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Comparison of the epidemiology of epizootic haemorrhagic disease and bluetongue viruses in dairy cattle in Israel
The Veterinary Journal 190 (2011) 77–83
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Comparison of the epidemiology of epizootic haemorrhagic disease and bluetongue viruses in dairy cattle in Israel Maor Kedmi a,b, Nadav Galon b, Yael Herziger a, Hagai Yadin c, Velizar Bombarov c, Carrie Batten d, Nahum Y. Shpigel a, Eyal Klement a,⇑ a
Koret School of Veterinary Medicine, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel Hachaklait, Mutual Society for Veterinary Services, Caesarea Industrial, Israel Department of Virology, Kimron Veterinary Institute, Bet Dagan, Israel d Institute for Animal Health, Ash Road, Pirbright, UK b c
a r t i c l e
i n f o
Article history: Accepted 17 October 2010
Keywords: Epizootic haemorrhagic disease Bluetongue virus Cattle Spatial analysis Endemic Epidemic
a b s t r a c t An outbreak of epizootic haemorrhagic disease virus (EHDV) in cattle in Israel in 2006 enabled a comparison of the spatial distribution of epidemic exposure to EHDV with that of exposure to bluetongue virus (BTV), which is endemic in the country. The seroprevalence of both viruses was examined in 1650 serum samples collected from 139 farms representative of the spatial distribution of dairy cattle in Israel. A significant association between exposure to EHDV and BTV was demonstrated in both univariate and multivariate analyses. Recent exposure to BTV and EHDV (demonstrated by seroprevalence in calves) was clustered in different geographical locations, indicating that the two viruses had different patterns of spread, that of EHDV being influenced by winds and terrain barriers and that of BTV by herd immunity. Ó 2010 Elsevier Ltd. All rights reserved.
Introduction Epizootic haemorrhagic disease virus (EHDV) and bluetongue virus (BTV) are viruses in the genus Orbivirus that are transmitted by several species of midge (genus Culicoides). Since the 1990s, there have been several incursions of BTV serotypes into new locations in Europe (Purse et al., 2005). In 2006, cattle-virulent EHDV appeared in Israel (Yadin et al., 2008), Morocco and Algeria,1 and entered Turkey in 2007 (Temizel et al., 2009). Environmental correlates of Culicoides spp. habitats have been used to predict the abundance of BTV and EHDV (Baylis and Rawlings, 1998; Baylis et al., 1999, 2001; Tatem et al., 2003; Purse et al., 2004a; Guis et al., 2007). The distributions of both viruses are often studied together (Stallknecht et al., 1991, 1995, 1996). Thus, data on the distribution of BTV could be used to assess the risk of exposure to EHDV and vice versa. Along with the expected similarities between the distributions of EHDV and BTV, variation may exist due to factors associated with their respective epidemic and endemic states. When virus incursion into a new region occurs in the autumn (as occurred during the 2006 EHDV outbreak in Israel), the attack rate in locations ⇑ Corresponding author. Tel.: +972 8 9489560; fax: +972 8 9489138. E-mail address: [email protected] (E. Klement). PROMED archive numbers 20061010.2906, 20061214.3513; http:// www.promedmail.org.
affected at the onset of the outbreak may be higher than in locations affected during the later stages. This is because environmental conditions change with the approach of winter and become less favourable for the vector. Outbreaks may also occur as a part of the endemic cycle following a reduction in herd immunity. The abundance of seropositive immune animals has a major influence on the spatial distribution of new cases of the endemic disease and may play a significant role in determining the appearance of morbidity in certain regions. For example, areas in West Virginia, USA, that were characterised by low EHDV seroprevalence in white tailed deer suffered more severe outbreaks of epizootic haemorrhagic disease (EHD) (Gaydos et al., 2004). Flacke et al. (2004) studied the distribution of endemic and epidemic EHDV in two regions that differed in environmental conditions. However, in order to understand how the distributions of EHDV and BTV differ in endemic and epidemic situations, comparisons should be made in the same region and at the same time, so that environmental conditions are the same and the main difference is in herd immunity to each virus. An epidemic of cattle-virulent EHDV occurred in Israel in the summer and autumn of 2006. While sheep were not affected during this epidemic (Kedmi et al., 2010a), it caused significant production losses to the dairy cattle industry, estimated at US$2.5 million (Kedmi et al., 2010b).2 As part of the outbreak investigation,
1
1090-0233/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tvjl.2010.10.003
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US$1 = approximately £0.63, €0.72, as at 17th October 2010.
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we conducted a serological survey to describe the spatial distribution of emerging EHDV and of BTV. BTV serotypes 2, 4, 6, 10 and 16 have been previously identified in Israel and serotypes 5, 8, 15 and 24 have been diagnosed recently. Among these serotypes, BTV-4 is the most dominant and the only one that appears consistently (Barzilai and Shimshony, 1985). We therefore consider BTV in this study to be an endemic virus, assuming that seroprevalence of BTV represents exposure to BTV-4. Culicoides imicola is a competent vector for EHDV (Paweska et al., 2005) and is the main vector of BTV in Israel (Braverman et al., 1985). Taken together with its high abundance, there is a high probability that C. imicola is the vector for EHDV in Israel. Other potential vectors of both viruses are Culicoides spp. of the schultzei group, since EHDV has been isolated from C. kingi (Mellor et al., 1984), a member of this group that is also abundant in Israel. The outbreak of cattle-virulent EHDV in Israel in 2006 provided an opportunity to compare the spatial distributions of emerging EHDV and endemic BTV. It also enabled us to assess whether exposure to an endemic virus, such as BTV, can be used to predict the spread of an emerging virus, such as EHDV, in Israel.
Materials and methods Outbreak data set Data collected during a large EHDV outbreak in Israel during the summer and autumn of 2006 were analysed. The first affected herd was reported on 28 August 2006. Infection spread from this herd southward towards the Dead Sea and northward along the Rift Valley to the northern border of Israel (Fig. 1). The virus then spread from the primary outbreak region westward through the valleys and into the coastal plain. Overall, the disease was reported in 83 dairy herds from 78 localities and from 22 beef herds. Newly affected herds continued to be reported for 13 weeks, until the third week of November 2006. The herd case definition for diagnosing EHD was as follows: conjunctivitis and/or salivation and/or stiff gait and/or congested mucous membranes in two or more cows, accompanied by a sudden reduction in milk production in several cows in the herd. Sample collection Serum samples suitable for inclusion in the serological survey were collected from 66 EHD-clinically affected dairy cattle herds at least one month after the onset of clinical signs in each herd. In each herd, veterinarians collected serum samples at random from five calves aged 6–12 months, five apparently healthy cows (age >1.5 years), five cows that exhibited clinical signs at the time of collection and five cows that had begun showing clinical signs (including reduction in milk yield) at
Fig. 1. Spatial distribution of sampled herds superimposed on a kernel density map depicting the quantitative distribution of dairy cows in Israel (the kernel smoothed density map was obtained by calculating cow density per square km with a search radius of 10 km). Black dots, affected herds (n = 66) (report of typical clinical disease during the outbreak); white dots, unaffected herds (n = 73). Solid arrows, herds affected during the first week of the outbreak (28th August–1st September 2006). Dashed arrow, the last herd affected (17th November 2006).
M. Kedmi et al. / The Veterinary Journal 190 (2011) 77–83 least 2 weeks before blood sampling. To serve as control herds, 73 unaffected dairy herds were randomly selected from the list of cooperative (Hachaklait) herds in Israel. In these herds, serum samples were collected only from calves and healthy cows. No clinical signs of bluetongue were observed in case or control herds. The probability of missing infection (u) was calculated according to the formula u = (1 P)n, where P is the seroprevalence and n is the number of sampled cows. With this sampling strategy, the probability of missing infection in a herd in which 20% of animals were seropositive was 10%, whereas for a herd in which 30% of animals were seropositive, this probability was <3%. Overall, 1650 serum samples were collected and tested serologically for exposure to BTV and EHDV.
Serological assays A competitive ELISA (c-ELISA) similar to an assay previously described by Thevasagayam et al. (1995) was used to detect serum antibodies against EHDV and BTV. This ELISA is able to detect exposure to all serotypes of EHDV and does not cross-react with BTV. The specificity and sensitivity of this assay were both 100% compared to the agar gel immunodiffusion test (Thevasagayam et al., 1996). EHDV-1, purified as described by Thevasagayam et al. (1995), was added to 96-well plates at a dilution of 1:250 and incubated at 37 °C for 1 h on an orbital shaker. The plates were washed three times prior to loading of the test serum samples, control sera and the competing EHDV VP7-specific monoclonal antibody at a dilution of 1:100. Four wells were loaded with all reagents but no serum and were regarded as zero-competition controls (C0). After incubation for 1 h at 37 °C, the conjugate (rabbit anti-mouse horseradish peroxidase, Dako; 1:1000) was added and the plate was incubated at 37 °C for a further 1 h. The substrate (TMB single solution, Zymed) was added, incubated for 15 min and then the reaction was stopped by addition of 1 M H2SO4. The plates were read at 405 nm (ELISA reader Sunrise, XFLUOR4 V4.51). Optical density (OD) values were converted to percentage inhibition (PI) values as follows: 100 [(OD of each test or control value)/(median OD of the 4 C0 wells)] 100. PI values P50% were considered to be positive. A commercial c-ELISA (Bluetongue Virus Antibody Test Kit, c-ELISA, VMRD) was used for detection of anti-BTV antibodies according to the manufacturer’s instructions.
Univariate analysis and spatial analysis of serological data The seroprevalence of BTV and EHDV was determined for each herd and for each group of cows and calves within a herd. The association between the seroprevalence of BTV and EHDV was assessed using a generalised estimating equation (GEE) regression model, which included the type of animal (calf, healthy cow, currently ill cow and cow affected at least two weeks before blood collection), the type of herd (clinically affected or not affected) and the interaction between them. The herd was included as a subject variable and an exchangeable covariance structure was used as the working matrix (i.e. correlation between serological measurements within the herds but not between the herds). The marginal means of seroprevalence and their 95% confidence intervals calculated from this model are reported. P values were calculated by running this model separately for each group.
100
Seroprevalence (%)
80
60
79
Spatial autocorrelation of EHDV and BTV seroprevalence in cows and calves was examined by calculation of Moran’s I statistic. This enabled assessment of the importance of location in exposure to the virus. Spatial weighting of herds used the K-nearest neighbours weighting method, with the number of neighbouring herds (K) set to 6. To overcome variance instability due to differences in the numbers of sampled animals in each herd, an adjustment procedure was applied that makes use of a variable transformation based on the empirical Bayes (EB) principle (Assuncao and Reis, 1999). This was referred to as the EB-corrected Moran’s I. Cluster analysis was then performed for these data by calculating the local Moran’s indicator for the spatial autocorrelation (LISA) statistic (Anselin, 1995). A lagged seroprevalence variable (i.e. a variable that estimates the seroprevalence in each herd from the spatially weighted seroprevalence of the herds that surround it) was computed for each herd. Positive clusters were defined where both standardised seroprevalence and standardised spatially lagged seroprevalence were positive. Only statistically significant clusters (P < 0.05) were mapped. A second cluster analysis was performed using SatScan software, with the Bernoulli model for case-control data (Kulldorf, 1997, 2006). For this analysis, individual cows/calves seropositive for BTV/EHDV were regarded as cases and negative animals as controls. In this iterative method, the ratio of cases and controls in and out of a scanning elliptic window was compared. Only ellipses that contained no more than 50% of the animals were examined. The window with the maximum likelihood was considered to be the most likely cluster, i.e. the cluster least likely to occur due to chance. The P value was then calculated through Monte Carlo hypothesis testing by comparing the rank (R) of the maximum likelihood from the real dataset with the maximum likelihoods from 999 random datasets using the following equation: P = R/(1 + number of simulations). Map construction and calculation of herd kernel density were performed using Geostatistical analyst and spatial analyst extensions and by the spatial statistics tools in ArcGis 9.3. Spatially based weighting and calculation of the LISA statistic and the global Moran’s I statistic were performed using Geoda software. Multivariate analysis of serological data Serological results for 1096 serum samples collected from calves and healthy cows, using individual cow/calf as the unit of interest, were used for the analysis of risk factors associated with EHDV seroprevalence, since these were the only groups sampled in both clinically infected and control herds. A GEE regression model with Logit as the link function and an exchangeable covariance structure for the working matrix was constructed to examine the association of EHDV seroprevalence with factors influencing the time of infection (e.g. distance from location of onset of outbreak) and with BTV seroprevalence. Variables included in the model were: (1) age group (i.e. calf or cow), to account for possible previous exposure to EHDV; (2) BTV seroprevalence in cows at the herd level; (3) geographical location (within or outside the Rift Valley); (4) distance from the location of the outbreak centre in the first week; and (5) altitude of the herd location. Transformations were attempted for each of these variables, e.g. categorisation, ArcSin transformation, square root. Transformations that gave the best model fit as calculated by the log quasi-likelihood function were included in the final GEE regression model. All two-way interactions were tested and included in the model if found to be significant. Using scatter plots, various transformations for each variable were examined and those that gave the best model fit were included. A P value >0.1 was defined as the criterion for a variable’s exclusion from the final model. Moran’s I statistic was then calculated for the residuals of the final model to detect possible spatial autocorrelation in the model residuals. Goodness of fit of the logistic model was determined by receiver operating characteristics (ROC) analysis and calculation of the area under the curve (AUC) for model prediction against the actual results and by calculation of R2 for the correlation of observed and model-predicted herd seroprevalence. GEE and model diagnostics were performed with SPSS 17.0 for Windows.
Results
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Analysis of EHDV and BTV seroprevalence 20
0 A
B
C
D
E
F
Fig. 2. Epizootic haemorrhagic disease virus (EHDV) and bluetongue virus (BTV) seroprevalence in 1650 calves, healthy and sick cows sampled in herds that reported EHDV-suggestive clinical signs and herds that did not report any typical signs. Grey, EHDV; Black, BTV. Circles represent marginal means calculated by the GEE logistic model. Error bars represent 95% confidence intervals. (A) Calves on unaffected farms. (B) Cows on unaffected farms. (C) Cows in acute stage on affected farms. (D) Cows in convalescent stage on affected farms. (E) Healthy cows on affected farms. (F) Calves on affected farms.
Antibodies against EHDV were detected in 5% of calves and 15% of adult cows in control herds (P < 0.0001) (Fig. 2). In clinically affected herds, antibodies against EHDV were detected in 44% of unaffected calves and 57% of unaffected cows (P < 0.0001), indicating a high frequency of sub-clinical infections. On affected farms, antibodies against EHDV were detected in 72% of cows that had clinical signs and 80% of cows in which disease had begun at least 2 weeks prior to sampling (P < 0.01). The seroprevalence of BTV in cows in EHDV affected herds was significantly higher than in cows in control herds (95% vs. 65% in case and control herds, respectively, P < 0.0001), whereas the seroprevalence of BTV in calves
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did not differ significantly between case and control herds (49% vs. 47%, respectively; P = 0.57). Spatial cluster analysis of BTV and EHDV seroprevalence The seroprevalence of EHDV in both cows and calves was highest in the Jordan valley and gradually decreased westward. A high seroprevalence of BTV was also observed in the Jordan valley, as well as in the coastal plain and western valleys of Israel (Jezreel valley) (Fig. 3). EB-corrected Moran’s I statistics for EHDV seroprevalence in calves and cows were 0.54 and 0.59, respectively, and
were significantly higher than 0 (P < 0.0001 for both), indicating statistically significant spatial autocorrelation of EHDV seropositivity in both cows and calves. Moran’s I statistics for BTV seroprevalence in calves and cows were 0.26 and 0.44, respectively, which were lower than for EHDV, but also significantly higher than 0 (P < 0.0001 for both), indicating statistically significant spatial autocorrelation of BTV seropositivity in both cows and calves. Spatial cluster analysis using the spatial scan statistic showed that a cluster indicating high infection risk of EHDV for calves was located in the Rift Valley (central point E 35 36 8, N 32 41 9, angle 0°, major semi-axis length 58.5 km, minor semi-axis length
Fig. 3. Spatial distribution of seroprevalence of bluetongue virus in cows (A) and calves (B) and of seroprevalence of epizootic haemorrhagic disease virus (EHDV) in cows (C) and calves (D) after the 2006 EHDV outbreak.
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Fig. 4. Clusters of high seroprevalence of bluetongue virus in calves (A) and cows (B) and of epizootic haemorrhagic disease virus in calves (C). Ellipses, Bernoulli model cluster zones found by the spatial scan statistic; white dots, herds identified by the spatial scan statistic as being part of a high-seroprevalence cluster; black dots, herds identified by the LISA statistic as being part of a high-seroprevalence cluster; grey dots, herds identified by both methods as being part of a high-seroprevalence cluster.
Table 1 Multivariate logistic regression model for the explanation of EHDV seroprevalence in Israel after the 2006 outbreak. Variable Constant Is the herd located in the Rift valley? No (reference category) Yes Age Calves (age 0.5–1 year) Cows (age >1.5 years) Square root of herd distance from mean centre of outbreak in the first week (km) Altitude (km) Arcsin-transformed BTV seroprevalence a
Ba (standard error) 1.07 (0.61)
Odds ratio (95% confidence interval) 0.34
P value 0.003
1.73 (0.31)
1 5.62 (3.09–10.22)
<0.0001
0.89 (0.21) 0.33 (0.05) 1.34 (0.66) 1.51 (0.37)
1 2.43 0.72 0.25 4.54
<0.0001 <0.0001 0.068 <0.0001
(1.61–3.67) (0.66–0.79) (0.05–1.11) (2.2–9.38)
Model coefficient, equals to ln(odds ratio).
14.6 km) and was almost identical to that of the cluster indicating high exposure to BTV in cows (central point E 35 36 10, N 32 49 9, angle 0°, major semi-axis length 61.5 km, minor semi-axis length 15.9 km) (Figs. 4B and C). High BTV exposure in calves was clustered in two different geographical locations: one cluster was located in the western valleys of Israel (central point E 35 15 24, N 32 39 42, angle 45°, major semi-axis length 44.4 km, minor semiaxis length 11.1 km); a second and smaller cluster was located in the coastal plain (central point E 34 38 2, N 31 42 59, angle 60°, major semi-axis length 29.3 km, minor semi-axis length 5.86 km). LISA analysis showed approximately the same positive cluster locations (although in this analysis a smaller number of herds were included in each cluster), except for BTV seroprevalence in calves, for which one of the clusters found by the spatial scan statistic was not found by this method (Fig. 4A). Multivariate model for EHDV seroprevalence The GEE regression model included geographical location (i.e. in or outside the Rift Valley, coinciding with wind direction), square root of the distance from the first week’s outbreak centre, arcsintransformed BTV seroprevalence in cows and altitude of herd location (Table 1). An odds ratio of 4.5 was found for arcsin(BTV) = 1. Since arcsin(0.85) = 1, the interpretation of this finding is that a cow or a calf from a herd with a seroprevalence of 85% for BTV had a 4.5-fold higher risk for being seropositive for EHDV. All two-way interactions were found to lack statistical significance
(P > 0.05) and therefore were not included in the model. R2 for the correlation between model results and the real observations was 0.755. AUC for the ROC curve performed for model prediction against the observed results was 0.876 (0.852–0.899). Moran’s I statistic for this model’s residuals was 0.04 (P = 0.15). Thus, it was concluded that there is no residual autocorrelation in the model residuals, i.e. the model explains the distribution of EHDV without the need for an inclusion of a term to account for the distance between herds. Discussion The distribution of EHDV and BTV is largely dependent on the environmental factors that determine the abundance of their arthropod vectors. Several studies have used environmental and remote-sensing data to predict the abundance and spread of BTV and Culicoides spp. (Baylis and Rawlings, 1998; Baylis et al., 1999, 2001; Tatem et al., 2003; Purse et al., 2004a; Guis et al., 2007). As expected, the seroprevalence of BTV in cows was significantly associated with the seroprevalence of EHDV (i.e. cows or calves from herds in which the seroprevalence of BTV was 85% had a 4.5-fold higher risk of being seropositive for EHDV). BTV seropositivity therefore could serve as a surrogate marker for the spread of EHDV. This is after controlling for the following covariates: herd altitude, location of the herd (within or outside the Rift Valley), distance from first outbreak location and animal age (cow or calf). The implication of these results is that, during an outbreak, surveillance
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in herds with a high seroprevalence of BTV should be more intense and preventive measures should be instituted (e.g. use of insecticides). Such herds should also be given a high priority for vaccination for the emerging virus, if such a vaccine exists. Biosecurity measures may also be applied, although previous analysis has showed that air movement rather than animal movement is probably the most important factor in the spread of EHDV (Kedmi et al., 2010c). Despite the significant association between exposure to BTV and exposure to EHDV, some of the herds that were located where C. imicola and members of the C. schultzei group are likely to be abundant (as shown by a high seroprevalence of BTV) had low or even no evidence of exposure to EHDV, probably because EHDV did not have sufficient time to reach them during September and October, the months of highest C. imicola activity in Israel (Mellor et al., 2000). Thus, other factors, such as distance from the point of outbreak onset and location (within or outside the Rift Valley), were added to the model and showed highly significant associations with exposure to EHDV. The explanation for these associations is that the attack rate in a particular herd is associated with the time it is infected, which depends on the rate of spread of the virus and the distance of the herd from the first outbreak centre. Location of a herd in the Rift Valley was correlated with EHDV seroprevalence, either because the environmental conditions in this location favour activity of the potential vectors or because spread in this direction was faster due to wind. An explanation for the similar seroprevalences of EHDV in healthy cows and calves is that EHDV is an emerging virus in Israel. In contrast, the seroprevalence of BTV in cows was significantly higher than in calves, consistent with endemic infection. There was a small but significant difference between the seroprevalence of EHDV in cows and calves on affected farms, which raises the possibility that a low prevalence of this or some other serotypes of EHDV existed in Israel prior to 2006; clinical signs typical of EHD were described in cattle in the early 1950s (Komarov and Goldsmit, 1951). BTV seroprevalence in cows from EHD-clinically affected herds was significantly higher than in cows from unaffected herds. High BTV seroprevalence in cows clustered in the same location as high EHDV seroprevalence in calves (this was found by two methods of cluster analysis: the LISA statistic and grid scanning by the spatial scan statistic). Both findings indicate that the higher exposure to EHDV during 2006 (as indicated by seroprevalence in calves) was located where there is usually high exposure to BTV. These findings may be explained by preferred environmental conditions for the development of the potential vectors (C. imicola or C. schultzei group) in these locations. Statistically significant autocorrelation for both EHDV and BTV in cows and calves further supports the hypothesis that environmental conditions which tend to correlate spatially have an important influence on prevalence of exposure to these viruses. However, autocorrelation may also be the result of the transmissibility of these viruses (i.e. infection of one herd increases the probability of infection in an adjacent herd). In comparison with the situation in cows, herds with a high seroprevalence for BTV in calves clustered in a different location from herds with a high seroprevalence for EHDV in calves. These findings may be explained by host population immunity; when there is a high proportion of immune animals, the reproductive number (R) of the virus may be <1. This would restrict outbreak development, despite adequate environmental conditions for vector development and virus transmission (Anderson and May, 1991). When herd immunity is reduced, R increases and an outbreak of disease caused by an otherwise endemic virus might develop. Stallknecht et al. (1995) suggested a similar hypothesis to explain outbreaks of EHD and bluetongue in deer. The addition of herd immunity as a factor may impair the autocorrelated spatial pattern related to environmental conditions. This may explain
the low Moran’s I autocorrelation statistic of BTV seroprevalence when compared to EHDV. The GEE regression model explained 75.5% (R2 = 0.755) of the variance in infection by EHDV during the 2006 outbreak, as demonstrated by seroprevalence of EHDV. Together with the high value of AUC in the ROC analysis (0.876), this represents a high goodness of fit, primarily when considering the low number of serum samples analysed in each herd (5–12 cows/calves per herd). There is a possibility that EHDV and BTV are transmitted by different vectors, which may influence the distribution of these viruses. However, as mentioned earlier, both circumstantial evidence (location and timing of the outbreak, as well as abundance of C. imicola in the region) and experimental evidence suggest that C. imicola is a competent vector for both viruses. As mentioned previously, members of the C. schultzei group may also be competent vectors for both viruses. The differences in the distribution of EHDV and BTV, despite the high likelihood of their being transmitted by the same vector, shed some light on the differences in the epidemiology of emerging and endemic viruses. The results of this study suggest that exposure to endemic viruses is influenced not only by the abundance of vectors, but also by the existence of herd immunity. Therefore, the location of outbreaks cannot be predicted by analysing environmental conditions alone, but must also include exposure to the virus in previous years. Similar results were found in a study describing the seroprevalence of EHDV in Indiana and Illinois (Boyer et al., 2008). The distribution of EHDV and BTV increased from north to south in these states, whereas differences occurred on a local scale due to the specific dynamics of each virus in each region (Boyer et al., 2008). This finding highlights the complexity of the epidemiology of vector-borne diseases and may explain why models that rely solely on environmental factors might fail to predict outbreaks of these diseases. For example, a model based on satellite-derived climatic variables could explain only 20% of the variation in occurrence of BTV outbreaks in Israel (Purse et al., 2004b). Adding prior seroprevalence to mathematical models that aim to predict outbreaks of viruses in an endemic setting may increase their accuracy by taking into account changes in infection dynamics attributed to herd immunity. Conclusions This study shows that the spatial distribution of BTV in Israel is similar to that of EHDV, consistent with similar epidemiological features of these two viruses. Exposure to an endemic virus (BTV) can be used to predict the spatial spread of an emerging virus (EHDV). However, herd immunity to the endemic virus and the rate of spread of the emerging virus should also be taken into account when such models are used. As shown in this study, these factors may cause differences in the geographical distribution of these viruses. Further investigation of the influence of herd immunity on the specific dynamics of endemic Culicoides-borne viruses should be performed in order to understand changes in morbidity and to predict local outbreaks. Conflict of interest statement None of the authors of this paper has a financial or personal relationship with other people or organisations that could inappropriately influence or bias the content of this paper. Acknowledgements This study was supported by the Binational Agricultural Research and Development Fund (BARD Grant Number IS-4105-08) and by the Israel Dairy Board Fund (Grant Number 705-0019-07).
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Komarov, A., Goldsmit, L., 1951. A disease, similar to blue tongue in cattle and sheep in Israel. Refuah Veterinarith 8, 96–100. Kulldorf, M., 1997. A spatial scan statistic. Communication in Statistics: Theory and Methods 26, 1481–1496. Kulldorf, M., 2006. SatScan User Guide for Version 7.0. Mellor, P.S., Osborne, R., Jennings, D.M., 1984. Isolation of bluetongue and related viruses from Culicoides spp. in the Sudan. Journal of Hygiene (London) 93, 621– 628. Mellor, P.S., Boorman, J., Baylis, M., 2000. Culicoides biting midges: their role as arbovirus vectors. Annual Review of Entomology 45, 307–340. Paweska, J.T., Venter, G.J., Hamblin, C., 2005. A comparison of the susceptibility of Culicoides imicola and C. bolitinos to oral infection with eight serotypes of epizootic haemorrhagic disease virus. Medical and Veterinary Entomology 19, 200–207. Purse, B.V., Tatem, A.J., Caracappa, S., Rogers, D.J., Mellor, P.S., Baylis, M., Torina, A., 2004a. Modelling the distributions of Culicoides bluetongue virus vectors in Sicily in relation to satellite-derived climate variables. Medical and Veterinary Entomology 18, 90–101. Purse, B.V., Baylis, M., Tatem, A.J., Rogers, D.J., Mellor, P.S., Van Ham, M., ChizovGinzburg, A., Braverman, Y., 2004b. Predicting the risk of bluetongue through time: climate models of temporal patterns of outbreaks in Israel. Revue Scientifique et Technique (International Office of Epizootics) 23, 761–775. Purse, B.V., Mellor, P.S., Rogers, D.J., Samuel, A.R., Mertens, P.P., Baylis, M., 2005. Climate change and the recent emergence of bluetongue in Europe. Nature Reviews. Microbiology 3, 171–181. Stallknecht, D.E., Blue, J.L., Rollor III, E.A., Nettles, V.F., Davidson, W.R., Pearson, J.E., 1991. Precipitating antibodies to epizootic hemorrhagic disease and bluetongue viruses in white-tailed deer in the southeastern United States. Journal of Wildlife Diseases 27, 238–247. Stallknecht, D.E., Nettles, V.F., Rollor III, E.A., Howerth, E.W., 1995. Epizootic hemorrhagic disease virus and bluetongue virus serotype distribution in whitetailed deer in Georgia. Journal of Wildlife Diseases 31, 331–338. Stallknecht, D.E., Luttrell, M.P., Smith, K.E., Nettles, V.F., 1996. Hemorrhagic disease in white-tailed deer in Texas: a case for endemic stability. Journal of Wildlife Diseases 32, 695–700. Tatem, A.J., Baylis, M., Mellor, P.S., Purse, B.V., Capela, R., Pena, I., Rogers, D.J., 2003. Prediction of bluetongue vector distribution in Europe and North Africa using satellite imagery. Veterinary Microbiology 97, 13–29. Temizel, E.M., Yesilbag, K., Batten, C., Senturk, S., Maan, N.S., Clement-Mertens, P.P., Batmaz, H., 2009. Epizootic hemorrhagic disease in cattle, Western Turkey. Emerging Infectious Diseases 15, 317–319. Thevasagayam, J.A., Mertens, P.P., Burroughs, J.N., Anderson, J., 1995. Competitive ELISA for the detection of antibodies against epizootic haemorrhagic disease of deer virus. Journal of Virological Methods 55, 417–425. Thevasagayam, J.A., Woolhouse, T.R., Mertens, P.P., Burroughs, J.N., Anderson, J., 1996. Monoclonal antibody based competitive ELISA for the detection of antibodies against epizootic haemorrhagic disease of deer virus. Journal of Virological Methods 57, 117–126. Yadin, H., Brenner, J., Bumbrov, V., Oved, Z., Stram, Y., Klement, E., Perl, S., Anthony, S., Maan, S., Batten, C., Mertens, P.P., 2008. Epizootic haemorrhagic disease virus type 7 infection in cattle in Israel. Veterinary Record 162, 53–56.
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Comparison of the epidemiology of epizootic haemorrhagic disease and bluetongue viruses in dairy cattle in Israel Maor Kedmi a,b, Nadav Galon b, Yael Herziger a, Hagai Yadin c, Velizar Bombarov c, Carrie Batten d, Nahum Y. Shpigel a, Eyal Klement a,⇑ a
Koret School of Veterinary Medicine, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel Hachaklait, Mutual Society for Veterinary Services, Caesarea Industrial, Israel Department of Virology, Kimron Veterinary Institute, Bet Dagan, Israel d Institute for Animal Health, Ash Road, Pirbright, UK b c
a r t i c l e
i n f o
Article history: Accepted 17 October 2010
Keywords: Epizootic haemorrhagic disease Bluetongue virus Cattle Spatial analysis Endemic Epidemic
a b s t r a c t An outbreak of epizootic haemorrhagic disease virus (EHDV) in cattle in Israel in 2006 enabled a comparison of the spatial distribution of epidemic exposure to EHDV with that of exposure to bluetongue virus (BTV), which is endemic in the country. The seroprevalence of both viruses was examined in 1650 serum samples collected from 139 farms representative of the spatial distribution of dairy cattle in Israel. A significant association between exposure to EHDV and BTV was demonstrated in both univariate and multivariate analyses. Recent exposure to BTV and EHDV (demonstrated by seroprevalence in calves) was clustered in different geographical locations, indicating that the two viruses had different patterns of spread, that of EHDV being influenced by winds and terrain barriers and that of BTV by herd immunity. Ó 2010 Elsevier Ltd. All rights reserved.
Introduction Epizootic haemorrhagic disease virus (EHDV) and bluetongue virus (BTV) are viruses in the genus Orbivirus that are transmitted by several species of midge (genus Culicoides). Since the 1990s, there have been several incursions of BTV serotypes into new locations in Europe (Purse et al., 2005). In 2006, cattle-virulent EHDV appeared in Israel (Yadin et al., 2008), Morocco and Algeria,1 and entered Turkey in 2007 (Temizel et al., 2009). Environmental correlates of Culicoides spp. habitats have been used to predict the abundance of BTV and EHDV (Baylis and Rawlings, 1998; Baylis et al., 1999, 2001; Tatem et al., 2003; Purse et al., 2004a; Guis et al., 2007). The distributions of both viruses are often studied together (Stallknecht et al., 1991, 1995, 1996). Thus, data on the distribution of BTV could be used to assess the risk of exposure to EHDV and vice versa. Along with the expected similarities between the distributions of EHDV and BTV, variation may exist due to factors associated with their respective epidemic and endemic states. When virus incursion into a new region occurs in the autumn (as occurred during the 2006 EHDV outbreak in Israel), the attack rate in locations ⇑ Corresponding author. Tel.: +972 8 9489560; fax: +972 8 9489138. E-mail address: [email protected] (E. Klement). PROMED archive numbers 20061010.2906, 20061214.3513; http:// www.promedmail.org.
affected at the onset of the outbreak may be higher than in locations affected during the later stages. This is because environmental conditions change with the approach of winter and become less favourable for the vector. Outbreaks may also occur as a part of the endemic cycle following a reduction in herd immunity. The abundance of seropositive immune animals has a major influence on the spatial distribution of new cases of the endemic disease and may play a significant role in determining the appearance of morbidity in certain regions. For example, areas in West Virginia, USA, that were characterised by low EHDV seroprevalence in white tailed deer suffered more severe outbreaks of epizootic haemorrhagic disease (EHD) (Gaydos et al., 2004). Flacke et al. (2004) studied the distribution of endemic and epidemic EHDV in two regions that differed in environmental conditions. However, in order to understand how the distributions of EHDV and BTV differ in endemic and epidemic situations, comparisons should be made in the same region and at the same time, so that environmental conditions are the same and the main difference is in herd immunity to each virus. An epidemic of cattle-virulent EHDV occurred in Israel in the summer and autumn of 2006. While sheep were not affected during this epidemic (Kedmi et al., 2010a), it caused significant production losses to the dairy cattle industry, estimated at US$2.5 million (Kedmi et al., 2010b).2 As part of the outbreak investigation,
1
1090-0233/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tvjl.2010.10.003
2
US$1 = approximately £0.63, €0.72, as at 17th October 2010.
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we conducted a serological survey to describe the spatial distribution of emerging EHDV and of BTV. BTV serotypes 2, 4, 6, 10 and 16 have been previously identified in Israel and serotypes 5, 8, 15 and 24 have been diagnosed recently. Among these serotypes, BTV-4 is the most dominant and the only one that appears consistently (Barzilai and Shimshony, 1985). We therefore consider BTV in this study to be an endemic virus, assuming that seroprevalence of BTV represents exposure to BTV-4. Culicoides imicola is a competent vector for EHDV (Paweska et al., 2005) and is the main vector of BTV in Israel (Braverman et al., 1985). Taken together with its high abundance, there is a high probability that C. imicola is the vector for EHDV in Israel. Other potential vectors of both viruses are Culicoides spp. of the schultzei group, since EHDV has been isolated from C. kingi (Mellor et al., 1984), a member of this group that is also abundant in Israel. The outbreak of cattle-virulent EHDV in Israel in 2006 provided an opportunity to compare the spatial distributions of emerging EHDV and endemic BTV. It also enabled us to assess whether exposure to an endemic virus, such as BTV, can be used to predict the spread of an emerging virus, such as EHDV, in Israel.
Materials and methods Outbreak data set Data collected during a large EHDV outbreak in Israel during the summer and autumn of 2006 were analysed. The first affected herd was reported on 28 August 2006. Infection spread from this herd southward towards the Dead Sea and northward along the Rift Valley to the northern border of Israel (Fig. 1). The virus then spread from the primary outbreak region westward through the valleys and into the coastal plain. Overall, the disease was reported in 83 dairy herds from 78 localities and from 22 beef herds. Newly affected herds continued to be reported for 13 weeks, until the third week of November 2006. The herd case definition for diagnosing EHD was as follows: conjunctivitis and/or salivation and/or stiff gait and/or congested mucous membranes in two or more cows, accompanied by a sudden reduction in milk production in several cows in the herd. Sample collection Serum samples suitable for inclusion in the serological survey were collected from 66 EHD-clinically affected dairy cattle herds at least one month after the onset of clinical signs in each herd. In each herd, veterinarians collected serum samples at random from five calves aged 6–12 months, five apparently healthy cows (age >1.5 years), five cows that exhibited clinical signs at the time of collection and five cows that had begun showing clinical signs (including reduction in milk yield) at
Fig. 1. Spatial distribution of sampled herds superimposed on a kernel density map depicting the quantitative distribution of dairy cows in Israel (the kernel smoothed density map was obtained by calculating cow density per square km with a search radius of 10 km). Black dots, affected herds (n = 66) (report of typical clinical disease during the outbreak); white dots, unaffected herds (n = 73). Solid arrows, herds affected during the first week of the outbreak (28th August–1st September 2006). Dashed arrow, the last herd affected (17th November 2006).
M. Kedmi et al. / The Veterinary Journal 190 (2011) 77–83 least 2 weeks before blood sampling. To serve as control herds, 73 unaffected dairy herds were randomly selected from the list of cooperative (Hachaklait) herds in Israel. In these herds, serum samples were collected only from calves and healthy cows. No clinical signs of bluetongue were observed in case or control herds. The probability of missing infection (u) was calculated according to the formula u = (1 P)n, where P is the seroprevalence and n is the number of sampled cows. With this sampling strategy, the probability of missing infection in a herd in which 20% of animals were seropositive was 10%, whereas for a herd in which 30% of animals were seropositive, this probability was <3%. Overall, 1650 serum samples were collected and tested serologically for exposure to BTV and EHDV.
Serological assays A competitive ELISA (c-ELISA) similar to an assay previously described by Thevasagayam et al. (1995) was used to detect serum antibodies against EHDV and BTV. This ELISA is able to detect exposure to all serotypes of EHDV and does not cross-react with BTV. The specificity and sensitivity of this assay were both 100% compared to the agar gel immunodiffusion test (Thevasagayam et al., 1996). EHDV-1, purified as described by Thevasagayam et al. (1995), was added to 96-well plates at a dilution of 1:250 and incubated at 37 °C for 1 h on an orbital shaker. The plates were washed three times prior to loading of the test serum samples, control sera and the competing EHDV VP7-specific monoclonal antibody at a dilution of 1:100. Four wells were loaded with all reagents but no serum and were regarded as zero-competition controls (C0). After incubation for 1 h at 37 °C, the conjugate (rabbit anti-mouse horseradish peroxidase, Dako; 1:1000) was added and the plate was incubated at 37 °C for a further 1 h. The substrate (TMB single solution, Zymed) was added, incubated for 15 min and then the reaction was stopped by addition of 1 M H2SO4. The plates were read at 405 nm (ELISA reader Sunrise, XFLUOR4 V4.51). Optical density (OD) values were converted to percentage inhibition (PI) values as follows: 100 [(OD of each test or control value)/(median OD of the 4 C0 wells)] 100. PI values P50% were considered to be positive. A commercial c-ELISA (Bluetongue Virus Antibody Test Kit, c-ELISA, VMRD) was used for detection of anti-BTV antibodies according to the manufacturer’s instructions.
Univariate analysis and spatial analysis of serological data The seroprevalence of BTV and EHDV was determined for each herd and for each group of cows and calves within a herd. The association between the seroprevalence of BTV and EHDV was assessed using a generalised estimating equation (GEE) regression model, which included the type of animal (calf, healthy cow, currently ill cow and cow affected at least two weeks before blood collection), the type of herd (clinically affected or not affected) and the interaction between them. The herd was included as a subject variable and an exchangeable covariance structure was used as the working matrix (i.e. correlation between serological measurements within the herds but not between the herds). The marginal means of seroprevalence and their 95% confidence intervals calculated from this model are reported. P values were calculated by running this model separately for each group.
100
Seroprevalence (%)
80
60
79
Spatial autocorrelation of EHDV and BTV seroprevalence in cows and calves was examined by calculation of Moran’s I statistic. This enabled assessment of the importance of location in exposure to the virus. Spatial weighting of herds used the K-nearest neighbours weighting method, with the number of neighbouring herds (K) set to 6. To overcome variance instability due to differences in the numbers of sampled animals in each herd, an adjustment procedure was applied that makes use of a variable transformation based on the empirical Bayes (EB) principle (Assuncao and Reis, 1999). This was referred to as the EB-corrected Moran’s I. Cluster analysis was then performed for these data by calculating the local Moran’s indicator for the spatial autocorrelation (LISA) statistic (Anselin, 1995). A lagged seroprevalence variable (i.e. a variable that estimates the seroprevalence in each herd from the spatially weighted seroprevalence of the herds that surround it) was computed for each herd. Positive clusters were defined where both standardised seroprevalence and standardised spatially lagged seroprevalence were positive. Only statistically significant clusters (P < 0.05) were mapped. A second cluster analysis was performed using SatScan software, with the Bernoulli model for case-control data (Kulldorf, 1997, 2006). For this analysis, individual cows/calves seropositive for BTV/EHDV were regarded as cases and negative animals as controls. In this iterative method, the ratio of cases and controls in and out of a scanning elliptic window was compared. Only ellipses that contained no more than 50% of the animals were examined. The window with the maximum likelihood was considered to be the most likely cluster, i.e. the cluster least likely to occur due to chance. The P value was then calculated through Monte Carlo hypothesis testing by comparing the rank (R) of the maximum likelihood from the real dataset with the maximum likelihoods from 999 random datasets using the following equation: P = R/(1 + number of simulations). Map construction and calculation of herd kernel density were performed using Geostatistical analyst and spatial analyst extensions and by the spatial statistics tools in ArcGis 9.3. Spatially based weighting and calculation of the LISA statistic and the global Moran’s I statistic were performed using Geoda software. Multivariate analysis of serological data Serological results for 1096 serum samples collected from calves and healthy cows, using individual cow/calf as the unit of interest, were used for the analysis of risk factors associated with EHDV seroprevalence, since these were the only groups sampled in both clinically infected and control herds. A GEE regression model with Logit as the link function and an exchangeable covariance structure for the working matrix was constructed to examine the association of EHDV seroprevalence with factors influencing the time of infection (e.g. distance from location of onset of outbreak) and with BTV seroprevalence. Variables included in the model were: (1) age group (i.e. calf or cow), to account for possible previous exposure to EHDV; (2) BTV seroprevalence in cows at the herd level; (3) geographical location (within or outside the Rift Valley); (4) distance from the location of the outbreak centre in the first week; and (5) altitude of the herd location. Transformations were attempted for each of these variables, e.g. categorisation, ArcSin transformation, square root. Transformations that gave the best model fit as calculated by the log quasi-likelihood function were included in the final GEE regression model. All two-way interactions were tested and included in the model if found to be significant. Using scatter plots, various transformations for each variable were examined and those that gave the best model fit were included. A P value >0.1 was defined as the criterion for a variable’s exclusion from the final model. Moran’s I statistic was then calculated for the residuals of the final model to detect possible spatial autocorrelation in the model residuals. Goodness of fit of the logistic model was determined by receiver operating characteristics (ROC) analysis and calculation of the area under the curve (AUC) for model prediction against the actual results and by calculation of R2 for the correlation of observed and model-predicted herd seroprevalence. GEE and model diagnostics were performed with SPSS 17.0 for Windows.
Results
40
Analysis of EHDV and BTV seroprevalence 20
0 A
B
C
D
E
F
Fig. 2. Epizootic haemorrhagic disease virus (EHDV) and bluetongue virus (BTV) seroprevalence in 1650 calves, healthy and sick cows sampled in herds that reported EHDV-suggestive clinical signs and herds that did not report any typical signs. Grey, EHDV; Black, BTV. Circles represent marginal means calculated by the GEE logistic model. Error bars represent 95% confidence intervals. (A) Calves on unaffected farms. (B) Cows on unaffected farms. (C) Cows in acute stage on affected farms. (D) Cows in convalescent stage on affected farms. (E) Healthy cows on affected farms. (F) Calves on affected farms.
Antibodies against EHDV were detected in 5% of calves and 15% of adult cows in control herds (P < 0.0001) (Fig. 2). In clinically affected herds, antibodies against EHDV were detected in 44% of unaffected calves and 57% of unaffected cows (P < 0.0001), indicating a high frequency of sub-clinical infections. On affected farms, antibodies against EHDV were detected in 72% of cows that had clinical signs and 80% of cows in which disease had begun at least 2 weeks prior to sampling (P < 0.01). The seroprevalence of BTV in cows in EHDV affected herds was significantly higher than in cows in control herds (95% vs. 65% in case and control herds, respectively, P < 0.0001), whereas the seroprevalence of BTV in calves
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did not differ significantly between case and control herds (49% vs. 47%, respectively; P = 0.57). Spatial cluster analysis of BTV and EHDV seroprevalence The seroprevalence of EHDV in both cows and calves was highest in the Jordan valley and gradually decreased westward. A high seroprevalence of BTV was also observed in the Jordan valley, as well as in the coastal plain and western valleys of Israel (Jezreel valley) (Fig. 3). EB-corrected Moran’s I statistics for EHDV seroprevalence in calves and cows were 0.54 and 0.59, respectively, and
were significantly higher than 0 (P < 0.0001 for both), indicating statistically significant spatial autocorrelation of EHDV seropositivity in both cows and calves. Moran’s I statistics for BTV seroprevalence in calves and cows were 0.26 and 0.44, respectively, which were lower than for EHDV, but also significantly higher than 0 (P < 0.0001 for both), indicating statistically significant spatial autocorrelation of BTV seropositivity in both cows and calves. Spatial cluster analysis using the spatial scan statistic showed that a cluster indicating high infection risk of EHDV for calves was located in the Rift Valley (central point E 35 36 8, N 32 41 9, angle 0°, major semi-axis length 58.5 km, minor semi-axis length
Fig. 3. Spatial distribution of seroprevalence of bluetongue virus in cows (A) and calves (B) and of seroprevalence of epizootic haemorrhagic disease virus (EHDV) in cows (C) and calves (D) after the 2006 EHDV outbreak.
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Fig. 4. Clusters of high seroprevalence of bluetongue virus in calves (A) and cows (B) and of epizootic haemorrhagic disease virus in calves (C). Ellipses, Bernoulli model cluster zones found by the spatial scan statistic; white dots, herds identified by the spatial scan statistic as being part of a high-seroprevalence cluster; black dots, herds identified by the LISA statistic as being part of a high-seroprevalence cluster; grey dots, herds identified by both methods as being part of a high-seroprevalence cluster.
Table 1 Multivariate logistic regression model for the explanation of EHDV seroprevalence in Israel after the 2006 outbreak. Variable Constant Is the herd located in the Rift valley? No (reference category) Yes Age Calves (age 0.5–1 year) Cows (age >1.5 years) Square root of herd distance from mean centre of outbreak in the first week (km) Altitude (km) Arcsin-transformed BTV seroprevalence a
Ba (standard error) 1.07 (0.61)
Odds ratio (95% confidence interval) 0.34
P value 0.003
1.73 (0.31)
1 5.62 (3.09–10.22)
<0.0001
0.89 (0.21) 0.33 (0.05) 1.34 (0.66) 1.51 (0.37)
1 2.43 0.72 0.25 4.54
<0.0001 <0.0001 0.068 <0.0001
(1.61–3.67) (0.66–0.79) (0.05–1.11) (2.2–9.38)
Model coefficient, equals to ln(odds ratio).
14.6 km) and was almost identical to that of the cluster indicating high exposure to BTV in cows (central point E 35 36 10, N 32 49 9, angle 0°, major semi-axis length 61.5 km, minor semi-axis length 15.9 km) (Figs. 4B and C). High BTV exposure in calves was clustered in two different geographical locations: one cluster was located in the western valleys of Israel (central point E 35 15 24, N 32 39 42, angle 45°, major semi-axis length 44.4 km, minor semiaxis length 11.1 km); a second and smaller cluster was located in the coastal plain (central point E 34 38 2, N 31 42 59, angle 60°, major semi-axis length 29.3 km, minor semi-axis length 5.86 km). LISA analysis showed approximately the same positive cluster locations (although in this analysis a smaller number of herds were included in each cluster), except for BTV seroprevalence in calves, for which one of the clusters found by the spatial scan statistic was not found by this method (Fig. 4A). Multivariate model for EHDV seroprevalence The GEE regression model included geographical location (i.e. in or outside the Rift Valley, coinciding with wind direction), square root of the distance from the first week’s outbreak centre, arcsintransformed BTV seroprevalence in cows and altitude of herd location (Table 1). An odds ratio of 4.5 was found for arcsin(BTV) = 1. Since arcsin(0.85) = 1, the interpretation of this finding is that a cow or a calf from a herd with a seroprevalence of 85% for BTV had a 4.5-fold higher risk for being seropositive for EHDV. All two-way interactions were found to lack statistical significance
(P > 0.05) and therefore were not included in the model. R2 for the correlation between model results and the real observations was 0.755. AUC for the ROC curve performed for model prediction against the observed results was 0.876 (0.852–0.899). Moran’s I statistic for this model’s residuals was 0.04 (P = 0.15). Thus, it was concluded that there is no residual autocorrelation in the model residuals, i.e. the model explains the distribution of EHDV without the need for an inclusion of a term to account for the distance between herds. Discussion The distribution of EHDV and BTV is largely dependent on the environmental factors that determine the abundance of their arthropod vectors. Several studies have used environmental and remote-sensing data to predict the abundance and spread of BTV and Culicoides spp. (Baylis and Rawlings, 1998; Baylis et al., 1999, 2001; Tatem et al., 2003; Purse et al., 2004a; Guis et al., 2007). As expected, the seroprevalence of BTV in cows was significantly associated with the seroprevalence of EHDV (i.e. cows or calves from herds in which the seroprevalence of BTV was 85% had a 4.5-fold higher risk of being seropositive for EHDV). BTV seropositivity therefore could serve as a surrogate marker for the spread of EHDV. This is after controlling for the following covariates: herd altitude, location of the herd (within or outside the Rift Valley), distance from first outbreak location and animal age (cow or calf). The implication of these results is that, during an outbreak, surveillance
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in herds with a high seroprevalence of BTV should be more intense and preventive measures should be instituted (e.g. use of insecticides). Such herds should also be given a high priority for vaccination for the emerging virus, if such a vaccine exists. Biosecurity measures may also be applied, although previous analysis has showed that air movement rather than animal movement is probably the most important factor in the spread of EHDV (Kedmi et al., 2010c). Despite the significant association between exposure to BTV and exposure to EHDV, some of the herds that were located where C. imicola and members of the C. schultzei group are likely to be abundant (as shown by a high seroprevalence of BTV) had low or even no evidence of exposure to EHDV, probably because EHDV did not have sufficient time to reach them during September and October, the months of highest C. imicola activity in Israel (Mellor et al., 2000). Thus, other factors, such as distance from the point of outbreak onset and location (within or outside the Rift Valley), were added to the model and showed highly significant associations with exposure to EHDV. The explanation for these associations is that the attack rate in a particular herd is associated with the time it is infected, which depends on the rate of spread of the virus and the distance of the herd from the first outbreak centre. Location of a herd in the Rift Valley was correlated with EHDV seroprevalence, either because the environmental conditions in this location favour activity of the potential vectors or because spread in this direction was faster due to wind. An explanation for the similar seroprevalences of EHDV in healthy cows and calves is that EHDV is an emerging virus in Israel. In contrast, the seroprevalence of BTV in cows was significantly higher than in calves, consistent with endemic infection. There was a small but significant difference between the seroprevalence of EHDV in cows and calves on affected farms, which raises the possibility that a low prevalence of this or some other serotypes of EHDV existed in Israel prior to 2006; clinical signs typical of EHD were described in cattle in the early 1950s (Komarov and Goldsmit, 1951). BTV seroprevalence in cows from EHD-clinically affected herds was significantly higher than in cows from unaffected herds. High BTV seroprevalence in cows clustered in the same location as high EHDV seroprevalence in calves (this was found by two methods of cluster analysis: the LISA statistic and grid scanning by the spatial scan statistic). Both findings indicate that the higher exposure to EHDV during 2006 (as indicated by seroprevalence in calves) was located where there is usually high exposure to BTV. These findings may be explained by preferred environmental conditions for the development of the potential vectors (C. imicola or C. schultzei group) in these locations. Statistically significant autocorrelation for both EHDV and BTV in cows and calves further supports the hypothesis that environmental conditions which tend to correlate spatially have an important influence on prevalence of exposure to these viruses. However, autocorrelation may also be the result of the transmissibility of these viruses (i.e. infection of one herd increases the probability of infection in an adjacent herd). In comparison with the situation in cows, herds with a high seroprevalence for BTV in calves clustered in a different location from herds with a high seroprevalence for EHDV in calves. These findings may be explained by host population immunity; when there is a high proportion of immune animals, the reproductive number (R) of the virus may be <1. This would restrict outbreak development, despite adequate environmental conditions for vector development and virus transmission (Anderson and May, 1991). When herd immunity is reduced, R increases and an outbreak of disease caused by an otherwise endemic virus might develop. Stallknecht et al. (1995) suggested a similar hypothesis to explain outbreaks of EHD and bluetongue in deer. The addition of herd immunity as a factor may impair the autocorrelated spatial pattern related to environmental conditions. This may explain
the low Moran’s I autocorrelation statistic of BTV seroprevalence when compared to EHDV. The GEE regression model explained 75.5% (R2 = 0.755) of the variance in infection by EHDV during the 2006 outbreak, as demonstrated by seroprevalence of EHDV. Together with the high value of AUC in the ROC analysis (0.876), this represents a high goodness of fit, primarily when considering the low number of serum samples analysed in each herd (5–12 cows/calves per herd). There is a possibility that EHDV and BTV are transmitted by different vectors, which may influence the distribution of these viruses. However, as mentioned earlier, both circumstantial evidence (location and timing of the outbreak, as well as abundance of C. imicola in the region) and experimental evidence suggest that C. imicola is a competent vector for both viruses. As mentioned previously, members of the C. schultzei group may also be competent vectors for both viruses. The differences in the distribution of EHDV and BTV, despite the high likelihood of their being transmitted by the same vector, shed some light on the differences in the epidemiology of emerging and endemic viruses. The results of this study suggest that exposure to endemic viruses is influenced not only by the abundance of vectors, but also by the existence of herd immunity. Therefore, the location of outbreaks cannot be predicted by analysing environmental conditions alone, but must also include exposure to the virus in previous years. Similar results were found in a study describing the seroprevalence of EHDV in Indiana and Illinois (Boyer et al., 2008). The distribution of EHDV and BTV increased from north to south in these states, whereas differences occurred on a local scale due to the specific dynamics of each virus in each region (Boyer et al., 2008). This finding highlights the complexity of the epidemiology of vector-borne diseases and may explain why models that rely solely on environmental factors might fail to predict outbreaks of these diseases. For example, a model based on satellite-derived climatic variables could explain only 20% of the variation in occurrence of BTV outbreaks in Israel (Purse et al., 2004b). Adding prior seroprevalence to mathematical models that aim to predict outbreaks of viruses in an endemic setting may increase their accuracy by taking into account changes in infection dynamics attributed to herd immunity. Conclusions This study shows that the spatial distribution of BTV in Israel is similar to that of EHDV, consistent with similar epidemiological features of these two viruses. Exposure to an endemic virus (BTV) can be used to predict the spatial spread of an emerging virus (EHDV). However, herd immunity to the endemic virus and the rate of spread of the emerging virus should also be taken into account when such models are used. As shown in this study, these factors may cause differences in the geographical distribution of these viruses. Further investigation of the influence of herd immunity on the specific dynamics of endemic Culicoides-borne viruses should be performed in order to understand changes in morbidity and to predict local outbreaks. Conflict of interest statement None of the authors of this paper has a financial or personal relationship with other people or organisations that could inappropriately influence or bias the content of this paper. Acknowledgements This study was supported by the Binational Agricultural Research and Development Fund (BARD Grant Number IS-4105-08) and by the Israel Dairy Board Fund (Grant Number 705-0019-07).
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