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.

BACKGROUND: Foodborne 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. METHODS: We 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. RESULTS: One 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. CONCLUSION: This 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.

Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression.

Despite ongoing control efforts, rabies remains an endemic zoonotic disease in many countries. Determining high-risk areas and the space-time patterns of rabies spread, as it relates to epidemiologically important factors, can support policymakers and program managers alike to develop evidence-based targeted surveillance and control programs. In this One Health approach which selected Thailand as the example site, the location-based risk of contracting dog-mediated rabies by both human and animal populations was quantified using a Bayesian spatial regression model. Specifically, a conditional autoregressive (CAR) Bayesian zero-inflated Poisson (ZIP) regression was fitted to the reported human and animal rabies case counts of each district, from the 2012-2017 period. The human population was used as an offset. The epidemiologically important factors hypothesized as risk modifiers and therefore tested as predictors included: number of dog bites/attacks, the population of dogs and cats, number of Buddhist temples, garbage dumps, animal vaccination, post-exposure prophylaxis, poverty, and shared administrative borders. Disparate sources of data were used to improve the estimated associations and predictions. Model performance was assessed using cross-validation. Results suggested that accounting for the association between human and animal rabies with number of dog bites/attacks, number of owned and un-owned dogs; shared country borders, number of Buddhist temples, poverty levels, and accounting for spatial dependence between districts, may help to predict the risk districts for dog-mediated rabies in Thailand. The fitted values of the spatial regression were mapped to illustrate the risk of dog-mediated rabies. The cross-validation indicated an adequate performance of the spatial regression model (AUC = 0.81), suggesting that had this spatial regression approach been used to identify districts at risk in 2015, the cases reported in 2016/17 would have been predicted with model sensitivity and specificity of 0.71 and 0.80, respectively. While active surveillance is ideal, this approach of using multiple data sources to improve risk estimation may inform current rabies surveillance and control efforts including determining rabies-free zones, and the roll-out of human post-exposure prophylaxis and anti-rabies vaccines for animals in determining high-risk areas.

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.

Epidemiological characterization of Clonorchis sinensis infection in humans and freshwater fish in Guangxi, China.

BACKGROUND: Clonorchiasis is a widespread yet neglected foodborne disease with over 85% of all cases found in China. Guangxi province, located in southeastern China, ranks among the highest endemic provinces. We explore the epidemiological status and determinants of Clonorchis sinensis (C. sinensis) infection in humans and freshwater fish in Guangxi, China. METHODS: Data on C. sinensis infection in humans from January 2008 to December 2017were extracted from the China Information System for Disease Control and Prevention. An active surveillance of C. sinensis infection in fish was conducted in 2016-2017. County level data including potential environmental, social-economical and behavioral determinants was also collected. Univariate and multivariate logistic regression models were used to explore the determinants of C. sinensis infection in humans and fish. Simple and multiple zero-inflated Poisson regression models were fit to assess the associated factors of clonorchiasis in humans at the county level. RESULTS: Totally, 4526 C. sinensis cases were reported between 2008 and 2017, with an annual prevalencerate of 0.96/100,000 persons. Of 101 counties in Guangxi, 97 reported at least 1 case. Among 2,098 fish samples, 203 (9.7%) from 70 counties contained C. sinensis. The rate was higher in small fish including Pseudorasbora parva (45.3%), Misgurnus anguillicaudatus (41.2%), Hemicculter leuciclus (34.5%), unclassified small fishes (30.9%), Cyprinidae (20.0%), Cirrhinus molitorella (16.4%), Carassius auratus (13.6%) and Cyprinus carpio (13.3%), while it was lower in fish species that are usually used in preparing raw fish dishes including Ctenopharyngodon idellus (3.6%), Spinibarbus denticulatus (3.7%), Monopterus albus (6.4%), Cyprinus carpio (4.4%), Oreochromis mossambicus (3.3%) and Spualiobarbus Curriculus (6.6%). The C. sinensis infection in fish was only associated with fish species. The estimated human clonorchiasis prevalence at the county level was positively associated with raw fish consumption habits and certain rivers. CONCLUSIONS: Clonorchiasis is highly prevalent in both humans and freshwater fish in Guangxi. Environmental, social-economic and behavioral determinants contribute to the high prevalence as well as the significant differential distribution by county. Regular surveillance should be implemented for clonorchiasis to demonstrate the change in epidemiology and burden, which will benefit the design of interventions.

A reanalysis: Do hog farms cause disease in North Carolina neighborhoods?

A 2018 publication reported that communities living near hog Concentrated Animal Feeding Operations (CAFO) in North Carolina, USA have increased negative health outcomes and mortalities. While the authors stated that the associations do not imply causation, speculative interpretation of their results by media and subsequent use as evidence in lawsuits caused detrimental effects on the swine industry. We repeated their study using updated data to evaluate the strength of conclusions and appropriateness of methods used with the ultimate goal of alerting on the impact that study limitations may have when used as evidence. As done in the 2018 study, logistic regression was conducted at the individual level using 2007-2018 data, while presumably correcting for six confounders drawn from zip code or county-level databases. Exposure to CAFOs was defined by categorizing zip codes into three by swine density; where, >1 hogs/km2 (G1), > 232 hogs/km2 (G2), and no hogs (Control). Association with CAFO exposure resulting in mortality, hospital admissions, and emergency department visits were analyzed related to eight conditions (six from the previous study: anemia, kidney disease, infectious diseases, tuberculosis, low birth weight, and we added HIV and diabetes). Re-evaluation identified shortcomings including ecological fallacy, residual confounding, inconsistency of associations, and overestimation of exposure. HIV and diabetes, which are not causally relatable to CAFOs, were also prominent in these neighborhoods likely reflecting underlying systemic health disparities. Hence, we emphasize the need for improved exposure analysis and the importance of responsible interpretation of ecological studies that affect both public health and agriculture.

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.

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

Disease spread in populations is a consequence of the interaction between host, pathogen, and environment, i.e. the epidemiological triad. Yet the influences of each triad component may vary dramatically for different settings. Comparison of environmental, demographic, socio-economic, and historical backgrounds may support tailoring site-specific control measures. Because of the long-term survival of Bacillus anthracis, Anthrax is a suitable example for studying the influence of triad components in different endemic settings. We compared the spatiotemporal patterns of historic animal Anthrax records in two endemic areas, located at northern latitudes in the western and eastern hemispheres. Our goal was to compare the spatiotemporal patterns in Anthrax progression, intensity, direction, and recurrence (disease hot spots), in relation to epidemiological factors and potential trigger events. Reported animal cases in Minnesota, USA (n = 289 cases between 1912 and 2014) and Kazakhstan (n = 3,997 cases between 1933 and 2014) were analyzed using the spatiotemporal directionality test and the spatial scan statistic. Over the last century Anthrax occurrence in Minnesota was sporadic whereas Kazakhstan experienced a long-term epidemic. Nevertheless, the seasonality was comparable between sites, with a peak in August. Declining number of cases at both sites was attributed to vaccination and control measures. The spatiotemporal directionality test detected a relative northeastern directionality in disease spread for long-term trends in Minnesota, whereas a southwestern directionality was observed in Kazakhstan. In terms of recurrence, the maximum timespans between cases at the same location were 55 and 60 years for Minnesota and Kazakhstan, respectively. Disease hotspots were recognized in both settings, with spatially overlapping clusters years apart. Distribution of the spatiotemporal cluster radii between study sites supported suggestion of site-specific control zones. Spatiotemporal patterns of Anthrax occurrence in both endemic regions were attributed to multiple potential trigger events including major river floods, changes in land use, agriculture, and susceptible livestock populations. Results here help to understand the long-term epidemiological dynamics of Anthrax while providing suggestions to the design and implementation of prevention and control programs, in endemic settings.

Evaluating the efficacy of beef slaughter line interventions by quantifying the six major non-O157 Shiga toxin producing Escherichia coli serogroups using real-time multiplex PCR.

Six major Shiga toxin producing Escherichia coli (STEC) serogroups: O26, O103, O145, O111, O121, and O45 have been declared as adulterants in federally inspected raw beef in the USA effective June 4th, 2012 in addition to the routinely tested STEC O157: H7. This study tests a real-time multiplex PCR assay and pooling of the samples to optimize the detection and quantification (prevalence and contamination) of six major non-O157 STEC, regardless of possessing Shiga toxins. To demonstrate the practicality, one large-scale slaughter plant (Plant LS) and one small-scale slaughter plant (Plant SS) located in the Mid-Western USA were sampled, in 2011, before the establishment of 2013 USDA laboratory protocols. Carcasses were sampled at consecutive intervention stations and beef trimmings were collected at the end of the fabrication process. Plant SS had marginally more contaminated samples than Plant LS (p-value 0.08). The post-hide removal wash, steam pasteurization, and lactic acid (≤5%) spray used in Plant LS seemed to reduce the six serogroups effectively, compared to the hot-water wash and 7-day chilling at Plant SS. Compared to the culture isolation methods, quantification of the non-O157 STEC using real-time PCR may be an efficient way to monitor the efficacy of slaughter line interventions.

A Probability Co-Kriging Model to Account for Reporting Bias and Recognize Areas at High Risk for Zebra Mussels and Eurasian Watermilfoil Invasions in Minnesota.

Zebra mussels (ZMs) (Dreissena polymorpha) and Eurasian watermilfoil (EWM) (Myriophyllum spicatum) are aggressive aquatic invasive species posing a conservation burden on Minnesota. Recognizing areas at high risk for invasion is a prerequisite for the implementation of risk-based prevention and mitigation management strategies. The early detection of invasion has been challenging, due in part to the imperfect observation process of invasions including the absence of a surveillance program, reliance on public reporting, and limited resource availability, which results in reporting bias. To predict the areas at high risk for invasions, while accounting for underreporting, we combined network analysis and probability co-kriging to estimate the risk of ZM and EWM invasions. We used network analysis to generate a waterbody-specific variable representing boater traffic, a known high risk activity for human-mediated transportation of invasive species. In addition, co-kriging was used to estimate the probability of species introduction, using waterbody-specific variables. A co-kriging model containing distance to the nearest ZM infested location, boater traffic, and road access was used to recognize the areas at high risk for ZM invasions (AUC = 0.78). The EWM co-kriging model included distance to the nearest EWM infested location, boater traffic, and connectivity to infested waterbodies (AUC = 0.76). Results suggested that, by 2015, nearly 20% of the waterbodies in Minnesota were at high risk of ZM (12.45%) or EWM (12.43%) invasions, whereas only 125/18,411 (0.67%) and 304/18,411 (1.65%) are currently infested, respectively. Prediction methods presented here can support decisions related to solving the problems of imperfect detection, which subsequently improve the early detection of biological invasions.

Rapid and reliable detection of Shiga toxin-producing Escherichia coli by real-time multiplex PCR.

Escherichia coli O26, O45, O103, O111, O121, O145, and O157 are the predominant Shiga toxin-producing E. coli (STEC) serogroups implicated in outbreaks of human foodborne illness worldwide. The increasing prevalence of these pathogens has important public health implications. Beef products have been considered a main source of foodborne human STEC infections. Robust and sensitive methods for the detection and characterization of these pathogens are needed to determine prevalence and incidence of STEC in beef processing facilities and to improve food safety interventions aimed at eliminating STEC from the food supply. This study was conducted to develop Taqman real-time multiplex PCR assays for the screening and rapid detection of the predominant STEC serogroups associated with human illness. Three serogroup-specific assays targeted the O-antigen gene clusters of E. coli O26 (wzy), O103 (wzx), and O145 (wzx) in assay 1, O45 (wzy), O111 (manC), and O121 (wzx) in assay 2, and O157 (rfbE) in assay 3. The uidA gene also was included in the serogroup-specific assays as an E. coli internal amplification control. A fourth assay was developed to target selected virulence genes for Shiga toxin (stx(1) and stx(2)), intimin (eae), and enterohemolysin (ehxA). The specificity of the serogroup and virulence gene assays was assessed by testing 100 and 62 E. coli strains and non-E. coli control strains, respectively. The assays correctly detected the genes in all strains examined, and no cross-reactions were observed, representing 100 % specificity. The detection limits of the assays were 10(3) or 10(4) CFU/ml for pure cultures and artificially contaminated fecal samples, and after a 6-h enrichment period, the detection limit of the assays was 10(0) CFU/ml. These results indicate that the four real-time multiplex PCR assays are robust and effective for the rapid and reliable detection of the seven predominant STEC serogroups of major public health concern and the detection of their virulence genes.