Forecasting seasonal influenza activity in Canada-Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness.

INTRODUCTION: Public health preparedness is based on timely and accurate information. Time series forecasting using disease surveillance data is an important aspect of preparedness. This study compared two approaches of time series forecasting: seasonal auto-regressive integrated moving average (SARIMA) modelling and the artificial neural network (ANN) algorithm. The goal was to model weekly seasonal influenza activity in Canada using SARIMA and compares its predictive accuracy, based on root mean square prediction error (RMSE) and mean absolute prediction error (MAE), to that of an ANN. METHODS: An initial SARIMA model was fit using automated model selection by minimizing the Akaike information criterion (AIC). Further inspection of the autocorrelation function and partial autocorrelation function led to 'manual' model improvements. ANNs were trained iteratively, using an automated process to minimize the RMSE and MAE. RESULTS: A total of 378, 462 cases of influenza was reported in Canada from the 2010-2011 influenza season to the end of the 2019-2020 influenza season, with an average yearly incidence risk of 20.02 per 100,000 population. Automated SARIMA modelling was the better method in terms of forecasting accuracy (per RMSE and MAE). However, the ANN correctly predicted the peak week of disease incidence while the other models did not. CONCLUSION: Both the ANN and SARIMA models have shown to be capable tools in forecasting seasonal influenza activity in Canada. It was shown that applying both in tandem is beneficial, SARIMA better forecasted overall incidence while ANN correctly predicted the peak week.

Investigating the association between socioeconomic and agricultural risk factors and the incidence of Salmonella Heidelberg and Salmonella Typhimurium in Ontario in 2015: A multi-level ecological approach.

The incidence of salmonellosis, and other enteric zoonotic diseases, has been associated with various food and environmental exposures and socioeconomic factors. Increasingly, there is interest in exploring differences among serotypes of Salmonella to better inform public health prevention efforts. Consequently, we investigated whether rates of Salmonella Heidelberg and Typhimurium in Ontario communities in 2015 were influenced by household characteristics, agricultural factors, and the presence of meat plants. Data for each reported case of S. Heidelberg and S. Typhimurium in Ontario in 2015 were collected. Expected cases of each serotype were calculated, stratified by age group and sex, according to the underlying population distribution from the Canadian census. Socioeconomic, agricultural census data, and data concerning provincial and federally inspected meat plants were combined with observed and expected case counts. The association between community-level agricultural, meat processing, and socioeconomic variables, serotype, and the rate of salmonellosis in each census subdivision (CSD) was explored using multilevel Poisson models, with random intercepts for CSD and census division (CD). Rates of S. Heidelberg and S. Typhimurium were associated with the proportion of married individuals in a CSD, and were higher in CSDs with the highest quantile of labour participation compared to those in the lowest quantile. There was an interaction effect between cattle, poultry and swine farm density in a CD and serotype, with rates of either serotype decreasing as cattle, poultry, or swine farm density in the encompassing CD increased. The rate of the decrease varied by serotype. Our findings concerning community-level household characteristics may be explained by the influence of family structure and occupation on food consumption patterns and environmental exposures. Rates of S. Heidelberg and S. Typhimurium may be lower in areas with increased animal farm density due to naturally acquired immunity from routine exposure to Salmonella via livestock.

Comparing the diagnostic performance of ordinary, mixed, and lasso logistic regression models at identifying opioid and cannabinoid poisoning in U.S. dogs using pet demographic and clinical data reported to an animal poison control center (2005-2014).

Researchers have begun studying the impact of human opioid and cannabinoid use on dog populations. These studies have used data from an animal poison control center (APCC) and there are concerns that due to the illicit nature and social stigma concerning the use of these drugs, owners may not always be forthcoming with veterinarians or APCC staff regarding pet exposures to these toxicants. As a result, models derived from APCC data that examine the predictability of opioid and cannabinoid dog poisonings using pet demographic and health disorder information may help veterinarians or APCC staff more reliably identify these toxicants when examining or responding to a call concerning a dog poisoned by an unknown toxicant. The fitting of epidemiologically informed statistical models has been useful for identifying factors associated with various health conditions and as predictive tools. However, machine learning, including lasso regression, has many useful features as predictive tools, including the ability to incorporate large numbers of independent variables. Consequently, the objectives of our study were: 1) identify pet demographic and health disorders associated with opioid and cannabinoid dog poisonings using ordinary and mixed logistic regression models; and 2) compare the predictive performance of these models to analogous lasso logistic regression models. Data were obtained from reports of dog poisoning events collected by the American Society for the Prevention of Cruelty to Animals' (ASPCA) Animal Poisoning Control Center, from 2005-2014. We used ordinary and mixed logistic regression models as well as lasso logistic regression models with and without controlling for autocorrelation at the state level to train our models on half the dataset and test their predictive performance on the remainder. Although epidemiologically informed logistic regression models may require substantial knowledge of the disease systems being investigated, they had the same predictive abilities as lasso logistic regression models. All models had relatively high predictive parameters except for positive predictive values, due to the rare nature of calls concerning opioid and cannabinoid poisonings. Ordinary and mixed logistic regression models were also substantially more parsimonious than their lasso equivalents while still allowing for the epidemiological interpretation of model coefficients. Controlling for autocorrelation had little effect on the predictive performance of all models, but it did reduce the number of variables included in lasso models. Several disorder variables were associated with opioid and cannabinoid calls that were consistent with the acute effects of these toxicants. These models may help build diagnostic evidence concerning dog exposure to opioids and cannabinoids, saving time and resources when investigating these cases.

Investigating the distribution of calls to a North American animal poison control call center by veterinarians and the public in space, time, and space-time.

Health assessments via phone call or tele-triage have become very popular. Tele-triage in the veterinary field and North American context is available since the early 2000s. However, there is little knowledge of how caller type influences the distribution of calls. The objectives of this study were to examine the distribution of calls to the Animal Poison Control Center (APCC) by caller type in space, time, and space-time. Data regarding caller location were obtained from the APCC by American Society for the Prevention of Cruelty to Animals (ASPCA). The data were analysed using the spatial scan statistic to identify clusters of higher-than-expected proportion of veterinarian or public calls in space, time, and space-time. Statistically significant spatial clusters of increased call frequencies by veterinarians were identified in some western, midwestern, and southwestern states for each year of the study period. Furthermore, annual clusters of increased call frequencies by the general public were identified from some northeastern states. Based on yearly scans, we identified statistically significant temporal clusters of higher-than-expected public calls during Christmas/winter holidays. During space-time scans of the entire study period, we identified a statistically significant cluster of higher-than-expected proportion of veterinarian calls at the beginning of the study period in the western, central, and southeastern states followed by a significant cluster of excess public calls near the end of the study period on the northeast. Our results suggest that user patterns of the APCC vary by region and both season and calendar time.

Estimating the test-adjusted incidence of Chlamydia trachomatis infections identified through Public Health Ontario Laboratories in Peel region, Ontario, 2010-2018: a population-based study.

BACKGROUND: Public health guidelines for chlamydia testing are not sex specific, but young females test at a disproportionally higher rate than males and other age groups. This study aims to describe testing trends across age and sex subgroups, then estimate a test-adjusted incidence of chlamydia in these subgroups to identify gaps in current testing practices. METHODS: We used a population-based study to examine observed chlamydia rates by age and sex subgroups: 15-19 years, 20-29 years, 30-39 years and older than 40 years. The study included diagnostic test results recorded by Public Health Ontario Laboratories between Jan. 1, 2010, and Dec. 31, 2018, for individuals living in Peel region, Ontario. We then employed meta-regression models as a method of standardization to estimate the effect of sex and age on standardized morbidity ratio, testing ratio and test positivity, then calculate a test-adjusted incidence of chlamydia for each subgroup. RESULTS: Over the study period, infection, testing and test positivity varied across age and sex subgroups. Observed incidence and testing were highest in females aged 20-29 years, whereas males had the highest standardized test positivity across all age groups. After estimating test-adjusted incidence for each age-sex subgroup, males in the 15-19-year and 30-39-year age groups had an increase in incidence of 60.2% and 9.7%, respectively, compared with the observed incidence. INTERPRETATION: We found that estimated test-adjusted incidence was higher than observed incidence in males aged 15-19 years and 30-39 years. This suggests that infections in males are likely being missed owing to differential testing, and this may be contributing to the persistent increase in reported cases in Canada. Public health programming that targets males, especially in high-risk settings and communities, and use of innovative partner notification methods could be critical to curbing overall rates of chlamydia.

Can North American animal poison control center call data provide early warning of outbreaks associated with contaminated pet food? Using the 2007 melamine pet food contamination incident as a case study.

The 2007 melamine pet food contamination incident highlighted the need for enhanced reporting of toxicological exposures and development of a national quantitative disease surveillance system for companion animals. Data from poison control centers, such as the Animal Poison Control Center (APCC), may be useful for conducting real-time surveillance in this population. In this study, we explored the suitability of APCC call data for early warning of toxicological incidents in companion animal populations by using a-priori knowledge of the melamine-related nephrotoxicosis outbreak. Patient and household-level information regarding possible toxicological exposures in dogs and cats reported to the APCC from 2005 to 2007, inclusive, were extracted from the APCC's AnTox database. These data were used to examine the impact of surveillance outcome, statistical methodology, analysis level, and call source on the ability to detect the outbreak prior to the voluntary recall issued by the pet food manufacturer. Retrospective Poisson temporal scan tests were applied for each combination of outcome, method, level, and call source. The results showed that month-adjusted scans using syndromic data may have been able to help detect the outbreak up to two months prior to the voluntary recall although the success of these methods varied across call sources. We also demonstrated covariate month-adjustment can lead to vastly different results based on the surveillance outcome and call source to which it is applied. This illustrates care should be taken prior to arbitrarily selecting a surveillance outcome and statistical model for surveillance efforts and warns against ignoring the impacts of call source or key covariates when applying quantitative surveillance methods to APCC call data since these factors can lead to very different results. This study provides further evidence that APCC call data may be useful for conducting surveillance in the US companion animal population and further exploratory analyses and validation studies are warranted.

Examining the differential use of a North American animal poison control call center by veterinarians and the public for dog-related calls.

Tele-triage, a subset of telehealth services, is becoming increasingly common, they offer users the ability to receive credible health advice from licensed professionals in the comfort of their own home. In the field of veterinary medicine, tele-triage services have been employed since the early 2000s, but there has been little examination of how these services are used by callers. The objectives of this study were to explore how the use of an animal poison control center (APCC) tele-triage service varied between veterinarians and the public in terms of toxicant type, animal demographics, availability of veterinary services, as well as seasonal and secular trends. Data regarding dog poisoning events were obtained from the APCC of the American Society for the Prevention of Cruelty to Animals' (ASPCA). We fitted a mixed logistic regression model with random intercepts for county and state and identified associations between caller type and the following: animal characteristics (i.e., age, weight, breed-class), type of toxicant, season, year, and access to veterinary services (i.e., veterinarians per capita in the county of the caller). The model included interaction effects between season and both plant and pesticide toxicants. There was also an interaction between year and access to veterinary care. Further investigations are needed to understand how the novelty of a toxicant and the severity of clinical signs associated with a toxicant predict the type of caller, if pet demographics are associated with the caller based on medical issues or owner attitudes, and how access to veterinary care influences the use of this tele-triage service.

Spatial, temporal, and space-time clusters associated with opioid and cannabis poisoning events in U.S. dogs (2005-2014).

While a substantial amount of research has focused on the abuse of opioids and cannabinoids in human populations, few studies have investigated accidental poisoning events in pet populations. The objective of this study was to identify whether poisoning events involving opioids and cannabinoids clustered in space, time, and space-time, and compare the locations of clusters between the two toxicants. Data were obtained concerning reports of dog poisoning events from the American Society for the Prevention of Cruelty to Animals' (ASPCA) Animal Poisoning Control Center (APCC), from 2005-2014. The spatial scan statistic was used to identify clusters with a high proportion of these poisoning events. Our analyses show that opioid and cannabinoid poisoning events clustered in space, time, and space-time. The cluster patterns identified for each toxicant were distinct, but both shared some similarities with human use data. This study may help increase awareness to the public, public health, and veterinary communities about where and when dogs were most affected by opioid and cannabinoid poisonings. This study highlights the need to educate dog owners about safeguarding opioid and cannabinoid products from vulnerable populations.

Reduction of free-roaming cat population requires high-intensity neutering in spatial contiguity to mitigate compensatory effects.

When free-roaming in natural areas, the domestic cat (Felis silvestris catus) is ranked high among the most destructive alien species. Near human dwellings, it might pose a risk to humans, impair sanitation, and suffer from poor welfare. Cats' popularity as companion animals complicates their population control. Thus, culling is often replaced by a fertility control method called "trap­neuter­return/release" (TNR), considered more humane. Despite the extensive application of TNR, a long-term controlled study was never performed to test its effectiveness. We present a uniquely designed controlled field experiment for examining TNR effectiveness. The study was performed over a 12-y period, divided into preintervention and mixed- and full-intervention phases, and spanned a 20-km2 urban area. Trends of cat, intact-female, and kitten counts, cat reproduction, and carcass reports were compared among study phases and areas with different neutering intensities. The cat population increased during the first two study phases and did not decline in highly neutered populations, presumably due to cat immigration. Expansion of high-intensity neutering to the entire city in the full-intervention phase (>70% neutering percentage) reversed cat population growth, reaching an annual approximately 7% reduction. This population reduction was limited by a rebound increase in cat reproduction and longevity. We conclude that cat population management by TNR should be performed with high intensity, continuously, and in geographic contiguity to enable population reduction. To enhance management effectiveness and mitigate compensatory effects, we recommend further evaluating an integrated strategy that combines TNR with complementary methods (e.g., vital resource regulation, ill cat euthanasia, and adoption).

Exploring the geographical distribution of human cryptosporidiosis in Southern Ontario from 2011 to 2014.

Cryptosporidium is a protozoan parasite of increasing global public health concern because of its ability to cause disease in both humans and animals through contaminated food and water supplies. In Canada, most human cryptosporidiosis cases are due to Cryptosporidium hominis; however, the presence of zoonotic Cryptosporidium parvum has been observed. Since 2005, the incidence of cryptosporidiosis in Ontario has been consistently higher than the national average; however, it is not understood why, suggesting an incomplete understanding of the pathogen's ecology, epidemiology and transmission pathways. The goal of this study was to explore the spatial distribution of human cryptosporidiosis across the 29 Public Health Unit (PHU) areas of Southern Ontario from 2011 to 2014. Surveillance data on human cryptosporidiosis were obtained from Public Health Ontario. Choropleth and isopleth maps were used to display the distribution of incidence rates of human cryptosporidiosis. High-rate clusters of human cryptosporidiosis were identified. Poisson and spatial Poisson regression models were used to determine the relationship between the incidence of human cryptosporidiosis, cattle density and the smoothed farm-level prevalence of bovine cryptosporidiosis at the PHU level. The annual incidence of reported human cryptosporidiosis in Southern Ontario ranged from 1.62 (95% CI: 1.41-1.86) to 1.82 (95%CI: 1.60-2.06) cases per 100,000 population, with an overall cumulative incidence of 6.91 (95%CI: 6.47-7.39) cases per 100,000 for the 4-year study period. High-risk clusters of human cryptosporidiosis were identified in each year. The relative risk for the clusters ranged from 2.03 (95% CI: 1.63-2.55) to 6.87 (95% CI: 5.07-9.30). A relationship was found between the incidence of cryptosporidiosis and dairy cattle density. Based on this study, the Central West region would be an ideal ecological system to conduct further targeted surveillance to identify factors that may be contributing to the higher burden of cryptosporidiosis in the human and bovine populations in the region.

Sporadic SARS-CoV-2 cases at the neighbourhood level in Toronto, Ontario, 2020: a spatial analysis of the early pandemic period.

BACKGROUND: As the largest city in Canada, Toronto has played an important role in the dynamics of SARS-CoV-2 transmission in Ontario, and the burden of disease across Toronto neighbourhoods has shown considerable heterogeneity. The purpose of this study was to investigate the spatial variation of sporadic SARS-CoV-2 cases in Toronto neighbourhoods by detecting clusters of increased risk and investigating effects of neighbourhood-level risk factors on rates. METHODS: Data on sporadic SARS-CoV-2 cases, at the neighbourhood level, for Jan. 25 to Nov. 26, 2020, were obtained from the City of Toronto COVID-19 dashboard. We used a flexibly shaped spatial scan to detect clusters of increased risk of sporadic COVID-19. We then used a generalized linear geostatistical model to investigate whether average household size, population density, dependency ratio and prevalence of low-income households were associated with sporadic SARS-CoV-2 rates. RESULTS: We identified 3 clusters of elevated risk of SARS-CoV-2 infection, with standardized morbidity ratios ranging from 1.59 to 2.43. The generalized linear geostatistical model found that average household size (relative risk [RR] 2.17, 95% confidence interval [CI] 1.80-2.61) and percentage of low-income households (RR 1.03, 95% CI 1.02-1.04) were significant predictors of sporadic SARS-CoV-2 cases at the neighbourhood level. INTERPRETATION: During the study period, 3 clusters of increased risk of sporadic SARS-CoV-2 infection were identified, and average household size and percentage of low-income households were found to be associated with sporadic SARS-CoV-2 rates at the neighbourhood level. The findings of this study can be used to target resources and create policy to address inequities that are shown through heterogeneity of SARS-CoV-2 cases at the neighbourhood level in Toronto, Ontario.

Zika virus outbreak in Brazil under current and future climate.

INTRODUCTION: Zika virus (ZIKV) is primarily transmitted byAedes aegypti and Aedes albopictus mosquitoes between humans and non-human primates. Climate change may enhance virus reproduction in Aedes spp. mosquito populations, resulting in intensified ZIKV outbreaks. The study objective was to explore how an outbreak similar to the 2016 ZIKV outbreak in Brazil might unfold with projected climate change. METHODS: A compartmental infectious disease model that included compartments for humans and mosquitoes was developed to fit the 2016 ZIKV outbreak data from Brazil using least squares optimization. To explore the impact of climate change, published polynomial relationships between temperature and temperature-sensitive mosquito population and virus transmission parameters (mosquito mortality, development rate, and ZIKV extrinsic incubation period) were used. Projections for future outbreaks were obtained by simulating transmission with effects of projected average monthly temperatures on temperature-sensitive model parameters at each of three future time periods: 2011-2040, 2041-2070, and 2071-2100. The projected future climate was obtained from an ensemble of regional climate models (RCMs) obtained from the Co-Ordinated Regional Downscaling Experiment (CORDEX) that used Representative Concentration Pathways (RCP) with two radiative forcing values, RCP4.5 and RCP8.5. A sensitivity analysis was performed to explore the impact of temperature-dependent parameters on the model outcomes. RESULTS: Climate change scenarios impacted the model outcomes, including the peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and the duration of the ZIKV outbreak. Comparing 2070-2100 to 2016, using RCP4.5, the peak incidence was 22,030 compared to 10,473; the time to epidemic peak was 12 compared to 9 weeks, and the outbreak duration was 52 compared to 41 weeks. Comparing 2070-2100 to 2016, using RCP8.5, the peak incidence was 21,786 compared to 10,473; the time to epidemic peak was 11 compared to 9 weeks, and the outbreak duration was 50 compared to 41weeks. The increases are due to optimal climate conditions for mosquitoes, with the mean temperature reaching 28 °C in the warmest months. Under a high emission scenario (RCP8.5), mean temperatures extend above optimal for mosquito survival in the warmest months. CONCLUSION: Outbreaks of ZIKV in locations similar to Brazil are expected to be more intense with a warming climate. As climate change impacts are becoming increasingly apparent on human health, it is important to quantify the effect and use this knowledge to inform decisions on prevention and control strategies.

Detection of spatial and spatio-temporal Salmonella Heidelberg and Salmonella Typhimurium human case clusters focused around licensed abattoirs in Ontario in 2015, and their potential relation to known outbreaks.

Salmonellosis is one of several zoonotic diseases for which individuals with occupational animal contact, including abattoir workers, are at an increased risk. If meat is contaminated during slaughter, this can increase the risk of enteric illness for consumers. In this study, we investigated whether reported cases of Salmonella Heidelberg and Typhimurium were clustered around abattoirs in Ontario in 2015 and whether there was any evidence (laboratory/exposure) to suggest an abattoir at the centre of a cluster might be the source of exposure. Data for each reported case of S. Heidelberg and S. Typhimurium in Ontario in 2015 were collected. Multi-focused and non-focused spatial and space-time cluster detection tests were performed for each serotype, with and without cases linked to known outbreaks, using Poisson and space-time permutation models. Focused tests included the location of abattoirs operational in all or part of 2015. Laboratory data and exposure information were used to explore the relatedness of cases within identified clusters. Focused spatial tests identified clusters of S. Heidelberg and S. Typhimurium around abattoirs. Focused space-time permutation tests identified 2 significant space-time clusters of S. Heidelberg; one cluster (n = 11 cases) included 8 of 9 cases associated with a known outbreak and the other cluster (n = 18 cases) was not part of a previously identified outbreak. Review of laboratory and risk factor information suggested that cases within each cluster shared a common exposure. Cases were not asked about goat or sheep meat consumption. The focused cluster test, particularly with the space-time permutation model, could assist in identifying outbreaks associated with a particular physical location, such as an abattoir. Improvements to the current case investigation process, such as consistent collection and reporting of high-risk occupation information and more detailed food consumption history, could assist in outbreak identification when coupled with this statistic.

Yellow fever virus outbreak in Brazil under current and future climate.

INTRODUCTION: Yellow fever (YF) is primarily transmitted by Haemagogus species of mosquitoes. Under climate change, mosquitoes and the pathogens that they carry are expected to develop faster, potentially impacting the case count and duration of YF outbreaks. The aim of this study was to determine how YF virus outbreaks in Brazil may change under future climate, using ensemble simulations from regional climate models under RCP4.5 and RCP8.5 scenarios for three time periods: 2011-2040 (short-term), 2041-2070 (mid-term), and 2071-2100 (long-term). METHODS: A compartmental model was developed to fit the 2017/18 YF outbreak data in Brazil using least squares optimization. To explore the impact of climate change, temperature-sensitive mosquito parameters were set to change over projected time periods using polynomial equations fitted to their relationship with temperature according to the average temperature for years 2011-2040, 2041-2070, and 2071-2100 for climate change scenarios using RCP4.5 and RCP8.5, where RCP4.5/RCP8.5 corresponds to intermediate/high radiative forcing values and to moderate/higher warming trends. A sensitivity analysis was conducted to determine how the temperature-sensitive parameters impacted model results, and to determine how vaccination could play a role in reducing YF in Brazil. RESULTS: Yellow fever case projections for Brazil from the models varied when climate change scenarios were applied, including the peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and the outbreak duration. Overall, a decrease in YF cases and outbreak duration was observed. Comparing the observed incidence in 2017/18 to the projected incidence in 2070-2100, for RCP4.5, the cumulative case incidence decreased from 184 to 161, and the outbreak duration decreased from 21 to 20 weeks. For RCP8.5, the peak case incidence decreased from 184 to 147, and the outbreak duration decreased from 21 to 17 weeks. The observed decrease was primarily due to temperature increasing beyond that suitable for Haemagogus mosquito survival. CONCLUSIONS: Climate change is anticipated to have an impact on mosquito-borne diseases. We found outbreaks of YF may reduce in intensity as temperatures increase in Brazil; however, temperature is not the only factor involved with disease transmission. Other factors must be explored to determine the attributable impact of climate change on mosquito-borne diseases.

Investigation of the occurrence of Angiostrongylus vasorum in coyotes in southern Ontario, Canada.

In North America, the only endemic focus for Angiostrongylus vasorum (French heartworm) was historically thought to occur in the southeastern part of the island of Newfoundland. However, reports of A. vasorum infection in wild canids in West Virginia, USA, and Nova Scotia, Canada, suggest the introduction of the parasite to mainland North America. We screened for A. vasorum in coyotes from across southern Ontario. Additionally, we evaluated the performance of ELISAs for detection of circulating A. vasorum antigen (Ag-ELISA) and antibodies against A. vasorum (Ab-ELISA) designed for use in sera or blood of foxes for use with coyotes in this region. Autopsies were performed on 397 coyotes, and lung tissue extract prepared from each carcass was tested via both ELISAs. The sensitivity and specificity for both tests were estimated in the absence of a gold standard using a 2-test single population Bayesian model; sensitivity and specificity priors were based on the performance of the assays in foxes in Switzerland. Eight coyotes tested positive for A. vasorum antigen; no animal was antibody positive. The estimated sensitivity and specificity of the Ag-ELISA were 90.8% (95% credible interval [CrI]: 83.8-95.6%) and 95.5% (95% CrI: 93.4-97.2%), respectively. For the Ab-ELISA, the estimated sensitivity and specificity were 41.9% (95% CrI: 32.1-51.9%) and 98.0% (95% CrI: 96.3-99.0%), respectively. Based on these findings and negative postmortem data for the same animals, there is insufficient evidence to suggest the presence of A. vasorum in southern Ontario coyotes.

The impact of state cannabis legislation, county-level socioeconomic and dog-level characteristics on reported cannabis poisonings of companion dogs in the USA (2009-2014).

With current trends in cannabis legalization, large efforts are being made to understand the effects of less restricted legislation on human consumption, health, and abuse of these products. Little is known about the effects of cannabis legalization and increased cannabis use on vulnerable populations, such as dogs. The objective of this study was to examine the effects of different state-level cannabis legislation, county-level socioeconomic factors, and dog-level characteristics on dog cannabis poisoning reports to an animal poison control center (APCC). Data were obtained concerning reports of dog poisoning events, county characteristics, and state cannabis legislation from the American Society for the Prevention of Cruelty to Animals' (ASPCA) APCC, the US Census Bureau, and various public policy-oriented and government websites, respectively. A multilevel logistic regression model with random intercepts for county and state was fitted to investigate the associations between the odds of a call to the APCC being related to a dog being poisoned by a cannabis product and the following types of variables: dog characteristics, county-level socioeconomic characteristics, and the type of state-level cannabis legislation. There were significantly higher odds of a call being related to cannabis in states with lower penalties for cannabis use and possession. The odds of these calls were higher in counties with higher income variability, higher percentage of urban population, and among smaller, male, and intact dogs. These calls increased throughout the study period (2009-2014). Reporting of cannabis poisonings were more likely to come from veterinarians than dog owners. Reported dog poisonings due to cannabis appear to be influenced by dog-level and community-level factors. This study may increase awareness to the public, public health, and veterinary communities of the effects of recreational drug use on dog populations. This study highlights the need to educate dog owners about safeguarding cannabis products from vulnerable populations.

Evaluation of the prevalence of Echinococcus multilocularis in dogs that visit off-leash dog parks in southern Ontario, Canada.

Prior to 2012, Echinococcus multilocularis was not known to occur in any host in Ontario, Canada. However, since that year, five cases of alveolar echinococcosis have been diagnosed in dogs that resided at the western end of Lake Ontario. In addition, E. multilocularis has been shown to be a common infection in wild canids (i.e. coyotes and foxes) across southern Ontario with a high-risk infection cluster in the area surrounding the western shores of Lake Ontario and northern shores of Lake Erie. In regions endemic for E. multilocularis, dog ownership is considered a risk factor for human alveolar echinococcosis. A study was therefore carried out to determine the prevalence of E. multilocularis intestinal infections in dogs within the high-risk infection cluster. From May to November 2018, faecal samples were collected from 477 dogs aged ≥6 months that visited 12 off-leash dog parks in the Halton, Hamilton and Niagara public health units. Faecal samples were analysed via a magnetic capture probe DNA extraction and real-time PCR method for E. multilocularis DNA. Overall, 0% (97.5% CI: 0%-0.80%) of samples tested positive. This result informs preventive recommendations for E. multilocularis infections in dogs in this region.

Companion animal exposures to potentially poisonous substances reported to a national poison control center in the United States in 2005 through 2014.

OBJECTIVE: To evaluate data concerning suspected companion animal exposures to possibly hazardous substances reported during telephone calls to the US Animal Poison Control Center (APCC) for characterization of dog and cat exposures to potentially toxic substances in the United States. SAMPLE: Household-level poisonings events involving dogs and cats in the United States that were reported to the APCC in 2005 through 2014. PROCEDURES: Substances involved in reported poisonings of dogs and cats were classified into 20 general categories, and descriptive statistical analysis was used to examine the most common categories. Case fatality ratios were estimated for all exposure categories for which a final outcome status of the affected animal was documented. RESULTS: Over the 10-year study period, 241,261 household-level poisoning events were reported to the APCC from across the United States, of which 86.0% and 14.0% involved dogs and cats, respectively. The most common agent categories reported for dogs included human medicines, foods, and pesticides. The most common agent categories reported for cats included human medicines, plants, and veterinary medicines. Chocolate and Lilium plants were the most commonly reported exposures of dogs and cats, respectively. Fluorouracil (65.2%) and bifenthrin (66.7%) were found to have the highest case fatality ratio for dogs and cats, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: The APCC call data can be used to identify the most common toxicological exposures of dogs and cats, understand the epidemiological aspects of these poisonings, and inform education programs for owners and veterinarians. Data from the APCC may be suitable for surveillance of toxicological exposures of companion animals in the United States.

Good times bad times: Automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning.

BACKGROUND: The rise of big data and related predictive modelling based on machine learning algorithms over the last two decades have provided new opportunities for disease surveillance and public health preparedness. Big data come with the promise of faster generation of and access to more precise information, potentially facilitating predictive precision in public health ("precision public health"). As an example, we considered forecasting of the future course of the monthly cryptosporidiosis incidence in Ontario. METHODS: The traditional statistical approach to forecasting is the seasonal autoregressive integrated moving-average (SARIMA) model. We applied SARIMA and an artificial neural network (ANN) approach, specifically a feed-forward neural network, to predict monthly cryptosporidiosis incidence in Ontario in 2017 using 2005-2016 data as a training set. Both forecasting approaches are automated to make them relevant in a disease surveillance context. We compared the resulting forecasts using the root mean squared error (RMSE) and mean absolute error (MAE) as measures of predictive accuracy. RESULTS: Cryptosporidiosis is a seasonal disease, which peaks in Ontario in late summer. In this study, the SARIMA model and ANN forecasting approaches captured the seasonal pattern of cryptosporidiosis well. Contrary to similar studies reported in the literature, the ANN forecasts of cryptosporidiosis were slightly less accurate than the SARIMA model forecasts. CONCLUSION: The ANN and SARIMA approaches are suitable for automated forecasting of public health time series data from surveillance systems. Future studies should employ additional algorithms (e.g. random forests) and assess accuracy by using alternative diseases for case studies and conducting rigorous simulation studies. Difference between the forecasts from the machine learning algorithm, that is, the ANN, and the statistical learning model, that is, the SARIMA, should be considered with respect to philosophical differences between the two approaches.

Evaluation of the SNAP® 4Dx® plus test for the detection of Dirofilaria immitis antigen and characterization of exposure to tick-borne pathogens in wild canids in southern Ontario.

Population-level surveys for Dirofilaria immitis in wild canids typically rely on identification of the parasite at necropsy. More recently, some studies have employed the use of the SNAP® 4Dx® Plus test. However, since the assay was designed for use with domestic dogs it needs to be validated for use with wild canids for accurate interpretation of results. We therefore evaluated the performance of the SNAP® 4Dx® Plus test for detection of D. immitis in wild canids in southern Ontario. Overall, 199 wild canid carcasses were collected from across the region and assessed for the presence of D. immitis parasites at necropsy; ten were infected. Lung tissue extract (LE) and thoracic fluid filter paper extract (TFE) prepared from each wild canid were tested via the SNAP® 4Dx® Plus test, which simultaneously tests for the presence of D. immitis antigen and antibodies to Borrelia burgdorferi, Anaplasma spp., and Ehrlichia spp. The prevalence adjusted bias adjusted kappa (PABAK) and Gwet's first-order-agreement coefficient (AC1) were used to assess the level of agreement between sample pairs. The PABAK and AC1 between LF and TFE applied to the SNAP® 4Dx® Plus test and the necropsy-confirmed D. immitis status indicated a very high level of agreement for all sample pairs. Compared to necropsy-confirmed D. immitis status, the estimated sensitivity and specificity of the SNAP® 4Dx® Plus test for D. immitis antigen in LE was 80 % (95 % CI 44.4-97.5%) and 98.9 % (95 % CI 96.2-99.9%), respectively. For the TFE, the sensitivity was 70 % (95 % CI 34.8-93.3%) and the specificity was 97.9 % (95 % CI 94.7-99.4%). With respect to the tick-borne pathogen components, 1.5 % (3/199; 95 % CI 0.3-4.5%) of wild canids tested positive for B. burgdorferi antibody; 1.0 % (2/199; 95 % CI 0-3.8%) of LE samples were positive and 0.5 % (1/199; 95 % CI 0-3.1%) of TFE samples were positive. No samples tested positive for antibody to Anaplasma spp. (95 % CI 0-2.3%) or Ehrlichia spp. (95 % CI 0-2.3%). Collectively, the results suggest that the SNAP® 4Dx® Plus test may be a suitable test for use with LE and TFE for the detection of D. immitis antigen in wild canids from southern Ontario.