters (Diekmann and Heesterbeek, 2006). By standard reasoning, the probabilities of infection in a small time interval (t, t + Dt] are given by p j ðtÞ ¼ 1 el j ðtÞDt
(2)
(j = 1, 2). Now, given that there are Sj(t) susceptible farms of type j at time t, the probabilities qj(t) of observing Cj(t, t + Dt) new infections in the interval (t, t + Dt] are binomially distributed with parameters pj(t) and binomial totals Sj(t): S j ðtÞ q j ðtÞ ¼ (3) p j ðtÞC j ðtÞ ð1 p j ðtÞÞðS j ðtÞC j ðtÞÞ C j ðtÞ (j = 1, 2). The likelihood of the data is given by the product of the probabilities of infection over the different types of farms and over all observation intervals. The log-likelihood, which is computationally more convenient to work with than the likelihood, is given by ‘¼
t last X
ðlog q1 ðtÞ þ log q2 ðtÞÞ;
(4)
t¼t first
where the summation runs across all observation intervals. In all our analyses we have taken Dt = 1 day. The model formulation in Eq. (1) puts no constraints on the four transmission parameters bij (i, j = 1, 2). In the following we will focus in detail on a suite of simplified models that are based on the assumption of proportionate mixing, i.e. which assumes that the transmission parameters can be written in the form bij = aibj, where ai and bj represent the (absolute) infectiousness of type i and (absolute)
b12 ¼ bg;
b21 ¼ b f ;
b22 ¼ b fg:
(5)
In Eqs. (1)–(5) the parameter b represents the transmission rate parameter determining transmission between commercial farms, and the parameters f and g represent the infectiousness and susceptibility of backyard farms relative to that of commercial farms. Hence, the transmission hazard in a population of commercial farms only is given by l1(t) = bI1(t), and it is given by l2(t) = bfgI2(t) in a population of backyard flocks only. The goal is to obtain estimates of the three parameters b, f, and g. Notice that the full model contains four parameters, while the simplified model specified by Eq. (5) contains three parameters. 2.4. Statistical analysis The maximum likelihood estimates of the parameters of interest are readily obtained by maximization of the loglikelihood in Eq. (4). Confidence intervals are calculated on the basis of profile likelihoods. To be able to compare and choose between models of different complexity we have made use of Akaike’s Information Criterion (Burnham and Anderson, 2002). Mathematica 6.0 was used for the data processing and statistical analyses. 2.5. Sensitivity analyses In the following the full model containing four parameters (bij, i, j = 1, 2) is labeled scenario M0. In our default scenario containing three parameters (labeled M1) we assumed proportionate mixing, and estimated three parameters (b, f, and g). To investigate the robustness of the results obtained using scenarios M0–M1, and to be able to determine the optimal model complexity, we considered a number of simplified scenarios based on M1, but with f = 0 (backyard farms are not infectious at all) (scenario M2), with f = 1 (commercial and backyard farms are equally infectious) (scenario M3), with g = 1 (commercial and backyard farms are equally susceptible) (scenario M4), with f = 0 and g = 1 (commercial and backyard farms are equally susceptible but backyard farms are not infectious) (scenario M5), and with f =g = 1 (commercial and backyard farms are equally susceptible and infectious) (scenario M6). Table 1 gives an overview of the different model scenarios.
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In addition to the model scenarios M0–M6, we assumed in our default scenario (M1) that moment of infection was 12 days before notification for the first five cases, and 7 days before notification of increased mortality or signs of AI for all other cases (Stegeman et al., 2004). A sensitivity analysis with respect to this assumption was performed by back-calculation of the moment of virus introduction based on within-flock mortality (Bos et al., 2007).
3. Results 3.1. Descriptive statistics During and after the epidemic, 30 million birds were killed of which approximately 180,000 were kept in backyard flocks. The number of poultry in backyard flocks varied between 4 and 99 birds (LNV, 2003). Fig. 1 shows a map of the Gelderse Vallei and the approximate location of
Fig. 1. . Location of commercial and backyard poultry premises in the Gelderse Vallei during the H7N7 HPAI epidemic in the Netherlands in 2003. Grey dots: uninfected commercial farms; black dots: infected commercial farms; red dots: infected backyard poultry (kindly provided by Dr. Gert-Jan Boender, Animal Sciences Group, Lelystad, The Netherlands).
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all commercial farms (n = 984), all infected commercial farms (n = 183), and all infected backyard flocks (n = 12) in the area. All premises were defined as being infected when HPAI virus strain H7N7 was isolated by RT-PCR. Six of the infected backyard flocks contained different chicken breeds and/or different bird species, such as ducks, ostriches, peacocks, pheasants, swans or geese. Ten backyard poultry keepers reported a sudden increase of mortality, in nine cases mortality concerned chicken only (LNV, 2003). All infected backyard flocks were located within close vicinity of infected commercial flocks. As for all infected flocks, back tracing of contacts between infected flocks was carried out to find a possible virus source, according to the regulations of the European Union (Council Directive 92/40/EEC, 1992). Only one contact (a co-worker) between a commercial farm and a backyard poultry flock was found, and it could not be demonstrated whether this contact was really the source of the introduction. No other specified virus transmission routes were found. 3.2. Parameter estimates ˆ (i, j = 1, 2) of the full model The parameter estimates b ij M0 containing four parameters had a lower support than either of the models M1–M3 that assume proportionate mixing and in which at least the parameter b describing transmission between commercial farms and the parameter g describing the relative susceptibility of backyard farms are estimated (Table 2). Therefore, we will focus in the following on models that assume proportionate mixing and contain two or three parameters. Table 2 shows the parameter estimates of the models. Model M2 in which backyard farms were assumed to be non-infectious (f = 0) had the highest support, although models M1 and M3 (f = 1) also had high support. All these models yielded consistently low estimates of the relative susceptibility of backyard flocks relative to commercial farms. In fact, in all these models g was estimated to be 0.013–0.014, and all three models yielded 95% confidence intervals ranging from 0.007 to 0.023. This indicates that backyard flocks are less susceptible to infection than commercial farms (since g < 1). With regard to the relative infectiousness of backyard flocks not much can be said. Model scenario M1 yielded an estimate of the relative infectiousness of fˆ ¼ 0:37, but with a broad confidence interval (95%CI = 0–5.0). In addition,
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both models scenarios M2 and M3 which assumed either f = 0 or f = 1 had higher support than model M1, indicating that the data did not allow precise estimates of the parameter f to be made. Finally, the remaining models (M4–M6) in which commercial and backyard flocks were assumed to be equally susceptible (g = 1) had very low overall support, and can safely be excluded from consideration. Apparently, these models are unable to give a satisfactory description of the data. Estimates of the transmission parameter b were similar for all model scenarios M0–M3 that had high support, and show that the rate at which an infected commercial farm infects a specific susceptible farm in a population consisting almost exclusively of susceptible farms is about 1.7 104 day1 farm1. The estimated total between-farm transmission rate is given by the ˆ and the total number of commercial farms, product of b i.e. 1.7 104 984 = 0.17 day1. This implies that an infected commercial farm is expected to infect, on average, 0.17 other commercial farms per day over the course of the epidemic. Likewise, if we denote by S1 and S2 the total number of commercial and backyard flocks, an infected commercial ˆ gS ˆ 2 ¼ 1:7 farm is expected to infect on average of b 104 0:014 3601 ¼ 8:6 103 backyard flocks per day in the early stages of the epidemic. In the default analysis, the mean infectious periods of commercial and backyard flocks were Tˆ1 ¼ 7:8 day and Tˆ2 ¼ 7:1 day, respectively (data not shown). Hence, it follows that the expected total number of type j infections caused by a type i infected flock, Rˆ i j , is given by ! bˆ Tˆ1 S1 bˆ gˆTˆ1 S2 Rˆ 11 Rˆ 12 ¼ Rˆ 21 Rˆ 22 bˆ fˆTˆ2 S1 bˆ fˆgˆTˆ2 S2 1:3 0:067 ¼ : (6) 0:44 0:023 An estimate of the overall reproduction number R is given by the largest eigenvalue of the above matrix of individual reproduction numbers. For the parameter estimates of Eq. (6) (which are based on model M1) this yields Rˆ ¼ 1:33. It should be noted that the above analysis neglects the fact that, during the Dutch epidemic, there were substantial differences in the transmission rates and infectious periods in the early versus late stages of the
Table 2 Estimates of the parameters b (between flock transmission rate), fˆ (infectiousness), and gˆ (susceptibility) for H7N7 avian influenza in commercial and backyard flocks in various model scenarios. Model scenario
Parameter estimates (95%CI)
bˆ ðday1 farm1 Þ M0 M1 M2 M3 M4 M5 M6 a b c
(full model) (default scenario) (f = 0) (f = 1) (g = 1) (f = 0, g = 1) (f = g = 1)
1.7 104 1.7 104 1.7 104 1.7 104 3.1 105 3.1 105 3.0 105
ˆ ¼b ˆ ) (1.3 104–2.0 104) (b 11 (1.3 104–2.0 104) (1.5 104–2.0 104) (1.4 104–1.9 104) (2.7 105–3.6 105) (2.7 105–3.6 105) (2.6 105–3.4 105)
AIC = Akaike’s Information Criterion (AIC). Corresponding model supports. NA: not applicable.
fˆ
gˆ
NAc 0.37 (0–5.0) – – 0 (0–1.3) – –
NA 0.014 (0.0071–0.023) 0.014 (0.0071–0.023) 0.013 (0.0071–0.023) – – –
AICa
Supportb
288.15 287.36 285.44 285.52 843.84 841.84 844.79
0.10 0.15 0.38 0.37 0 0 0
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epidemic (Stegeman et al., 2004). As a consequence, Eq. (6) is better suited to get an impression of the relative contributions of backyard flocks versus commercial farms than to get a reliable estimate of the absolute magnitude of the reproduction numbers of commercial farms and backyard flocks in the early stage of the epidemic. The sensitivity analysis with respect to changes in day of introduction into the flocks yielded results that were ˆ ¼ 1:0 104 ð95%CI ¼ 1:0 similar to those in Table 2 ðb 104 1:4 104 Þ; fˆ ¼ 0ð95%CI ¼ 01:5Þ; gˆ ¼ 0:014ð95%CI ¼ 0:000740:024ÞÞ: 4. Discussion Backyard poultry is considered a risk for becoming infected with AI virus (e.g. Sedyaningsih et al., 2007; Gilbert et al., 2006; Ducatez et al., 2006; Saad et al., 2007; Gall-Recule´ et al., 2008; Lee et al., 2008), but their role in the spread of the virus during the epidemic in the Netherlands in 2003 was disputed. The aim of this study was to estimate the relative susceptibility and infectiousness of these flocks in comparison to commercial poultry farms. The relative susceptibility of backyard poultry flocks was estimated to be 1.4% of the susceptibility of a commercial farm (95%CI = 0.00074–0.024). Thus, the probability of infection is much smaller for backyard flocks than for commercial farms. Retrospectively, it is possible to get an intuitive understanding of why the susceptibility of backyard flocks is much lower than the susceptibility of commercial farms. In general, as long as the infection hazard is small and the number of susceptible farms remains approximately constant, the expected number of infected flocks, DSi, can be approximated by DSi = liSiDt. Since l1 = bI1 + bfI2 and l2 = g(bI1 + bfI2), it follows that g = l2/l1, and gˆ ðDS2 =S2 Þ=ðDS1 =S1 Þ in our models with high support (M1–M3). Hence, a crude estimate of the relative susceptibility of backyard flocks is given by the fraction of infected backyard flocks divided by the fraction of infected commercial farms, i.e. gˆ ð12=3601Þ=ð183=984Þ ¼ 0:018. However, in the Dutch epidemic significant depletion of susceptible commercial farms occurred, through infection and culling. This results in an underestimation of the infection hazard on commercial farms in the above reasoning, and an overestimation of the relative susceptibility of backyard flocks. At this point we would like to stress that although the aforementioned line of reasoning provides an intuitive grasp of why backyard flocks are estimated to be much less susceptible to infection than commercial farms, it cannot replace the formal analyses which set limits to the parameters of interest and determine which models have high support. It is tempting to speculate that differences in the susceptibility are related to the fact that commercial farms usually are at least an order of magnitude larger than backyard flocks. However, we consider it unlikely that a simple linear relation exists between the number of animals and the probability of infection. Rather, the probability of infection is most likely determined by a complex combination of determinants as the number of animals, the type of species or breeds present, the number
and type of contacts between flocks, and the sanitary measures that are put in place. The number of backyard flocks was based on information from the Dutch Ministry of Agriculture, but exact numbers were unknown. Changing the ‘true’ number of backyard flocks in the analysis, would, however, not affect the conclusions from the analysis. We made assumptions about the moment of virus introduction and the latent period, as the exact routes remained unclear. A change of the dates, however, did not change the parameter estimations significantly, which means that the model was fairly robust. We also assumed that each flock type (commercial or backyard) was equally infectious or susceptible. However, since no additional information was available, we did not have an alternative. Moreover, it is also questionable whether it would have affected the outcome of the model, as the outcomes were rather distinct. Due to the small number of infected backyard flocks it was not possible to estimate the relative infectiousness of backyard flocks with sufficient precision. However, considering the smaller size of backyard flocks, the general absence of forced ventilation in backyard flocks, and the fact that backyard flocks in the Netherlands probably have fewer contacts with other flocks/farms than commercial farms, one could argue that backyard flocks are expected to be less infectious than commercial farms (see for example Van Nes et al., 1998). On the other hand, it is also conceivable that backyard flocks are in effect more infectious than commercial farms, owing to the fact that animals are more often kept outdoors in backyard flocks, and that certain species regularly kept in backyard flocks (especially ducks and teals) can be infected subclinically (Van der Goot et al., 2007), thereby increasing the effective infectious period. In addition to this, it might be possible that small outbreaks of H7N7 infection have been missed despite the serological sampling at culling. The sample size is not sufficient to detect small outbreaks in flocks larger than 20 heads. It is, however, not likely that an infection with this H7N7 strain would have run a subclinical course in backyard flocks, considering the fact that the most common species in these flocks is chicken and the mortality rates in chicken were very high (Elbers et al., 2004). Overall, our analyses indicate that backyard poultry played a marginal role in the Dutch epidemic of highly pathogenic avian influenza. This is mainly due to the fact that the relative susceptibility of hobby flocks was low and could be estimated with much precision. While the relative infectiousness of hobby flocks could not be estimated with great precision, and indeed the upper limit of the 95% confidence interval allowed backyard flocks to be five times more infectious than commercial farms, this still is not enough to compensate for the very low relative susceptibility of backyard flocks. In fact, if we take the upper limits of 95% confidence intervals of both the relative susceptibility and relative infectiousness of hobby flocks (0.023 and 5.0) and insert both figures in Eq. (6) we get an upper bound of the reproduction number of transmission between hobby flocks of R22 = 0.50. This is substantially below the threshold value of 1 for epidemic spread, and rules out the possibility of sustained transmission among backyard flocks alone.
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In contrast to our results concerning H7N7 in the Netherlands, backyard poultry is considered being an important source of spread and persistence of HPAI H5N1 in for example South East Asia (Tiensin et al., 2005). Explanations for this difference are the differences in animal husbandry systems, bio-security measures, farm density, and between flock contact structures between the Netherlands and Thailand. Moreover, as already mentioned, virus strains, bird species involved, and other factors may differ between countries, but also may be different if a new outbreak occurred in the Netherlands. These differences may complicate extrapolations from our results to other situations or countries. As birds are kept outdoors, backyard flocks may be more at risk for introductions of AI strains of either high or low pathogenicity. The epidemic in The Netherlands in 2003 was controlled by measures such as culling of infected flocks, and pre-emptive depopulation of contiguous flocks. The objectives of pre-emptive culling in general are removal of farms that have been infected but not yet detected, and depletion of farms with susceptible animals in the region (Stegeman et al., 2004; Matthews et al., 2003). It has been shown that pre-emptive culling significantly contributed to the reduction of virus transmission between farms, although it may not be sufficient to efficiently stop virus spread in regions with a high density of poultry flocks (e.g. Stegeman et al., 2004; Boender et al., 2007; Truscott et al., 2007). If, in a future epidemic, backyard flocks appear to be less susceptible than commercial flocks, as shown in our study, preemptive culling might not be necessarily applied to backyard poultry flocks, as the probability of becoming infected appears to be much lower. Taking into account the effort needed to track and cull these backyard flocks, and the risk owners might take to hide their animals, there may be reason to reconsider the pre-emptive culling strategy for backyard flocks that are not clinically affected. However, more data are needed on rural or backyard farms in geographic areas with high poultry density, regarding potentially infectious contacts with commercial farms, spatial distribution and density. Using this type of information during a future epidemic may help policy makers to make decisions on pre-emptive slaughter of rural farms in the case of a HPAI epidemic. Conflict of interest statement The authors do not have commercial or other relationships that might pose a conflict of interest. References Akey, B.L., 2003. Low-pathogenicity H7N2 avian influenza outbreak in Virginia during 2002. Avian Dis. 47, 1099–1103. Alexander, D.J., 2000. A review of avian influenza in different bird species. Vet. Microbiol. 74, 3–13. Alexander, D.J., 2003. Should we change the definition of avian influenza for eradication purposes? Avian Dis. 47, 976–981. Alexander, D.J., 2007. Summary of avian influenza activity in Europe, Asia, Africa, and Australasia, 2002–2006. Avian Dis. 51, 161–166. Boender, G.J., Hagenaars, T.J., Bouma, A., Nodelijk, G., Elbers, A.R.W., De Jong, M.C.M., Van Boven, M., 2007. Risk maps for the spread of highly pathogenic avian influenza in poultry. PLoS Comput. Biol. 3, e71.
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