Comparison of spatiotemporal patterns of historic natural Anthrax outbreaks in Minnesota and Kazakhstan

Spatial indices quantifying exposure to swine farming in North Carolina

Introduction

Proximity to swine farms is often used as a surrogate in exposure assessments, allowing for the relative quantification of potential pollutant dispersion, odor intensity, and health impacts on neighboring communities. However, defining exposure is complex, and the resulting risk profiles can vary depending on the definition used.

Methods

To quantify the spatially based exposure of surrounding communities to swine farms in North Carolina, three spatially explicit metrics were developed at the census tract-level: IDx1: number of households within 1-mile from a hog farm, IDx2: Co-kriging using the number of hogs and manure lagoons, and IDx3: hog density per square mile. Then, the correlation between these indices and Centers for Disease Control and Prevention (CDC)'s Social Vulnerability Index (SVI) and Environmental Justice Index (EJI), which are generalized vulnerability measures, was evaluated to assess direct impact from swine farms versus multiple stressors.

Results

The three indices differed visually, with IDx3 strongly correlated with IDx1 (0.8) and moderately correlated with IDx2 (0.4). CDC EJI and SVI were not prominently correlated with any of the swine-farm specific indices (≤0.3) indicating limited overlap. The correlation between swine-farm-specific indices and CDC SVI was slightly pronounced in rural areas indicating socially vulnerable populations are more likely to live near swine farming areas in rural census tracts. Having swine farm-specific indices offers a more tailored and nuanced understanding of the potential health and environmental risks. However, the differences between the maps and the varying correlations underscored how different definitions of exposure can yield distinct narratives about which neighborhoods are at risk. Defining and measuring potential exposure, considering factors like proximity, duration, frequency, vulnerability, and cumulative impact, is highly challenging.

Discussion

The study emphasizes the need for a hierarchical framework to quantify and compare environmental exposures, addressing risk-modifying factors and individual-level exposure across space and time before implying direct exposure risks. This approach enables more informed planning for targeted solutions and fosters collaboration among stakeholders, facilitating critical discussions on integrated One Health solutions.

Space-time cluster analysis and maximum entropy modeling of Peste des petits ruminants (PPR) in Türkiye

Peste des petits ruminants (PPR) is an economically important highly serious transboundary disease that mainly occurs in small ruminants such as sheep and goats. The aim of this study was to identify the probability of risk and and space-time clusters of Peste des Petits Ruminants (PPR) in Türkiye. The occurrence of PPR in Türkiye from 2017 to 2019 was investigated in this study using spatial analysis based on geographic information system (GIS). Between these dates, it was determined that 337 outbreaks and 18,467 cases. The highest number of outbreaks were detected in the Central Anatolia region. It was determined that PPR is seen more intensely in sheep compared to goats in Türkiye. In this study, 34 environmental variables (19 bioclimatic, 12 precipitation, altitude and small livestock density variables) were used to explore the environmental influences on PPR outbreak by maximum entropy modeling (Maxent). The clusters of PPR in Türkiye were identified using the retrospective space-time scan data that were computed using the space-time permutation model. A PPR prediction model was created using data on PPR outbreaks combination with environmental variables. Nineteen significant (p < 0.001) space-time clusters were determined. It was discovered that the variables altitude, sheep density, precipitation in june, and average temperature in the warmest season made important contributions to the model and the PPR outbreak may be strongly related with these variables. In this study, PPR in Türkiye has been characterized significantly spatio-temporal and enviromental factors. In this context, the disease pattern and obtained these findings will contribute to policymakers in the prevention and control of the disease.

Outbreak of Anthrax in Livestock with Human Occupational Exposures - Minnesota, 2023

In July 2023, the Minnesota Department of Health (MDH) was notified of possible occupational exposures to anthrax during an outbreak in animals. In consultation with the Centers for Disease Control and Prevention, MDH epidemiologists created a questionnaire that assessed exposure risks and helped determine individual illness monitoring and antibiotic post-exposure prophylaxis needs. This investigation and the resources developed for it could be useful in future scenarios where there are occupational exposures to naturally occurring anthrax.

Development of ELISA based on Bacillus anthracis capsule biosynthesis protein CapA for naturally acquired antibodies against anthrax

Anthrax is a zoonotic disease caused by the gram-positive spore-forming bacterium Bacillus anthracis. Detecting naturally acquired antibodies against anthrax sublethal exposure in animals is essential for anthrax surveillance and effective control measures. Serological assays based on protective antigen (PA) of B. anthracis are mainly used for anthrax surveillance and vaccine evaluation. Although the assay is reliable, it is challenging to distinguish the naturally acquired antibodies from vaccine-induced immunity in animals because PA is cross-reactive to both antibodies. Although additional data on the vaccination history of animals could bypass this problem, such data are not readily accessible in many cases. In this study, we established a new enzyme-linked immunosorbent assay (ELISA) specific to antibodies against capsule biosynthesis protein CapA antigen of B. anthracis, which is non-cross-reactive to vaccine-induced antibodies in horses. Using in silico analyses, we screened coding sequences encoded on pXO2 plasmid, which is absent in the veterinary vaccine strain Sterne 34F2 but present in virulent strains of B. anthracis. Among the 8 selected antigen candidates, capsule biosynthesis protein CapA (GBAA_RS28240) and peptide ABC transporter substrate-binding protein (GBAA_RS28340) were detected by antibodies in infected horse sera. Of these, CapA has not yet been identified as immunoreactive in other studies to the best of our knowledge. Considering the protein solubility and specificity of B. anthracis, we prepared the C-terminus region of CapA, named CapA322, and developed CapA322-ELISA based on a horse model. Comparative analysis of the CapA322-ELISA and PAD1-ELISA (ELISA uses domain one of the PA) showed that CapA322-ELISA could detect anti-CapA antibodies in sera from infected horses but was non-reactive to sera from vaccinated horses. The CapA322-ELISA could contribute to the anthrax surveillance in endemic areas, and two immunoreactive proteins identified in this study could be additives to the improvement of current or future vaccine development.

Anthrax Surveillance and the Limited Overlap Between Obligate Scavengers and Endemic Anthrax Zones in the United States

Anthrax is a zoonosis caused by the spore-forming bacterium Bacillus anthracis, with potential for high fatality rate, especially in herbivores. Upon host death, spores can enter the soil surrounding the carcass and be ingested by other animals feeding in the same location. Accordingly, surveillance to quickly identify and decontaminate anthrax carcasses is crucial to outbreak prevention. In endemic anthrax areas such as Texas and Africa, vultures are used as a surveillance tool for identifying presence and location of dead animals. However, many anthrax outbreaks in the United States have occurred in areas outside the ranges of both black and turkey vultures. Here, we used a longitudinal camera trap survey at carcass sites in southwestern Montana to investigate the utility of facultative avian scavengers on disease and carcass surveillance in a reemerging anthrax risk zone. From August 2016 to September 2018, camera traps at 11 carcass sites were triggered 1996 times by avian scavengers. While the majority were facultative avian scavengers such as corvids and eagles, our results suggest that facultative scavengers cannot replace vultures as a surveillance tool in this ecosystem due to their absence during the anthrax risk period (June to August), reduced search efficiency, or low flight patterns. We found that the conditions in Montana likely parallel systems elsewhere in the continental United States. Using ecological niche models of B. anthracis distribution overlaid with relative abundance maps of turkey vultures, we found that much of North Dakota, South Dakota, Minnesota, Wyoming, Nebraska, and Iowa have areas of anthrax risk, but low or absent turkey vulture populations. Without vultures in these areas, surveillance capacity is reduced, and it becomes more difficult to identify anthrax cases, meaning fewer carcasses are decontaminated, and consequently, outbreaks could become more frequent or severe.

An Estimate of Global Anthrax Prevalence in Livestock: A Meta-analysis

Background and Aim:

Anthrax, caused by the soil-borne spore-forming bacteria called Bacillus anthracis, is a zoonotic disease that persists worldwide in livestock and wildlife and infects humans. It is a great hazard to livestock; henceforth, evaluating the global concerns about the disease occurrence in livestock is essential. This study was conducted to estimate the global prevalence of anthrax and predict high-risk regions, which could be an input to veterinarians to take necessary steps to control and avoid the disease.

Materials and Methods:

A literature review was performed using online databases, namely, PubMed, Google Scholar, Scopus, Biomed Central, and Science Direct, to extract relevant publications worldwide between 1992 and 2020.</AQ9> Initially, 174 articles were selected, and after scrutinizing, 24 articles reporting the prevalence of anthrax were found to be adequate for the final meta-analysis. The statistical study was accompanied by employing fixed effects and random effects models using R.

Results:

The pooled prevalence of anthrax globally was 28% (95% confidence interval, 26-30%) from 2452 samples through the fixed effects model. Continent-wise subgroup analysis through the random effects model revealed that the pooled prevalence of anthrax was highest in Africa (29%) and least in North America (21%).

Conclusion:

In these publications, anthrax causes economic loss to farmers and, thus, to the world. Hence, controlling anthrax infections in high-risk regions are essential by implementing appropriate control measures to decrease the effect of the disease, thereby reducing economic loss.

Ecological and Socio-Economic Determinants of Livestock Animal Leptospirosis in the Russian Arctic

Leptospirosis is a re-emerging zoonotic infectious disease caused by pathogenic bacteria of the genus Leptospira. Regional differences in the disease manifestation and the role of ecological factors, specifically in regions with a subarctic and arctic climate, remain poorly understood. We here explored environmental and socio-economic features associated with leptospirosis cases in livestock animals in the Russian Arctic during 2000–2019. Spatial analysis suggested that the locations of the majority of 808 cases were in “boreal” or “polar” climate regions, with “cropland,” “forest,” “shrubland,” or “settlements” land-cover type, with a predominance of “Polar Moist Cropland on Plain” ecosystem. The cases demonstrated seasonality, with peaks in March, June, and August, corresponding to the livestock pasturing practices. We applied the Forest-based Classification and Regression algorithm to explore the relationships between the cumulative leptospirosis incidence per unit area by municipal districts (G-rate) and a number of socio-economic, landscape, and climatic factors. The model demonstrated satisfactory performance in explaining the observed disease distribution (R² = 0.82, p < 0.01), with human population density, livestock units density, the proportion of crop area, and budgetary investments into agriculture per unit area being the most influential socio-economic variables. Climatic factors demonstrated a significantly weaker influence, with nearly similar contributions of mean yearly precipitation and air temperature and number of days with above-zero temperatures. Using a projected climate by 2100 according to the RCP8.5 scenario, we predict a climate-related rise of expected disease incidence across most of the study area, with an up to 4.4-fold increase in the G-rate. These results demonstrated the predominant influence of the population and agricultural production factors on the observed increase in leptospirosis cases in livestock animals in the Russian Arctic. These findings may contribute to improvement in the regional system of anti-leptospirosis measures and may be used for further studies of livestock leptospirosis epidemiology at a finer scale.

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.

Lessons Learned From the Stakeholder Engagement in Research: Application of Spatial Analytical Tools in One Health Problems

Stakeholder engagement in research is widely advocated as a tool to integrate diverse knowledge and perspectives in the management of health threats while addressing potential conflicts of interest. Although guidelines for stakeholder engagement exist in public health and environmental sciences, the feasibility of actionable decisions based on scientific analyses and the lessons learned from the stakeholder engagement in the process co-creation of knowledge have been rarely discussed in One Health literature and veterinary sciences. Risk maps and risk regionalization using spatiotemporal epidemiological/analytical tools are known to improve risk perception and communication. Risk maps are useful when informing policy and management decisions on quarantine, vaccination, and surveillance intended to prevent or control threats to human, animal, or environmental health interface (i.e., One Health). We hypothesized that researcher-stakeholder engagement throughout the research process could enhance the utility of risk maps; while identifying opportunities to improve data collection, analysis, interpretation, and, ultimately, implementation of scientific/evidence-based management and policy measures. Three case studies were conducted to test this process of co-creation of scientific knowledge, using spatiotemporal epidemiological approaches, all related to One Health problems affecting Minnesota. Our interpretation of the opportunities, challenges, and lessons learned from the process are summarized from both researcher and stakeholder perspectives. By sharing our experience we intend to provide an understanding of the expectations, realizations, and “good practices” we learned through this slow-moving iterative process of co-creation of knowledge. We hope this contribution benefits the planning of future transdisciplinary research related to risk map-based management of One Health problems.