Olaf Berke

Measles remains endemic in Pakistan despite global vaccine efforts to prevent its transmission. This study sought to identify the patterns of measles cases in Pakistan, understand the reasons for its recurrence, and determine the optimal timing for measles vaccine campaigns in Pakistan to be most effective. As such, a time series analysis of the monthly measles incidence in Pakistan from 2014 to 2024, obtained from World Health Organization’s international measles database, was performed to identify temporal patterns in infections. An increasing trend was identified in the monthly measles cases overtime, along with seasonal swings of low occurrences during the summer, with average lows reaching 304 cases, and a rise during the winter months, with average highs reaching 1038 cases. An automated Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model was further applied to forecast the future incidence of measles, which revealed a three-fold increase in measles cases in the next two years, compared with 2023-2024. Low routine vaccine coverage, topped with climate change related migration and subsequent crowding in urban spaces were found to be major contributors to measles transmission in the country. Findings suggest that supplementary vaccination campaigns are essential and if held in August–September, can mitigate the anticipated rise in measles transmission beginning October every year.

Background: H3 influenza A viruses (IAV) have been shown to frequently cross the species barrier which can be an important factor in sustained transmission and spread. Machine learning methods have been widely explored for host prediction of IAV using genomic data; however, this is often done using data from only one of the eight IAV segments or by using all available IAV data to predict broad categories of hosts. Objective: The objective of this study was to combine machine learning algorithms with H3 IAV sequence data from all eight segments to train predictive machine learning models for distinct host prediction and validate model performance. Methods: Models were trained on both k-mers and amino acid properties alongside machine learning algorithms that included random forest and XGBoost for each of the eight IAV genome segments. Models were then validated on a test dataset through analytics of model class predicted probabilities and subsequently used to investigate between-species transmission patterns within case studies including canine H3N8, swine H3N2 2010.2, and duck H3 sequences. Results: Models demonstrated strong performance in host prediction across all eight segments on the test dataset, with overall accuracies and κ (kappa) values ranging from 0.995–0.997, 0.984–0.990, respectively. Misclassified test dataset sequences with high predicted probabilities (> 90%) were validated using available literature and were identified to be frequently associated with between-species transmission events. Between-species transmission patterns within case study model class predicted probabilities were also identified to be consistent with the literature in cases of both correct and incorrect classification. Conclusions: These models allow for rapid and accurate host prediction of H3 IAV datasets from any of the eight IAV segments and provide a solid framework that allows for identification of variants with higher than typical between-species transmission potential. However, results obtained on selected case studies suggest further improvements of the training and validation processes should be considered.

Objective: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a prime example of the One Health concept. While little can be done to immediately reverse climate change and population growth, public health must resort to health communication as its best option for disease control until an effective vaccine becomes available. Disease surveillance enabling precision public health has an important role in this respect: one of the goals of disease surveillance is to forecast the future burden of disease to inform those who need to know. The goal of this study was to forecast the burden of Lyme disease using automated machine learning and statistical learning approaches. Methods: Lyme disease reports were retrieved from Ontario’s integrated Public Health Information System surveillance system from January 2005 to December 2023. The reports from January 2005 to December 2021 were used as training data, and reports from January 2022 to December 2023 served as validation data. Forecasts from a seasonal autoregressive integrated moving-average model were used as a benchmark for forecasts from a feed-forward single-layer neural network machine learning algorithm. Results: The Lyme disease burden in Ontario is predicted to increase dramatically. Neither the neural network nor the seasonal autoregressive integrated moving-average model proved to be generally more accurate. Conclusions: The increasing burden of human Lyme disease is concerning to public health, further indicating ecosystem changes and challenges for canine health. Clinical Relevance: Human Lyme disease surveillance provides useful information to veterinarians.

Disease maps are integral to spatial epidemiology and public health. The map appearance and analysis of corresponding data may both depend on a map projection used to transform the 3-dimensional world onto a 2-dimensional surface. Map projections necessarily introduce bias - an issue that has not received full attention in the literature. This study aims to demonstrate the impact map projections can have on spatial analysis and disease maps for public health. Case studies applied varying map projections, including the Lambert, Mercator and Robinson projections, to Israel, North Carolina and Southern Ontario as study areas. The effect of projections on various measures, estimates, tests and models was assessed. When the map projection was changed: (i) a distance in Israel increased by 30%; (ii) for Southern Ontario an areal size increased by almost 95%; Moran's I test switched from significant to not; and (iii) a single disease cluster in North Carolina converted into three distinct clusters. Visual bias in disease mapping is unavoidable and should be recognized. Disease maps and spatial analytical inferences, including disease clusters should be reported with their geographic projection. Using geographic coordinates can prevent analytical bias.

One health is based on an interdisciplinary collaboration across professions using a common language. Geographic epidemiology is the study of spatial patterns of population health in a study area. Such spatial patterns (trend, cluster and clustering) require clear definition to be meaningful in science communication. However, the term "disease cluster" has been defined in the literature in various and rather different ways. When geographic epidemiology is unable to make sense of its own concepts it is questionable how respective research results can benefit one health. The goal of this study was to clarify the disease cluster concept. Examples of disease cluster definitions from the literature were used for illustration. The epidemiological triangle of causation (agent, host and environment) was used to conceptualize geographic epidemiological data analysis. The term disease cluster was distinguished from related concepts (clustering, high-risk area, hot spot and outbreak) additionally the semantics and statistical meaning of expectation and prediction were reviewed to further identify the cluster concept as a statistical outlier. The new paradigm of the geographic epidemiological trillium is proposed here and embedded within the spatial generalized linear mixed model to clarify concepts of spatial patterns and guide epidemiological research and teaching.

Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) are heterogeneous neoplasms of hematopoietic stem cells that are challenging to diagnose, differentiate, and prognosticate. Cytogenetic and mutational analyses are useful in humans but unavailable for dogs, where diagnosis and classification still rely largely on hematologic and morphologic assessment. The objectives of this study were to apply a classification scheme to myeloid neoplasms and to assess outcome in relation to predictor variables. Keyword search of a laboratory database, application of sequential exclusion criteria, and consensus from 3 reviewers yielded 70 cases of myeloid neoplasia with hematology results, and cytologic (11), histologic (14), or both (45) types of marrow specimens. Based on blast percentage and morphology, 42 cases were classified as MDS and 28 as AML. Dogs with MDS had significantly lower body weights, hemoglobin concentrations and blood blasts, and higher red blood cell size variability and platelet numbers than dogs with AML. Estimates of median survival using Kaplan-Meier curves for dogs with MDS and AML were 384 and 6 days, respectively ( P < .001). The instantaneous risk of death for dogs with MDS was approximately 5× lower than that of dogs with AML. Significant predictor variables of survival were body weight, white blood cell count, platelet count, and percent blood blasts ( P < .05). Hazard ratios (HRs) derived from best-fitting Cox regression models were 1.043, 0.998, and 1.061 for increased neutrophils, decreased platelets, and increased blood blasts, respectively. Findings from this study suggest that hematologic and morphologic variables are useful to predict outcomes in myeloid neoplasia.

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.

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.

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.