Habitat selection by free-roaming domestic dogs in rabies endemic countries in rural and urban settings.

Domestic dogs can affect human health through bites and pathogen transmission, particularly in resource-poor countries where dogs, including owned ones, predominantly roam freely. Habitat and resource selection analysis methods are commonplace in wildlife studies but have not been used to investigate the environmental resource use of free-roaming domestic dogs (FRDD). The present study implements GPS devices to investigate habitat selection by FRDD from an urban site and a rural site in Indonesia, and one urban and two rural sites in Guatemala (N = 321 dogs). Spatial mixed effects logistic regression models, accounting for heterogeneous distribution of the resources, showed that patterns of habitat selection by FRDD were similar across study sites. The most preferred resources were anthropogenic, being buildings and roads, which implies selection for human proximity. Vegetation and open fields were less preferred and steep terrain was avoided, indicating that FRDD were synanthropic and that their space patterns likely optimised energy use. Results presented here provide novel data on FRDD habitat selection patterns, while improving our understanding of dog roaming behaviour. These findings provide insights into possible high-risk locations for pathogen transmission for diseases such as rabies, and can assist management authorities in the planning and deployment of efficient disease control campaigns, including oral vaccination

The use of spatiotemporal analytical tools to inform decisions and policy in One Health scenarios

University of Minnesota Ph.D. dissertation. February 2019. Major: Veterinary Medicine. Advisors: Andres Perez, Nicholas Phelps. 1 computer file (PDF); xiii,, 230 pages.The use of spatiotemporal analytical tools to generate risk maps and risk scores that facilitate early detection of health and environmental threats is increasingly popular in many countries and international organizations around the world. The traditional approach of spatial epidemiology focuses on mapping and conducting tests for detection of spatial aggregation of cases, referred to as “clusters”, to determine visual and geographical relational clues, and then ecologic approaches to recognize etiologic signs of disease distribution in relation to explanatory factors. The advances in spatial epidemiology are focused on the application of spatiotemporal findings to inform mitigation measures, use of big data to improve the validity and reliability of case-data based analyses, and eventually to provide risk estimates in a timely manner to support decision and policy in preventive and control measures, while supporting the improvement of existing data collection processes. This study provided a framework for choosing spatiotemporal analytical tools, summarizing the features of tools commonly used in spatial analysis, and discussing their potential use when informing decisions related to One Health scenarios. To this end, three case studies addressing endemic conditions affecting ecosystem health, animal health, and public health in Minnesota were compared. A risk score; an estimate/characterization of the disease spread, and suggestions on risk zones were introduced, using spatiotemporal analytical tools, addressing aquatic invasive species in Minnesota waters, Johne’s disease in dairy cattle, and Anthrax, affecting wildlife, livestock, and humans, respectively. The One Health concept promotes a collaborative approach, through effective communication and cooperation across disciplines and sectors, to solve complex problems that intersect animal, human and environmental health. An essential component in the process is understanding the stakeholder perspectives of the problem. Therefore, the comparison between the case studies focused on the lessons learned through the researcher-stakeholder interactions and identification of the opportunities and challenges in the process. Overall, the work presented through this dissertation, serves as precedent for establishing a protocol of “good practices” when promoting the use of spatiotemporal analytical tools to inform the implementation of scientifically driven risk management and policy solutions to One Health scenarios

Modeling the Seasonal Variation of Windborne Transmission of Porcine Reproductive and Respiratory Syndrome Virus between Swine Farms

Modeling the windborne transmission of aerosolized pathogens is challenging. We adapted an atmospheric dispersion model named the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate the windborne dispersion of porcine reproductive and respiratory syndrome virus (PRRSv) between swine farms and incorporated the findings into an outbreak investigation. The risk was estimated semi-quantitatively based on the cumulative daily deposition of windborne particles and the distance to the closest emitting farm with an ongoing outbreak. Five years of data (2014:2018) were used to study the seasonal differences of the deposition thresholds of the airborne particles containing PRRSv and to evaluate the model in relation to risk prediction and barn air filtration. When the 14-day cumulative deposition was considered, in winter, above-threshold particle depositions would reach up to 30 km from emitting farms with 84% of them being within 10 km. Long-distance pathogen transmission was highest in winter and fall, lower in spring, and least in summer. The model successfully replicated the observed seasonality of PRRSv, where fall and winter posed a higher risk for outbreaks. Reaching the humidity and temperature thresholds tolerated by the virus in spring and summer reduced the survival and infectivity of aerosols beyond 10–20 km. Within the data limitations of voluntary participation, when wind was assumed to be the sole route of PRRSv transmission, the predictive performance of the model was fair with >0.64 AUC. Barn air filtration was associated with fewer outbreaks, particularly when exposed to high levels of viral particles. This study confirms the usefulness of the HYSPLIT model as a tool when determining seasonal effects and distances and informs the near real-time risk of windborne PRRSv transmission that can be useful in future outbreak investigations and for implementing timely control measures

Global prevalence of 4 neglected foodborne trematodes targeted for control by WHO: A scoping review to highlight the gaps.

BackgroundFoodborne trematodiases (FBTs) are a group of trematodes targeted for control as part of the World Health Organization (WHO) road map for neglected tropical diseases from 2021 to 2030. Disease mapping; surveillance; and capacity, awareness, and advocacy building are critical to reach the 2030 targets. This review aims to synthesise available data on FBT prevalence, risk factors, prevention, testing, and treatment.MethodsWe searched the scientific literature and extracted prevalence data as well as qualitative data on the geographical and sociocultural risk factors associated with infection, preventive/protective factors, and methods and challenges of diagnostics and treatment. We also extracted WHO Global Health Observatory data representing the countries that reported FBTs during 2010 to 2019.ResultsOne hundred and fifteen studies reporting data on any of the 4 FBTs of focus (Fasciola spp., Paragonimus spp., Clonorchis sp., and Opisthorchis spp.) were included in the final selection. Opisthorchiasis was the most commonly reported and researched FBT, with recorded study prevalence ranging from 0.66% to 88.7% in Asia, and this was the highest FBT prevalence overall. The highest recorded study prevalence for clonorchiasis was 59.6%, reported in Asia. Fascioliasis was reported in all regions, with the highest prevalence of 24.77% reported in the Americas. The least data was available on paragonimiasis, with the highest reported study prevalence of 14.9% in Africa. WHO Global Health Observatory data indicated 93/224 (42%) countries reported at least 1 FBT and 26 countries are likely co-endemic to 2 or more FBTs. However, only 3 countries had conducted prevalence estimates for multiple FBTs in the published literature between 2010 to 2020. Despite differing epidemiology, there were overlapping risk factors for all FBTs in all geographical areas, including proximity to rural and agricultural environments; consumption of raw contaminated food; and limited water, hygiene, and sanitation. Mass drug administration and increased awareness and health education were commonly reported preventive factors for all FBTs. FBTs were primarily diagnosed using faecal parasitological testing. Triclabendazole was the most reported treatment for fascioliasis, while praziquantel was the primary treatment for paragonimiasis, clonorchiasis, and opisthorchiasis. Low sensitivity of diagnostic tests as well as reinfection due to continued high-risk food consumption habits were common factors.ConclusionThis review presents an up-to-date synthesis on the quantitative and qualitative evidence available for the 4 FBTs. The data show a large gap between what is being estimated and what is being reported. Although progress has been made with control programmes in several endemic areas, sustained effort is needed to improve surveillance data on FBTs and identify endemic and high-risk areas for environmental exposures, through a One Health approach, to achieve the 2030 goals of FBT prevention

Comparison of Spatiotemporal Patterns of Historic Natural Anthrax Outbreaks in Minnesota and Kazakhstan (Supplementary data)

1. Minnesota data are summarized by year (1912-2014) by county (n=87) 2. Kazakhstan data are summarized by year (1933-2014) by district (n=163)We compared the spatiotemporal patterns of historic animal Anthrax records in Minnesota and Kazakhstan. In Minnesota, 289 animal Anthrax cases reported between 1912 and 2014 to the Minnesota Board of Animal Health were used in the analysis. For events occurred between 1920 and 1999 the geographical coordinates were obtained using historic aerial images whereas, for those cases that occurred after 2000, coordinates were recorded during site visits. For the Republic of Kazakhstan, laboratory confirmed Anthrax cases reported by the Cadastral register of stationary unfavorable foci on Anthrax between 1933 and 2014 (n=3,997) were analyzed. Because of the sensitivity of providing the actual geographical locations/coordinates, these data on reported Anthrax cases were summarized by administrative unit, by year. The administrative units were Minnesota counties and districts of Kazakhstan. This repository contains two separate EXCEL sheets summarizing the data accordingly.This study was funded in part by: 1) the Minnesota Discovery, Research, and Innovation Economy (MnDRIVE) program of the Office of the Vice President for Research (OVPR) of the University of Minnesota and 2) Scientific Thematic “Zonification of Kazakhstan according to biosecurity categories with regard to dangerous infectious animal diseases” under the Program #249 of funding scientific researches in agro-industry and environmental management by the Ministry of Agriculture of Kazakhstan

Identifying individual animal factors associated with Mycobacterium avium subsp. paratuberculosis (MAP) milk ELISA positivity in dairy cattle in the Midwest region of the United States

Abstract Background Mycobacterium avium subsp. paratuberculosis (MAP) is a widespread chronic disease of ruminants that causes severe economic losses to the dairy cattle industry worldwide. The objective of this study was to evaluate the association between individual cow MAP-ELISA and relevant milk production predictors in dairy cattle using data routinely collected as part of quality and disease control programs in the Midwest region of the U.S. Milk ELISA results of 45,652 animals from 691 herds from November 2014 to August 2016 were analyzed. Results The association between epidemiological and production factors and ELISA results for MAP in milk was quantified using four individual-level mixed multivariable logistic regression models that accounted for clustering of animals at the farm level. The four fitted models were one global model for all the animals assessed here, irrespective of age, and one for each of the categories of  8 year-old cattle, respectively. A small proportion (4.9%; n = 2222) of the 45,652 tested samples were MAP-seropositive. Increasing age of the animals and higher somatic cell count (SCC) were both associated with increased odds for MAP positive test result in the model that included all animals, while milk production, milk protein and days in milk were negatively associated with MAP milk ELISA. Somatic cell count was positively associated with an increased risk in the models fitted for  26 × 1000/ml), low milk production and within the first 60 days of lactation may maximize the odds of detecting seropositive animals. These results could be useful in helping to design better surveillance strategies based in testing of milk

Adapting an Atmospheric Dispersion Model to Assess the Risk of Windborne Transmission of Porcine Reproductive and Respiratory Syndrome Virus between Swine Farms

Modeling the windborne transmission of aerosolized pathogens is challenging. We adapted an atmospheric dispersion model (ADM) to simulate the windborne dispersion of porcine reproductive and respiratory syndrome virus (PRRSv) between swine farms. This work focuses on determining ADM applicable parameter values for PRRSv through a literature and expert opinion-based approach. The parameters included epidemiological features of PRRSv, characteristics of the aerosolized particles, and survival of aerosolized virus in relation to key meteorological features. A case study was undertaken to perform a sensitivity analysis on key parameters. Farms experiencing ongoing PRRSv outbreaks were assigned as particle emitting sources. The wind data from the North American Mesoscale Forecast System was used to simulate dispersion. The risk was estimated semi-quantitatively based on the median daily deposition of particles and the distance to the closest emitting farm. Among the parameters tested, the ADM was most sensitive to the number of particles emitted, followed by the model runtime, and the release height was the least sensitive. Farms within 25 km from an emitting farm were at the highest risk; with 53.66% being within 10 km. An ADM-based risk estimation of windborne transmission of PRRSv may inform optimum time intervals for air sampling, plan preventive measures, and aid in ruling out the windborne dispersion in outbreak investigations

Additional file 1: of Identifying individual animal factors associated with Mycobacterium avium subsp. paratuberculosis (MAP) milk ELISA positivity in dairy cattle in the Midwest region of the United States

Estimated ratios and coefficients with p-values of the fixed effects on factors associated with animal MAP-ELISA results from individual milk sample for young cows (26,153 animals), adult cows (18,384 animals) from four states in the north of USA. (DOCX 21 kb

Quantifying and predicting antimicrobials and antimicrobial resistance genes in waterbodies through a holistic approach:a study in Minnesota, United States

The environment plays a key role in the spread and persistence of antimicrobial resistance (AMR). Antimicrobials and antimicrobial resistance genes (ARG) are released into the environment from sources such as wastewater treatment plants, and animal farms. This study describes an approach guided by spatial mapping to quantify and predict antimicrobials and ARG in Minnesota’s waterbodies in water and sediment at two spatial scales: macro, throughout the state, and micro, in specific waterbodies. At the macroscale, the highest concentrations across all antimicrobial classes were found near populated areas. Kernel interpolation provided an approximation of antimicrobial concentrations and ARG abundance at unsampled locations. However, there was high uncertainty in these predictions, due in part to low study power and large distances between sites. At the microscale, wastewater treatment plants had an effect on ARG abundance (sul1 and sul2 in water; blaSHV, intl1, mexB, and sul2 in sediment), but not on antimicrobial concentrations. Results from sediment reflected a long-term history, while water reflected a more transient record of antimicrobials and ARG. This study highlights the value of using spatial analyses, different spatial scales, and sampling matrices, to design an environmental monitoring approach to advance our understanding of AMR persistence and dissemination.</p