Canine distemper is a high-impact disease of many mammal species and has caused substantial carnivore population declines. Analysis was conducted on passive surveillance data of canine distemper (CDV)–positive wild mammal cases submitted to the Southeastern Cooperative Wildlife Disease Study, Athens, Georgia, US, between January 1975 and December 2019. Overall, 964 cases from 17 states were CDV positive, including 646 raccoons (Procyon lotor), 254 gray foxes (Urocyon cinereoargenteus), 33 striped skunks (Mephitis mephitis), 18 coyotes (Canis latrans), four red foxes (Vulpes vulpes), three gray wolves (Canis lupus), three American black bears (Ursus americanus), two American mink (Mustela vison), and one long-tailed weasel (Mustela frenata). Raccoon and gray fox case data from the state of Georgia (n=441) were selected for further analysis. Autoregressive integrated moving average models were developed predicting raccoon and gray fox case numbers. The best-performing model for gray foxes used numbers of gray fox CDV cases from the previous 2 mo and of raccoon cases in the present month to predict the numbers of gray fox cases in the present month. The best-performing model for raccoon prediction used numbers of raccoon CDV cases from the previous month and of gray fox cases in the present month and previous 2 mo to predict numbers of raccoon cases in the present month. Temporal trends existed in CDV cases for both species, with cases more likely to occur during the breeding season. Spatial clustering of cases was more likely to occur in areas of medium to high human population density; fewer cases occurred in both the most densely populated and sparsely populated areas. This pattern was most prominent for raccoons, which may correspond to high transmission rates in suburban areas, where raccoon population densities are probably highest, possibly because of a combination of suitable habitat and supplemental resources.

Canine distemper affects a wide range of wild and domestic mammals, principally carnivores (Deem et al. 2000; Martinez-Gutierrez and Ruiz-Saenz 2016). It has the second highest case fatality rate among canine diseases, after rabies (Swango 1995). The causative agent, canine morbillivirus (CDV) is an enveloped, single-stranded, negative-sense RNA virus in the Morbillivirus family. The major route of transmission is through aerosolization of virus from respiratory secretions (Deem et al. 2000). Canine morbillivirus is highly infectious and may be shed for 60–90 d postinfection (Greene and Appel 1990; Loots et al. 2018).

Canine morbillivirus results in a highly immunizing, acute infection that typically requires high densities and large populations of hosts for long-term persistence (Williams 2001). The virus is maintained among wild carnivores by multihost transmission, which overcomes obstacles of population size and host density within a single host species (Almberg et al. 2010). There is evidence of CDV infection in all terrestrial carnivore families and some marine carnivore families (Deem et al. 2000). The Mustelidae family includes some of the species with the highest fatality rate, and the domestic dog (Canis lupus familiaris) can be a subclinical carrier (Deem et al. 2000). Canine distemper has been responsible for substantial population declines in the African lion (Panthera leo; Roelke-Parker et al. 1996), Amur tigers (Panthera tigris altaica; Seimon et al. 2013), and the endangered black-footed ferret (Mustela nigripes) in the US (Williams et al. 1988).

In North America, raccoons (Procyon lotor), red and gray foxes (Vulpes vulpes and Urocyon cinereoargenteus), coyotes (Canis latrans), wolves (Canis lupus), striped skunks (Mephitis mephitis), American badgers (Taxidea taxus), American mink (Mustela vison), and ferrets and weasels (Mustela spp.) are among the wild species susceptible to CDV infection (Beineke et al. 2015). Distemper is endemic in raccoon populations in the eastern US, and raccoons are thought to be a reservoir for other wild animals and domestic dogs (Roscoe 1993). Canine morbillivirus is also a major cause of disease among gray foxes in the southeastern US; in one study, 78% of animals that underwent postmortem diagnostic evaluation from 1972 to 1989 were diagnosed with the disease (Davidson et al. 1992). It can persist in areas with diverse carnivore populations, including Yellowstone National Park, where multiple outbreaks have occurred in wolf, coyote, and cougar (Puma concolor) populations within the park (Almberg et al. 2009, 2010).

Although there is a large body of work identifying distemper outbreaks in wild carnivores in the US (Roscoe 1993; Deem et al. 2000; Martinez-Gutierrez and Ruiz-Saenz 2016), there has been limited work on the spatio-temporal dynamics of CDV, particularly in raccoon populations. Furthermore, most studies have been short term and often have focused on systems such as the Yellowstone National Park system, which differs from the southeastern US in a number of ways, including the absence of major urban centers (Almberg et al. 2009, 2010). It is well established that urbanization can play a role in wildlife disease dynamics, particularly with multihost pathogens such as CDV (Bradley and Altizer 2007). Outside the US, time-series data from the wildlife–domestic animal interface has demonstrated that infection dynamics in one population can significantly impact the other, resulting in spatially structured incidence (Viana et al. 2015). As host species vary considerably in their clinical signs (Deem et al. 2000), determining the extent to which cross-species transmission drives epizootics within wildlife species could improve estimated risk of infection in subclinical hosts and facilitate integrated management strategies that include consequences of cross-species transmission (Haydon et al. 2002).

The primary objective of our study was to identify long-term spatial and temporal patterns in CDV cases submitted for diagnostic evaluation to the Southeastern Cooperative Wildlife Disease Study (SCWDS) at the University of Georgia. We also analyzed how outbreaks in two different species (raccoons and gray foxes) may be related, because raccoons are considered to be the primary wildlife reservoir of CDV, and epizootics in this species may be followed by epizootics in others. Finally, we identified spatial patterns of infection within the southeastern US, including potential associations with human activity.

Data acquisition and overview

We acquired data from wild mammals submitted to SCWDS between 1975 and 2019 that were diagnosed with CDV infection on postmortem examination. Cases of CDV infection were identified by one or more of the following diagnostic features: CDV positive by fluorescent antibody testing (Fairchild et al. 1971) or immunohistochemistry (Palmer et al. 1990) and characteristic histopathology (including intranuclear and intracytoplasmic inclusions). Many of the animals submitted were found dead or were found moribund and were subsequently euthanized. The data set contained the following variables: case number, state, county, area, sex, species, age, and collection year. Cases were submitted from many southeastern states; however, the greatest proportion of confirmed cases came from gray foxes and raccoons in Georgia (46%); all other reporting states had confirmed case numbers below 100 for the entire 44-yr period and thus did not provide sufficient cases for the time course analysis, as they are temporally sparse. Additionally, the states with the next highest cases (Louisiana, Kansas, and North Carolina) are not geographically contiguous with the state of Georgia, making spatial relationships unlikely. Therefore, analyses focused on confirmed cases in the state of Georgia.

Human population and land-area data for Georgia counties from 1975 to 2019 were accessed and downloaded from census.gov. Data were imported into R Studio (version 1.3.1056). A detailed description of data analysis is contained in the scripts within a project repository (Wilson 2020). All analyses were conducted in the R programming environment (version 3.5.3.; R Core Team 2020). References to packages in this Methods section indicate specific packages used within the R environment to perform analyses.

Spatial analysis

Mapping data for states and counties in the US, used to harmonize data at the county level, were obtained through the ggplot2 (Wickham 2016) package. As the location data were limited to county-level information, county centroid coordinates were used for plotting case points. Individual cases were mapped for the entire data set and the presence of CDV infection in raccoons and gray foxes was mapped at the county level in Georgia. Presence was defined as at least one CDV diagnosis in raccoon or gray fox in a particular county in Georgia in a particular year. Analysis of spatial clustering of cases in Georgia was performed using Ripley's K from the spatstat package (Baddeley et al. 2015). These analyses identified if, and at what spatial scale, spatial point data were more clustered or dispersed compared to a random distribution.

Temporal analysis

Cases were also analyzed in relation to the raccoon and gray fox reproduction cycles. The breeding season was defined as the period from January to March, and the period of lactation was defined as April to June. The remainder of the year was designated as the nonbreeding season (Zeveloff 2002). A chi-square test (McHugh 2013) was performed on these data to determine if cases occurred disproportionately in certain phases of the reproduction cycle.

Time-series analysis and auto-regressive integrated moving average (ARIMA) model construction was conducted using the “fpp2” package (Hyndman and Athanasopoulos 2018). The three components of an ARIMA model are autoregression, differencing, and the moving average. Autoregression uses previous data (i.e., cases) in the time series from present month, t, and previous months, tn, for n number of previous time points, up to n=12 mo, to predict future data; differencing computes the difference between observations in nonstationary data to remove the influence of trends or seasonality, and the moving average uses the past forecast errors (ϵtn) in the model to make future predictions. In addition to these basic components of the ARIMA model, lagged case data of the other species were included in models. In the gray fox predictive model, raccoon cases, ytn, were used along with past gray fox cases, xtn, to predict the present months gray for cases, xt, with the opposite being used in the raccoon model to predict present raccoon cast, yt.

For time series analysis, the data were pooled into raccoon or gray fox distemper cases in Georgia for each month from April 1975 to December 2019. For training and testing of the ARIMA models, the sequence of months from April 1975 to December 2019, n=524 time points, was divided approximately 80:20 into the training set (up to n=423; i.e., July 2011) and the testing set (n=423 to n=524; i.e., August 2011–December 2019). The training set was used for building the model and the testing set, the latter of which was withheld during model building, is used for testing the accuracy of the model's predictions compared to the real data. The stationarity of the time series was confirmed using both the augmented Dickey–Fuller test (Dickey and Fuller 1979) and the Kwiatkowski–Phillips–Schmidt–Shin test (Kwiatkowski et al. 1992). The best fit models for each species were evaluated using the Akaike information criterion (Akaike 1974). The best model from each species was tested using the later part of the data set, and the root-mean-squared error (RMSE) was used to evaluate model performance. Predictors were considered significant if the coefficient was more than two standard errors from zero.

A total of 964 cases were diagnosed with canine distemper among nine host species; these cases were submitted from 17 states over the 44-yr period (Table 1). A mean of 21.42 cases were diagnosed with distemper per year with a SD of 16.96. The highest annual number of cases was reported in 1988 (Fig. 1).

Table 1

Summary table of the species and state of wildlife cases diagnosed with canine distemper submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019, Athens, Georgia, USA.a

Summary table of the species and state of wildlife cases diagnosed with canine distemper submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019, Athens, Georgia, USA.a
Summary table of the species and state of wildlife cases diagnosed with canine distemper submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019, Athens, Georgia, USA.a
Figure 1

Total number of wildlife cases diagnosed with canine distemper for all species and all states per year submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Figure 1

Total number of wildlife cases diagnosed with canine distemper for all species and all states per year submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Close modal

Canine distemper cases in all species most often originated in southeastern states (93%), and cases tended to occur in the same or adjacent counties to those with raccoon cases (Supplementary Material Fig. S1). Supplementary Material Figure S2 shows the number of diagnosed canine distemper cases for each species from 1975 to 2019. The greatest number of cases throughout all years of study were submitted from Georgia, although from 2010 to 2019, the highest number was from Louisiana (Supplementary Material Fig. S3).

From 1975 to 2019, numbers of raccoon and gray fox distemper cases varied significantly over time; the greatest numbers per year, for both species, were seen in 1980–90 (n=441, mean=9.8, SD=11.82). The years 2000, 2007, 2008, and 2010 had no reported cases in gray foxes or raccoons in Georgia (Fig. 2).

Figure 2

Total number of raccoons (Procyon lotor) and gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper per year from Georgia that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Figure 2

Total number of raccoons (Procyon lotor) and gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper per year from Georgia that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Close modal

Most of the raccoon and gray fox cases in Georgia were received during the breeding seasons for these species (Zeveloff 2002). There was a significant association between breeding season and the number of cases, particularly in raccoons (Supplementary Material Fig. S4). There was a significant association between breeding season and number of distemper cases in both raccoons (χ2=21.20, P=2.49e–05) and gray foxes (χ2=11.46, P=0.003) in Georgia; cases occur disproportionately during the breeding season, with fewest cases occurring in the nonbreeding season.

Spatial analysis

The general pattern over the entire study period showed most cases occurring in counties in the northern part of Georgia around the population centers of Atlanta, Athens, and Augusta (Fig. 3). Additionally, high case numbers were documented in the southeastern part of the state, around Savannah. Ripley's K analysis of CDV cases in raccoons and gray foxes in Georgia showed that these cases are significantly clustered (Fig. 4). This pattern appeared again in case data stratified by year (Fig. 5), with cases occurring near these population centers, particularly in the 1980s. The northern part of the state had a cluster of cases involving many counties during the 1980s and early 1990s, with a smaller number of counties involved in the southeast. The data indicated increased frequency of distemper case submissions in medium to medium–high human-population–density counties (suburban), with fewer cases diagnosed in very high- (urban) and very low-(rural) density counties. This relationship was more pronounced for raccoons than for gray foxes (Fig. 6).

Figure 3

Total number of raccoons (Procyon lotor), and gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper per county in Georgia that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Figure 3

Total number of raccoons (Procyon lotor), and gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper per county in Georgia that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Close modal
Figure 4

Ripley's K analysis of the location of raccoons (Procyon lotor) or gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper in Georgia that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019. obs(r), solid line, is the Ripley's K statistic for the observed cases of raccoons and gray foxes combined. Ktheo(r), dashed line, is the K statistic for a completely random (Poisson) point process. hi(r) and lo(r), shading around dashed line, are the upper and lower envelopes for the Poisson simulation.

Figure 4

Ripley's K analysis of the location of raccoons (Procyon lotor) or gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper in Georgia that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019. obs(r), solid line, is the Ripley's K statistic for the observed cases of raccoons and gray foxes combined. Ktheo(r), dashed line, is the K statistic for a completely random (Poisson) point process. hi(r) and lo(r), shading around dashed line, are the upper and lower envelopes for the Poisson simulation.

Close modal
Figure 5

Presence of raccoons (Procyon lotor) or gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper per county in Georgia, USA per year that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Figure 5

Presence of raccoons (Procyon lotor) or gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper per county in Georgia, USA per year that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Close modal
Figure 6

The average human population density per square mile for each county in the state of Georgia, USA from 1975 to 2019 and the number of raccoons (Procyon lotor) and gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper, plotted as a county centroid coordinate, submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Figure 6

The average human population density per square mile for each county in the state of Georgia, USA from 1975 to 2019 and the number of raccoons (Procyon lotor) and gray foxes (Urocyon cinereoargenteus), diagnosed with canine distemper, plotted as a county centroid coordinate, submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019.

Close modal

Predictive model development

The best-performing model for predicting the number of gray fox cases, xt, used gray fox case numbers from t–1 and t–2 mo, the t–1 predictive error, and the current month's raccoon cases, yt (Fig. 7A). The best-performing model for predicting raccoon cases, yt used the t–1 raccoon cases and t–1 predictive error in addition to the number of gray fox cases from t, t–1, t–2 mo (Fig. 7B). The gray fox predictive model was the most accurate model with an RMSE of 0.2669 for the test data, meaning it more accurately predicted gray fox distemper cases from these data. The raccoon prediction had a higher RMSE (0.7290).

Figure 7

Best fit autoregressive integrated moving average model for predicting canine distemper cases in (A) gray foxes (Urocyon cinereoargenteus), and (B) raccoons (Procyon lotor) in the state of Georgia, USA, using the numbers of raccoons and gray foxes diagnosed with canine distemper per month that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019. The case data for gray foxes and raccoons are shown by the gray line, with the model prediction for the training period in black on the left of the figure. The predictions for the test period and beyond are the black line surrounded by shading on the right of the figure. The shading represents 80% and 95% prediction intervals.

Figure 7

Best fit autoregressive integrated moving average model for predicting canine distemper cases in (A) gray foxes (Urocyon cinereoargenteus), and (B) raccoons (Procyon lotor) in the state of Georgia, USA, using the numbers of raccoons and gray foxes diagnosed with canine distemper per month that were submitted to the Southeastern Cooperative Wildlife Disease Study, 1975–2019. The case data for gray foxes and raccoons are shown by the gray line, with the model prediction for the training period in black on the left of the figure. The predictions for the test period and beyond are the black line surrounded by shading on the right of the figure. The shading represents 80% and 95% prediction intervals.

Close modal

Analysis of this 45-yr diagnostic data set of canine distemper in wild carnivores for spatial and temporal trends found that nine species were diagnosed with canine distemper in 17 states, primarily concentrated in the southeastern US. Distemper cases in different species tended to cluster spatially. Ripley's K analysis carried out on raccoon and gray fox data from Georgia showed significant clustering of reported cases and an apparent association between urban gradient and number of cases, with more cases occurring in suburban counties surrounding major urban areas. There was a significant temporal association with the overlapping breeding season of foxes and raccoons (March–June) and higher numbers of cases.

Georgia reported the most cases among the 17 states, especially in the 1980s. The cause(s) of this upswing in submitted cases is not known but may include interest in rabies in wildlife at the time (Parham 1983; Davidson et al. 1992). Louisiana submitted the most cases in the 2010s, whether this was due to an actual increase in CDV epizootics or other factors related to willingness to collect and submit carcasses, detection, or reporting is unknown.

Spatial analysis showed significant clustering of distemper cases, probably reflecting viral transmission ecology. Viral persistence generally requires dense populations of susceptible hosts to facilitate viral spread, leading or contributing to epizootics (Williams 2001). The primary mode of CDV infection is aerosol inhalation, suggesting that habitat overlap and direct contact is important in transmission (Hoff et al. 1974). The immunizing nature of infection is useful for spatial and temporal analysis, as cases occurring in the same area but years later are likely to be due to a new outbreak that has spread back into the area after an increase in the susceptible population has exceeded the threshold required for an outbreak. This has been demonstrated with CDV in Serengeti lions as the number of susceptible animals increased during the years following an epizootic (Packer et al. 1999). Disease presence can be used for analysis in this scenario, as one can assume that a case corresponds to an outbreak (Nouvellet et al. 2013).

Cases of CDV in raccoons appeared to be more commonly submitted (and diagnosed) from suburban counties in Georgia, with Atlanta and more rural counties submitting fewer animals. This may be due to suburban areas being a hotspot for disease circulation, with these areas being attractive to raccoons due to the available habitat and easy scavenging opportunities. Raccoon populations can reach higher densities within urban and suburban environments (Prange et al. 2003). A similar pattern was reported from studies in Germany: Suburban and urban red foxes had a higher prevalence of CDV infection compared to their rural counterparts, but the CDV risk was reduced in highly urbanized areas in Berlin (Frolich et al. 2000; Gras et al. 2018). This is of particular relevance, as spillover of CDV infection from raccoons into domestic dogs has been suggested (Kapil and Yeary 2011).

There were many more distemper cases in the northern and northeastern part of Georgia than in other areas of the state, followed by the southeastern part. There are numerous possible reasons for this. Reporting biases may have played a role. For example, the northern part of the state is more densely populated by humans; it is also in closer proximity to SCWDS at the University of Georgia, and thus it may be more convenient to deliver carcasses. Further, this region may have more suitable habitat and thus more robust populations of raccoons and gray foxes. In addition, distemper cases in our study were possibly skewed toward those that involved obvious illness or death of wildlife. In contrast, one study reported that gray foxes and raccoons in Tennessee (November 2013–August 2014) were frequently infected, but passive surveillance only detected those with clinical signs; this failed to account for subclinically infected animals, with 55% of subclinically infected animals testing positive by real-time reverse transcription PCR assay (Pope et al. 2016).

The data also suggested a correlation between breeding season and the number of distemper cases diagnosed in raccoons and gray foxes, with cases more likely to occur during the breeding season. This could have been due to more frequent contact between individual animals as they search for mates, thereby promoting aerosol spread of virus. Similarly, rabies virus infection can spread quickly among raccoons when the virus is introduced during the breeding season (Reynolds et al. 2015). During the breeding season, susceptible animals are introduced to the population and can be infected when they interact for the first time with infected established members of the population. In addition, the physiological stress of reproduction may leave animals more susceptible to the virus (Hawley and Altizer 2011) or, as the cases in our study were from animals found moribund or dead, it could be that the increased movement during the breeding season leads to higher mortality rates from a variety of causes, such as vehicular collision, for which CDV may have contributed but was not the primary cause of death.

The ARIMA model for predicting monthly distemper cases in gray foxes could accurately predict this using the numbers of distemper cases in gray foxes from the previous 2 mo and in raccoons from the current month, suggesting that there was an association between cases in the two species. Initial introduction of CDV into wild carnivores in the US in the 1960s was through gray foxes with subsequent spread to raccoons (Hoff et al. 1974). Similarly, a distemper outbreak in raccoons in Berlin, Germany appears to have originated in foxes, with transmission seeming to occur readily among wildlife species (Renteria-Solis et al. 2014).

The raccoon prediction model also was accurate in predicting raccoon distemper cases, albeit with a larger predictive error than the gray fox prediction model. This suggested a possible association between gray fox cases in previous months and raccoon cases in the current month. However, as the data for the time series analysis and ARIMA model building were pooled for the whole state, it was possible that the cases contributing to the overall number per month could have been separated by hundreds of miles; thus, the cases may not have been related to each other. Although this was rare, the long-term data set covered a wide geographic region (i.e., the southeastern US and statewide across Georgia), and there were limitations to using such a large area to generate meaningful conclusions about temporal trends.

In both predictive models, the test data, which used the more recent data to test the accuracy of the model, had a lower error than the model training data. This can be explained by the variability of the case submission data over time. For example, the year-to-year variation in distemper case numbers among raccoons and gray foxes in Georgia was higher prior to 1996. The training data ran from April 1975 to July 2011, so the model had to contend with more variability in case numbers than for the test data (August 2011–December 2019), which probably reduced accuracy of the output. The higher RMSE in the raccoon prediction model may be explained by the larger degree of variability in submitted raccoon case numbers throughout the study.

In conclusion, this analysis suggests that CDV infection is widely distributed in the southeastern US; it was diagnosed in nine different carnivore species from 17 states, with raccoon and gray foxes being most commonly diagnosed with canine distemper. The other species diagnosed were generally from the same or adjacent counties to those with raccoon or gray fox cases. Within the most represented state, Georgia, further analysis indicated distinct temporal and spatial patterns of CDV cases in raccoons and gray foxes, with cases more likely to occur during the breeding season. Spatially there was clustering of cases of both species within the same areas, with cases tending to be focused in more suburban areas. Our results also suggested that numbers of gray fox and raccoon cases can be predicted using data on past cases in foxes and raccoons. Among wild carnivore species, raccoons are easiest to capture and sample, and thus may serve as a useful predictor for other, less tractable species.

Ultimately, there were sufficient potential relationships suggested from this passively collected data to support recommendation of targeted active surveillance, including comprehensive sampling of wild carnivores over time and space to try to elucidate spillover trends between species, particularly in suburban areas. Data from both active and passive surveillance systems can also inform predictive models (Gras et al. 2018), which may aid in management decisions that help to reduce disease incidence, and facilitate molecular epidemiological studies, which can uncover more in-depth relationships between the different host species (Packer et al. 1999; Kamath 2020; Piewbang et al. 2020), as well as clarify the directionality of cross-species transmission between different host species (Weckworth et al. 2020). This could also inform decision making regarding vaccination (Viana et al. 2015) and other strategies aimed at reducing transmission among both domestic and wild animals.

Supplementary material for this article is online at http://dx.doi.org/10.7589/JWD-D-20-00212.

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Author notes

7 Current address: Ross University School of Veterinary Medicine, Basseterre, St. Kitts, West Indies, KN-03

Supplementary data