Data

The data used to forecast are the same as those used in the prevalence maps. Data are obtained as aggregated counts of positive tests or total tests by county and month. These data can be seen in each of the raw prevalence maps (Figs. 1a, 2a, 3a, and 4a). Counts of positive tests and total tests performed in a given county from 2012 through 2018 were summed. The proportion of all positive tests to total tests in a given county is the overall prevalence for that time period. Heartworm antigen test results are obtained from IDEXX Laboratories, Inc. (SNAP® Heartworm RT Test, SNAP® 4Dx® Plus Test, and PetChek® Heartworm PF Antigen Test) and Antech Diagnostics (DiroCHEK® Heartworm Antigen Test and AccuPlex4 heartworm antigen detection assay). The remaining data are obtained from IDEXX (SNAP® 4Dx® Plus Test). Data are collected both from tests performed at the reference laboratories and from clinics that are connected to the laboratories via clinic practice management software. No measure of uncertainty is given for any test. Travel, disease, and testing histories of the dogs are also not available. Roughly half of the 3109 counties, boroughs, parishes, and other county-equivalent areas in the US do not report any tests in a given year; in contrast, some highly populated counties report thousands of tests annually. More details on the data can be found in Bowman et al. (Reference Bowman, Liu, McMahan, Nordone, Yabsley and Lund2016); Liu et al. (Reference Liu, Lund, Nordone, Yabsley and McMahan2017a, Reference Liu, Watson, Gettings, Lund, Nordone, Yabsley and McMahan2017b), and Watson et al. (Reference Watson, Liu, Lund, Gettings, Nordone, McMahan and Yabsley2017).

Fig. 1.

Raw canine Borrelia burgdorferi prevalence aggregated by county from 2012 through 2018 and forecasted B. burgdorferi prevalence for 2019. Counts for positive tests and for total tests performed in a given county from 2012 through 2018 were summed. The proportions of all positive tests to total tests in a given county are shown in (a). The expected seroprevalence of B. burgdorferi in 2019 is shown in (b).

Fig. 2.

Raw canine Anaplasma species prevalence aggregated by county from 2012 through 2018, and forecasted Anaplasma spp. prevalence for 2019. Counts for positive tests and for total tests performed in a given county from 2012 through 2018 were summed. The proportions of all positive tests to total tests in a given county are shown in (a). The expected seroprevalence of Anaplasma spp. in 2019 is shown in (b).

Fig. 3.

Raw canine Ehrlichia species prevalence aggregated by county from 2012 through 2018, and forecasted Ehrlichia spp. prevalence for 2019. Counts for positive tests and for total tests performed in a given county from 2012 through 2018 were summed. The proportions of all positive tests to total tests in a given county are shown in (a). The expected seroprevalence of Ehrlichia spp. in 2019 is shown in (b).

Fig. 4.

Raw canine heartworm prevalence aggregated by county from 2012 through 2018, and forecasted heartworm prevalence for 2019. Counts for positive tests and for total tests performed in a given county from 2012 through 2018 were summed. The proportions of all positive tests to total tests in a given county are shown in (a). The expected prevalence of heartworm in 2019 is shown in (b).

Covariates

The statistical model described in the next section uses many putative risk factors to improve the annual prevalence forecasts. Eight risk factors (covariates or predictor variables) are considered: annual mean temperature, annual mean precipitation, annual mean relative humidity, elevation, percent forest coverage, percent water coverage, population density, and median household income. The listed factors were previously selected based on similar models. See Wang et al. (Reference Wang, Bowman, Brown, Harrington, Kaufman, McKay, Nelson, Sharp and Lund2014) for more information. Most of these factors are available to a county spatial resolution. In addition to these eight factors, the heartworm forecast also uses the presence/absence of eight mosquito species: Aedes albopictus, A. aegypti, A. canadensis A. sierrensis, A. trivittatus, Anopheles punctipennis, Anopheles quadrimaculatus, and Culex quinquefasciatus.

Data for the proposed risk factors were obtained from a variety of sources. Sources for all forecasts made up to 2017 are discussed in Wang et al. (Reference Wang, Bowman, Brown, Harrington, Kaufman, McKay, Nelson, Sharp and Lund2014). The forecast models are updated annually. As such, covariate data sources are periodically updated. This occurs for multiple reasons: prior sources become unavailable, data accuracy or spatio-temporal resolution is improved, new knowledge is gained about potential risk factors, or data acquisition becomes easier. In an effort to provide the most reliable and up to date information, covariate sources were updated for our 2018 and 2019 forecasts presented below.

As one would expect, changing covariate sources can change the significance level of the individual covariates and the forecast. However, the changes are usually not large. For clarity, the significance of factors based on 2011–2015 data will be reported. These years are chosen to be consistent with the data used in publications for each pathogen: B. burgdorferi (Watson et al., Reference Watson, Liu, Lund, Gettings, Nordone, McMahan and Yabsley2017), Anaplasma spp. (Liu et al., Reference Liu, Watson, Gettings, Lund, Nordone, Yabsley and McMahan2017b), Erhlichia spp. (Liu et al., Reference Liu, Lund, Nordone, Yabsley and McMahan2017a), and heartworm (Bowman et al., Reference Bowman, Liu, McMahan, Nordone, Yabsley and Lund2016). These studies used the most recent data available at the time of publication. For B. burgdorferi and Anaplasma spp., elevation, percent forest coverage, and percent water coverage are positively related to prevalence; annual temperature and population density are negatively related to prevalence; annual precipitation, annual relative humidity, and median household income are statistically insignificant. For Ehrichlia spp., annual temperature, percent forest coverage, and percent water coverage are positively related to prevalence; population density and median household income are negatively related to prevalence; and annual precipitation, annual relative humidity, and elevation are insignificant. For heartworm, annual temperature, annual relative humidity, and percent forest coverage are positively related to prevalence; population density, median household income, and presence of A. albopictus are negatively related to prevalence; and annual precipitation, elevation, percent water coverage, and presence of A. aegypti, A. canadensis A. sierrensis, A. trivittatus, A. punctipennis, A. quadrimaculatus, and C. quinquefasciatus are insignificant.

Forecast model

This technical section describes the statistical methods used in making the forecasts. For notation, let Y _st and n _st denote the number of positive and total tests reported in county s during year t, respectively. In general, counties that are geographically close to each other often report a similar prevalence. Similarly, a fixed county tends to report a similar disease prevalence in consecutive years. These phenomena entail positive spatio-temporal correlation and need to be accounted for in an accurate statistical analysis (Besag, Reference Besag1974; Martínez-Beneito et al., Reference Martínez-Beneito, López-Quilez and Botella-Rocamora2008; López-Quilez and Munoz, Reference López-Quilez and Munoz2009; Banerjee et al., Reference Banerjee, Carlin and Gelfand2014).

Our forecast model is Bayesian and accounts for spatio-temporal dependence using random effects as described below. In the model, the number of positive tests in each county and year is assumed to conditionally follow a Poisson distribution, which is a common choice for modeling count data (Martínez-Beneito et al., Reference Martínez-Beneito, López-Quilez and Botella-Rocamora2008; López-Quilez and Munoz, Reference López-Quilez and Munoz2009). Specifically, Y _st is assumed to be statistically distributed as

(1)$$Y_{{\rm st}} \vert n_{{\rm st}},p_{{\rm st}}\sim {\rm Poisson}\lpar {n_{{\rm st}}p_{{\rm st}}} \rpar,$$

where

(2)$$\log \lpar {\,p_{{\rm st}}} \rpar = \beta _0 + \mathop \sum \limits_{k = 1}^p \beta _kX_{k{\rm st}} + \xi _{{\rm st}},$$

where X _kst is the kth covariate at county s and time t, β _k is the corresponding regression coefficient, p _st is the prevalence, and ξ _st are the spatio-temporal random effects. Equation (1) posits that the number of positive tests Y _st, given the number of tests conducted n _st and the prevalence p _st, follows a Poisson distribution with mean n _stp _st.

There are many model choices for the spatio-temporal random effects. Perhaps the most popular for spatial data is the conditional autoregression (CAR) model (Banerjee et al., Reference Banerjee, Carlin and Gelfand2014), which is used here. Spatial and temporal dependence is modeled by embedding a CAR model in the following first-order temporal autoregressive scheme:

(3)$$\eqalign{{\bf \xi} _1 = &{\bi \phi} _{1} \cr {\bf \xi} _t = &\varphi {\bf \xi} _{t-1} + {\bi \phi} _{t{\rm \;}}, \;{\rm for}\,t \ge 2 \cr {\bi \phi} _t\sim &{\rm CAR}\lpar {\tau^2,\rho} \rpar,\,{\rm for}\,t \ge 1,} $$

where ξ_t = (ξ _1t, …, ξ _St), and where ϕ_t = (ϕ _1t, …, ϕ _St). Time-dependence is accounted for with a first-order temporal autoregression, which is commonly used to model temporal data (Brockwell and Davis, Reference Brockwell and Davis2002). The parameter φ lies between −1 and 1 and controls the level of temporal correlation between consecutive years.

Under our spatio-temporal model, the ϕ_t's are independent and identically distributed random vectors in time t whose distribution follows a CAR model (Besag, Reference Besag1974; Banerjee et al., Reference Banerjee, Carlin and Gelfand2014). Typically, CAR models are defined by specifying a univariate conditional distribution for the random effect at each county s. The mean and variance of this conditional distribution depend on the spatial relationship between the counties. Our forecast model uses the CAR model variant proposed in Besag (Reference Besag1974):

$$\phi _{{\rm st}} \vert {\bi \phi} _{-{\rm st}},\tau ^2,\rho \sim {\rm Normal}\left( {\displaystyle{\rho \over {d_s}}\mathop \sum \limits_{{s}^{\prime}\ne s} w_{{s^\prime}s}\phi_{s^{\prime}}, \displaystyle{{\tau^2} \over {d_s}}} \right),\;{\rm for}\,{\rm all}\,s.$$

Here, ϕ_−st denotes a random vector that includes every component of ϕ_t except for the sth component ϕ _st, Normal(μ, σ ²) indicates the normal distribution with mean μ and variance σ ², w _s′s is equal to 1 if counties s and s′ border each other (and is 0 otherwise), and d _s is the number of counties that border county s. The parameter τ ² scales the variance structure of the CAR process. Note that the conditional variance of ϕ _st is inversely proportional to the number of counties bordering it. Thus, if a county has more neighbors, its random effect has a smaller variance. This is desirable because if a county has many neighbors, there is more information to use to estimate its random effect. The parameter ρ is also between 0 and 1 and controls the amount of correlation between bordering counties. As ρ increases, so does the degree of dependence between the random effects in neighboring counties. It can be shown that the model specifications above define a valid joint distribution for ϕ_t.

Our model specification is Bayesian in nature. To completely specify the model, prior distributions need to be assigned. The exact prior distributions used are listed in the previous forecasting publications (Bowman et al., Reference Bowman, Liu, McMahan, Nordone, Yabsley and Lund2016; Watson et al., Reference Watson, Liu, Lund, Gettings, Nordone, McMahan and Yabsley2017; Liu et al., Reference Liu, Lund, Nordone, Yabsley and McMahan2017a, Reference Liu, Watson, Gettings, Lund, Nordone, Yabsley and McMahan2017b). Diffuse prior distributions were placed on the β _k parameters. Diffuse priors have little to no influence on the posterior distribution, thus allowing the data to primarily drive inference and forecasting. Uninformative (flat) prior distributions are assigned to φ and ρ for the same reasons. Uninformative priors assign prior probability evenly over all feasible values of the parameter. The prior for τ ² is chosen as a conjugate prior, which means that the posterior and prior distributions are from the same distributional family, a computationally simplifying but reasonable assumption. A Markov chain Monte Carlo (MCMC) posterior sampling algorithm was developed to sample all model parameters and random effects from their posterior distributions. The Y _st for counties not reporting test results were treated as latent (missing) variables and were sampled along with the unknown model parameters. For more on Bayesian models and MCMC methods, see Gelman et al. (Reference Gelman, Carlin, Stern and Rubin2014).

To make a forecast, the model is first fit using all available test and covariate data for a fixed pathogen. The significance of covariate factors (whether or not they should be in the model) is judged using 95% Bayesian credible intervals. All insignificant variables were removed resulting in a reduced model consisting of only significant variables; for specific details on how this was done, see Bowman et al. (Reference Bowman, Liu, McMahan, Nordone, Yabsley and Lund2016), Liu et al. (Reference Liu, Lund, Nordone, Yabsley and McMahan2017a, Reference Liu, Watson, Gettings, Lund, Nordone, Yabsley and McMahan2017b), and Watson et al. (Reference Watson, Liu, Lund, Gettings, Nordone, McMahan and Yabsley2017).

Once the model has been fit, all significant covariates and spatio-temporal random effects are forecasted for the forthcoming year. Forestation, water coverage, mosquito species presence/absence, and elevation are assumed constant from year to year and do not need to be forecasted. These covariates are updated occasionally (e.g. when a new National Land Cover Database is released). Thus, the covariates that need to be forecasted for each county are annual temperature, annual relative humidity, annual precipitation, median household income, and population density. How this is done is described in Bowman et al. (Reference Bowman, Liu, McMahan, Nordone, Yabsley and Lund2016), Liu et al. (Reference Liu, Lund, Nordone, Yabsley and McMahan2017a, Reference Liu, Watson, Gettings, Lund, Nordone, Yabsley and McMahan2017b), and Watson et al. (Reference Watson, Liu, Lund, Gettings, Nordone, McMahan and Yabsley2017).

To forecast the spatial and temporal random effects a year in advance, Equation (3) is used. The ϕ_t's are independent and identically distributed over time given τ ² and ρ, which are sampled from the posterior distribution. A realization of ϕ_t+1 is generated randomly from the fitted CAR model structure; one next sets ${\bf \xi} _{t + 1} = \varphi {\bf \xi} _t + {\bi \phi} _{t + 1}$. This process is repeated for each quartet of ${\bf \xi} _t,\tau ^2,\rho, \varphi $ from the posterior sample, thus yielding a sample of the forthcoming year's random effects. For further details, see Harrison and West (Reference Harrison and West1996) and Gelman et al. (Reference Gelman, Carlin, Stern and Rubin2014). A posterior sample of next year's forecasted prevalence, p _s,t+1, is then calculated from (2) using the posterior sample of the β _k's and the forecasted values of X _s,k,t+1 and ξ _s,t+1. Point estimates of the forecasted prevalence can be generated by taking the mean or median of the posterior sample of next year's prevalence.

Weighted correlation will be used to assess the accuracy of the forecasting methods. To compare the observed and forecasted county prevalence sets, a weighted correlation is computed. The contribution of each county to this correlation is weighted by the number of tests reported in that county; thus, counties not reporting data do not influence this correlation. The weighted correlation between two sets of county prevalences – say A = {a ₁, a ₂, …, a _S} and B = {b ₁, b ₂, …, b _S) – is defined as

$${\rm Corr}\lpar {A,B} \rpar = \displaystyle{{\mathop \sum \nolimits_{s = 1}^S h_s\lpar {a_s-a} \rpar \lpar {b_s-b} \rpar } \over {\sqrt {\mathop \sum \nolimits_{s = 1}^S h_s{\lpar {a_s-a} \rpar }^2\mathop \sum \nolimits_{s = 1}^S h_s{\lpar {b_s-b} \rpar }^2}}}, $$

where h _s is the total number of tests reported in county s and the weighted means are

$$a = \displaystyle{{\mathop \sum \nolimits_{s = 1}^S h_sa_s} \over {\mathop \sum \nolimits_{s = 1}^S h_s}};\quad b = \displaystyle{{\mathop \sum \nolimits_{s = 1}^S h_sb_s} \over {\mathop \sum \nolimits_{s = 1}^S h_s}}.$$

The interpretation of a weighted correlation is identical to that of the usual correlation, except that it accounts for unequal sample sizes through the weights. In particular, the more tests a county reports, the stronger the penalty is for deviations between the forecasted and observed prevalences. To create a final forecast map, the forecasted county prevalences are smoothed using Kriging, an interpolation method (Stein, Reference Stein2012), using the default settings within ArcGIS (ESRI, 2016).

Forecasting fidelity assessment

To measure forecasting accuracy in 2018, forecasts for each pathogen were made from 2012–2017 data. The forecasts were then compared to the observed 2018 data via a weighted correlation as described in the preceding section. The same process has been completed for the 2016 and 2017 forecasts. Table 1 displays weighted correlations for each pathogen. Heartworm prevalence was the most difficult to predict and likely attributable to the complexity of factors regulating mosquito populations (Jian et al., Reference Jian, Silvestri, Brown, Hickman and Marani2014), but still had a correlation of around 0.78. The weighted correlation for the three tick-borne pathogens exceeded 0.98.

Table 1.

Forecast fidelity assessment

The table displays the weighted correlation between the observed prevalence and forecasted prevalence. This weighted correlation is similar to ordinary correlation, but here the contribution of each county to the overall correlation is weighted by the number of tests that each county reports. Thus, counties which report many tests contribute more to the correlation than counties which report only a few tests, and counties which do not report tests do not contribute at all. For details on how the weighted correlation is calculated, see Watson et al. (Reference Watson, Liu, Lund, Gettings, Nordone, McMahan and Yabsley2017).

Evidence-based preventive medicine

The cornerstone of companion animal general practice is preventive medicine. Annual wellness examinations are recommended by most veterinarians to address a multitude of health concerns: nutrition, oral health, parasite control, vaccinations, and screening tests, etc. Administration of preventive medication, vaccines, and testing is based on the standards of practice and disease risk levels. Here, two methods are presented for assessing the exposure risk for four canine vector-borne pathogens. Prevalence maps provide a local, real-time quantification of risk, while annual forecasts allow for preventive care recommendations to be proactively strengthened. Historically, the management of emerging infectious diseases has been retroactive, with the response coming after the disease significantly affects the population. With forecasts, higher than normal prevalence can be a priori predicted, allowing veterinarians to modify their preventative care protocols as needed. The changes (often increased prevalence) between the baseline prevalence and the forecasted year serve to highlight the areas in which veterinarians should be alerted to a possible change in the risk of exposure to their patients. Slowly changing risk, as can be the case with VBD, can be difficult to monitor year to year. The modeling techniques utilized by the forecasts are more reliable with respect to accurately detecting these changes when compared to examining the prevalence maps alone. Additionally, approximately one-third of the counties in the US do not currently have these test data reported. As part of the model-building, the missing data from these counties are interpolated using known data from surrounding counties. As a result, the forecast maps allow us to predict the risk in areas that would otherwise have no information.

Both prevalence and forecast maps aid risk communication with clients. Effective communication skills are critical for the education of clients when making recommendations for preventive care and are essential for strengthening the veterinarian–client bond (Lue et al., Reference Lue, Pantenburg and Crawford2008). From the client perspective, a clear understanding of recommendations is essential as up to 30% of clients do not follow recommendations when they lack clarity on the necessity of the intervention (Lue et al., Reference Lue, Pantenburg and Crawford2008). Communication can be improved by supplementing conversations with written instructions, prepared handouts, and other visual aids (Coe et al., Reference Coe, Adams and Bonnett2008). Both the prevalence and forecast maps are available for this purpose in a simple format and on multiple platforms. The ability to display the maps on hand-held devices allows consultation of the maps in the exam room. Local prevalence statistics provide veterinarians an opportunity to engage clients in data-driven conversations about the risk of VBD.

Within the context of the veterinarian–client conversation, it is important to incorporate the pet's individual risk of exposure to vectors. Clients expect a personalized approach (Coe et al., Reference Coe, Adams and Bonnett2008), which ensure that the recommended preventive care measures are appropriate for that pet. Veterinarians should ask whether the pet spends any time in favored tick habitats (areas with tall grass, leaf litter, woods, and brush), including areas around the client's home, and about past and future travel. Travel with pets, particularly dogs, has increased significantly in the last decade, with 37% of clients reporting pets accompany them on trips (American Pet Products Association, 2017). This is especially important when a client is traveling from a non-endemic to an endemic region. To help facilitate this, CAPC provides prevalence maps at the national, state, and county level for in-clinic communication efforts as the knowledge of vector-borne pathogens outside the home county may be limited for the veterinarian or client.

Unambiguous risk communication leads to a mutual understanding of an individual pet's risk. This allows veterinarians to further educate clients on why the proactive management of VBD is important. Preventative measures vary by the pathogen of concern and currently include annual testing with pet-side assays such as those for D. immits, Ehrlichia spp., Anaplasma spp., and B. burgdorferi, administration of acaricides to reduce the exposure to tick-borne pathogens, anthelmintics to prevent the development of D. immitis, and when appropriate, vaccination against B. burgdorferi. Surveys suggest that owners become confused about the value of services and products offered during wellness examination when offered a multitude of choices (Lue et al., Reference Lue, Pantenburg and Crawford2008). If overwhelmed, clients usually choose fewer services. Explaining the value of services is cited as being perceived by clients as increasing the quality of care provided during annual wellness exams. In particular, communication about the cost-savings of preventing infection, early detection of disease, prevention of long-term health complications, and early treatment become invaluable in fostering the veterinarian–client relationship. Average costs range from $215 (US$ 2017) (Nationwide Insurance, personal communication) to $1000 (2017 US$) (Bowman and Atkins, Reference Bowman and Atkins2009; Colby et al., Reference Colby, Levy, Dunn and Michaud2011; Maxwell et al., Reference Maxwell, Ryan, Reynolds and Pariaut2014; Wolstenholme et al., Reference Wolstenholme, Evans, Jimenez and Moorhead2015) depending on the infection; however, not all animals that test positive for the various pathogens are treated. In the case of anaplasmosis, the majority of seropositive dogs are subclinical. Therefore, current recommendations for veterinary care in Anaplasma spp. seroreactive dogs include a complete blood count with platelet count to determine if treatment is necessary (Little, Reference Little2010).

Unfortunately, no consolidated reports exist regarding the industry-level annual financial impact of VBD in companion animals. As a result, the financial burden of VBD is difficult to estimate. Lyme disease treatment is approximately $215 per pet (Nationwide Insurance, personal communication). When aggregated across the veterinary healthcare system, the annual cost likely lies somewhere between $3.3 and $54 million, with the low estimate representing the percentage of seropositive animals that were treated based on insurance claims (Nationwide Insurance, personal communication), and the high estimate representing the treatment of all seropositive animals. Similar estimates can be made for the rickettsial diseases, with both ehrlichiosis and anaplasmosis having average treatment costs of $221 per patient (Nationwide Insurance, personal communication). From this, treatment for ehrlichiosis is estimated to range from $1.1 to $23 million, and anaplasmosis from $1.1 and $25 million. Finally, the cost for treatment for heartworm infection is by far the most significant, averaging $400–$1000 per patient (Bowman and Atkins, Reference Bowman and Atkins2009; Colby et al., Reference Colby, Levy, Dunn and Michaud2011; Maxwell et al., Reference Maxwell, Ryan, Reynolds and Pariaut2014; Wolstenholme et al., Reference Wolstenholme, Evans, Jimenez and Moorhead2015) and costing US pet owners approximately $75 million annually. Collectively, the burden of VBD on the veterinary health care sector is estimated to be $80.5–$177 million annually, and the costs of treatment may change over time. Note that these estimates are based on just one of many insurance companies and may not represent out-of-pocket costs and those spent on complicated cases. The broad range of estimates for the financial impact of VBD highlights an opportunity for a subsequent analysis of cost that is inclusive of all stakeholders, including the pet insurance industry, corporate veterinary groups and regional private practices.

Despite our limitations in clearly defining financial impact, the financial burden of treating infected animals supports the need for anthelmintics, acaricides, vaccinations, and testing. For veterinarians, there is an obvious cost-to-benefit and financial saving to pet owners who opt for prevention, yet most veterinarians face challenges with preventive compliance. Administration of preventive medications is generally recommended year-round to prevent any exposure windows (Nelson et al., Reference Nelson, McCall and Carithers2014; Companion Animal Parasite Council, 2018; US Food and Drug Administration, 2018); yet, the majority of dogs only receive seasonal treatment, averaging 6–8 months of heartworm prevention (Gates and Nolan, Reference Gates and Nolan2010). Only 64% of clients use parasiticides on a year-round basis (American Pet Products Association, 2017). Similarly, in a recent survey, only 62% of owners recalled their veterinarian's recommendation of 12-month coverage for flea and tick prevention (Lavan et al., Reference Lavan, Tunceli, Zhang, Normile and Armstrong2017). The seasonal window for exposure risk depends on the phenology (seasonality) of the vectors, which is influenced by dynamic climatological factors (Eisen et al., Reference Eisen, Eisen, Ogden and Beard2015) and can vary temporally and geographically, even within a small region of a state. Attempting to predict this window and only provide prevention could result in inadvertent exposure that could have been easily prevented with year-round administration.

Beyond preventive treatments, veterinarians practicing in areas of high B. burgdorferi seroprevalence may also recommend vaccination against B. burgdorferi. The maps are useful in identifying endemic regions of Lyme disease to determine when a Lyme disease vaccine should be recommended, with the additional consideration of having a discussion with clients about frequent travel with pets to well-established endemic regions such as the Northeast and Central Upper Midwest. Veterinarians in regions between high- and low-prevalence areas could use the prevalence and forecast maps in conjunction with knowledge of the individual dog to establish a cost-to-benefit of vaccination. Guidelines for the prevention and treatment of VBD and a broad range of parasitic diseases are accessible at http://www.capcvet.org/guidelines/, recent guidelines from the American College of Veterinary Internal Medicine on Lyme borreliosis in dogs are available (Littman et al., Reference Littman, Gerber, Goldstein, Labato, Lappin and Moore2018), and guidelines on the prevention, diagnosis, and treatment of heartworm are available from the American Heartworm Society (Nelson et al., Reference Nelson, McCall and Carithers2014). Despite the availability of preventative medications and vaccines, pets are never 100% protected against exposure (Vogt et al., Reference Vogt, Sargeant, MacKinnon and Versluis2019). Therefore, annual screening tests are recommended (Nelson et al., Reference Nelson, McCall and Carithers2014; Companion Animal Parasite Council, 2018; US Food and Drug Administration, 2018). Furthermore, additional testing data improve the fidelity of the forecasts. Reporting of test results back to the diagnostic company greatly aids VBD surveillance, particularly when in-clinic tests are utilized.

Finally, most practitioners recognize a shift in veterinary–client interactions toward a consumer-centric model where clients are frequently empowered consumers and prefer to have a voice in their pet's healthcare decisions. Pet owners often enter the exam room armed with information and opinions on optimal care for their pets. Sometimes, clients' preconceived ideas are at odds with the practitioner's training and knowledge base, with the source of information used by clients often coming from Internet sources of questionable validity. To satisfy growing consumer demands for information, VBD forecast and prevalence maps are available at the CAPC consumer-focused website www.petsandparasites.org. These maps provide accurate information on disease prevalence, with an emphasis that their veterinarian is the local expert on VBD testing, prevention, and treatment. Communication of science to the general public is increasingly recognized as a core responsibility of scientists and health care providers; ultimately, the presentation of accurate information in jargonless language allows clients to make informed decisions about prevention measures.

One Health

Often, the VBD conversation between veterinarians and clients extends to the human risk and the health and well-being of clients and their family. Many vector-borne pathogens infect both people and dogs, including B. burgdorferi, E. chaffeensis, E. ewingii, A. phagocytophilum, and A. platys. A recent study found that tick-infested dogs can transfer ticks directly to human hosts during interactions and serve as transport hosts, carrying ticks in and around dwellings (Jones et al., Reference Jones, Hinckley, Hook, Meek, Backenson, Kugeler and Feldman2018). For these zoonotic pathogens, the CVBD maps suggest where human beings are at risk of exposure (Liu et al., Reference Liu, Nordone, Yabsley, Lund, McMahan and Gettings2019). Unfortunately, routine testing is not conducted for many other vector-borne pathogens of relevance to people and dogs and general surveillance is lacking. However, one may still derive pathogen risk information when a vector of one of the mapped pathogens is shared. For example, co-infections of Ixodes scapularis with multiple pathogens (e.g. B. burgdorferi, Borrelia miyamotoi, A. phagocytophilum, Babesia microti, and Bartonella spp.) are common (Adelson et al., Reference Adelson, Rao, Tilton, Cabets, Eskow, Fein, Occi and Mordechai2004; Diuk-Wasser et al., Reference Diuk-Wasser, Vannier and Krause2016), and infection with Ehrlichia species and several other vector-borne pathogens such as Rickettsia and Bartonella have been concurrently observed in people and dogs (Kordick et al., Reference Kordick, Breitschwerdt, Hegarty, Southwick, Colitz, Hancock, Bradley, Rumbough, Mcpherson and MacCormack1999). Wildlife is the reservoir for many vector-borne pathogens and the environment is a critical factor in determining the emergence of those pathogens (Daszak et al., Reference Daszak, Cunningham and Hyatt2001), highlighting the incredible importance of One Health, not just public health, in the prevention of VBD in all hosts. Inclusion of information about family risk serves to foster client relationships and illustrates the professional commitment of veterinary practitioners to pets and their owners.

Vector-borne disease research

Vector information, such as endemic regions and abundance, are critical for understanding VBD establishment and spread. Unfortunately, accurate data on vector populations are currently economically and logistically unfeasible to collect, and such data deficiencies will likely continue to limit micro-scale VBD knowledge. Given the logistical and financial barriers, targeting vector-specific research to emerging areas generally has the greatest impact. Forecast estimates can be used to guide study designs for regional comparison of vector presence and abundance, comparative studies in zoonotic disease research relating human incidence to companion animal disease prevalence, and VBD research comparing different canine populations.

Examples of the impact of VBD mapping on research are evident in the literature. For example, an annual analysis of VBD has revealed regional foci of infections that were inexplicable in the context of what was currently known about the pathogens or present vectors. Specifically, in 2009, researchers were surprised by the foci of seroreactivity to Ehrlichia spp. in dogs in Wisconsin and Minnesota, a region where E. chaffeensis is not present or is only present in low prevalence (Bowman et al., Reference Bowman, Little, Lorentzen, Shields, Sullivan and Carlin2009). Investigators reasoned that there may be foci of intense E. canis transmission in these specific regions, or alternately, exposure of dogs to a novel Ehrlichia spp. that has yet to be described. Soon after, researchers identified four human cases of ehrlichiosis in Minnesota and Wisconsin that were not caused by E. chaffeensis or E. ewingii, but instead by a newly discovered Ehrlichia species, ultimately named E. muris euclairensis (Pritt et al., Reference Pritt, Sloan, Johnson, Munderloh, Paskewitz, McElroy, McFadden, Binnicker, Neitzel, Liu, Nicholson, Nelson, Franson, Martin, Cunningham, Steward, Bogumill, Bjorgaard, Davis, McQuiston, Warshauer, Wilhelm, Patel, Trivedi and Eremeeva2011). By 2012, researchers identified a dog infected with E. muris euclairensis in the same region (Hegarty et al., Reference Hegarty, Maggi, Koskinen, Beall, Eberts, Chandrashekar and Breitschwerdt2012), illustrating that anomalies can be early indicators of an emerging disease. In another example, unexpected increases in Anaplasma spp. seroprevalence have been observed in Texas, notably on the Mexican border and along the western edge of the state. These observations are notable given the recent study by Movilla et al., who documented canine seroprevalence of Anaplasma spp. in several states in Mexico (Movilla et al., Reference Movilla, García, Siebert and Roura2016) (the highest seroprevalence occurs in northwestern Mexico at 16.4%, and the lowest occurs in the north-central states of the country at 0.6%).

It is important for map users to understand that the estimates shown in the maps are based on historical data. As such, rapidly occurring events are unlikely to be predicted. This is less of a concern with tick-borne disease, which generally exhibits slow spread and is not prone to rapid, large-scale epidemics. However, forecast models of tick-borne pathogens may be improved by the inclusion of predictors shown to forecast the distribution and densities of ticks (Feria et al., Reference Feria-Arroyo, Castro-Arellano, Gordillo-Perez, Cavazos, Vargas-Sandoval, Grover, Torres, Medina, de León and Esteve-Gassent2014; Brugger et al., Reference Brugger, Walter, Chitimia-Dobler, Dobler and Rubel2018). In contrast, mosquito vectors of D. immitis may be strongly influenced by short-term disturbances in weather patterns, and notably in abundance of bodies of water, both standing and transitory (Valle et al., Reference Valle, Zaitchik, Feingold, Spangler and Pan2013). Until a better understanding of the ecology of mosquito-borne disease transmission at a fine spatial and temporal scale is achieved, it will be difficult to forecast short-term changes.

There is still much to learn about the epidemiology of canine vector-borne infections. Novel pathogens are continually being discovered in dogs and people. Additional organisms are detected in wildlife species that could one day emerge in other hosts. These new pathogens pose diagnostic challenges and generally are poorly understood in regard to vector(s) used, reservoir host(s) of importance, and clinical disease risks. Regarding modeling, future studies are needed to produce long-term forecasts, develop models that can define areas that are experiencing increasing/decreasing prevalence in both space and time, understand the impact of natural disasters on prevalence, understand the prevalence of disease, not just exposure, within the population, and ultimately evaluate the application of these data to forecast human exposure risk. Canadian testing data has been recently made available and future studies will seek to include these data. Veterinarians in Canada use similar testing practices, allowing us to expand the forecasts to include Canada.