Predicting and reducing potential parasite infection between migratory livestock and resident Asiatic ibex of Pin valley, India.

Disease cross-transmission between wild and domestic ungulates can negatively impact livelihoods and wildlife conservation. In Pin valley, migratory sheep and goats share pastures seasonally with the resident Asiatic ibex (Capra sibirica), leading to potential disease cross-transmission. Focussing on gastro-intestinal nematodes (GINs) as determinants of health in ungulates, we hypothesized that infection on pastures would increase over summer from contamination by migrating livestock. Consequently, interventions in livestock that are well-timed should reduce infection pressure for ibex. Using a parasite life-cycle model, that predicts infective larval availability, we investigated GIN transmission dynamics and evaluated potential interventions. Migratory livestock were predicted to contribute most infective larvae onto shared pastures due to higher density and parasite levels, driving infections in both livestock and ibex. The model predicted a c.30-day antiparasitic intervention towards the end of the livestock's time in Pin would be most effective at reducing GINs in both hosts. Albeit with the caveats of not being able to provide evidence of interspecific parasite transmission due to the inability to identify parasite species, this case demonstrates the usefulness of our predictive model for investigating parasite transmission in landscapes where domestic and wild ungulates share pastures. Additionally, it suggests management options for further investigation.

Distribution of ticks in the Western Palearctic: an updated systematic review (2015-2021).

BACKGROUND: The distributions of ticks and tick-borne pathogens are thought to have changed rapidly over the last two decades, with their ranges expanding into new regions. This expansion has been driven by a range of environmental and socio-economic factors, including climate change. Spatial modelling is being increasingly used to track the current and future distributions of ticks and tick-borne pathogens and to assess the associated disease risk. However, such analysis is dependent on high-resolution occurrence data for each species. To facilitate such analysis, in this review we have compiled georeferenced tick locations in the Western Palearctic, with a resolution accuracy under 10 km, that were reported between 2015 and 2021 METHODS: The PubMed and Web of Science databases were searched for peer-reviewed papers documenting the distribution of ticks that were published between 2015 and 2021, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The papers were then screened and excluded in accordance with the PRISMA flow chart. Coordinate-referenced tick locations along with information on identification and collection methods were extracted from each eligible publication. Spatial analysis was conducted using R software (version 4.1.2). RESULTS: From the 1491 papers identified during the initial search, 124 met the inclusion criteria, and from these, 2267 coordinate-referenced tick records from 33 tick species were included in the final dataset. Over 30% of articles did not record the tick location adequately to meet inclusion criteria, only providing a location name or general location. Among the tick records, Ixodes ricinus had the highest representation (55%), followed by Dermacentor reticulatus (22.1%) and Ixodes frontalis (4.8%). The majority of ticks were collected from vegetation, with only 19.1% collected from hosts. CONCLUSIONS: The data presented provides a collection of recent high-resolution, coordinate-referenced tick locations for use in spatial analyses, which in turn can be used in combination with previously collated datasets to analyse the changes in tick distribution and research in the Western Palearctic. In the future it is recommended that, where data privacy rules allow, high-resolution methods are routinely used by researchers to geolocate tick samples and ensure their work can be used to its full potential.

Prevalence of Human and Animal Fasciolosis in Butajira and Gilgel Gibe Health Demographic Surveillance System Sites in Ethiopia.

Fasciolosis is regarded as a major challenge to livestock productivity worldwide, but the burden of disease in humans has only started to receive some attention in the past three decades. The aim of this study was to determine the prevalence of human and animal fasciolosis and its determinant factors in the Gilgel Gibe and Butajira Health and Demographic Surveillance System (HDSS) sites in Ethiopia. A study was undertaken among 389 households across the two sites. Face-to-face interviews were conducted to investigate the knowledge, attitudes and practices of households with regard to fasciolosis. Stools from 377 children aged 7-15 years, and 775 animals (cattle, goats and sheep) were analyzed using a proprietary Fasciola hepatica (F. hepatica) coproantigen ELISA kit. The prevalence of fasciolosis in children was 0.5% and 1% in Butajira and Gilgel Gibe HDSS sites, respectively. The overall prevalence of animal fasciolosis was 29%, 29.2%, and 6% among cattle, sheep, and goats, respectively. More than half of the respondents from Gilgel Gibe (59%, n = 115) did not know that humans can be infected with F. hepatica. The majority of respondents in Gilgel Gibe (n = 124, 64%) and Butajira (n = 95, 50%) did not know the transmission route for fasciolosis. Grazing animals were 7 times more likely to be infected with fasciolosis than animals in cut-and-carry production systems (adjusted odds ratio [AOR] = 7.2; 95% confidence interval [CI]: 3.91-13.17). The findings indicated a lack of knowledge amongst local populations about fasciolosis. Thus, there is a need for public health awareness campaigns about fasciolosis in the study areas.

Effects of Land-Use and Environmental Factors on Snail Distribution and Trematode Infection in Ethiopia.

Freshwater snails are intermediate hosts for several snail-borne diseases affecting humans and animals. Understanding the distribution of snail intermediate hosts and their infection status is very important to plan and implement effective disease prevention and control interventions. In this study, we determined the abundance, distribution, and trematode infection status of freshwater snails in two agro-ecological zones of Ethiopia. We sampled snails from 13 observation sites and examined them for trematode infections using a natural cercarial shedding method. A redundancy analysis (RDA) was used to examine the relationship between snail abundance and environmental variables. Overall, a total of 615 snails belonging to three species were identified. Lymnea natalensis and Bulinus globosus were the dominant snail species, representing 41% and 40% of the total collection, respectively. About one-third of the total snail population (33%) shed cercariae. The cercariae species recorded were Xiphidiocercaria, Brevifurcate apharyngeate distome (BAD), Echinostome, and Fasciola. Snail species were found in high abundance in aquatic habitats located in the agricultural landscape. Therefore, land-use planning and protection of aquatic habitats from uncontrolled human activities and pollution can be considered as important strategies to prevent and control the spread of snail-borne diseases in the region.

An improved model for the population dynamics of cattle gastrointestinal nematodes on pasture: parameterisation and field validation for Ostertagia ostertagi and Cooperia oncophora in northern temperate zones.

Gastrointestinal nematodes (GIN) are amongst the most important pathogens of grazing ruminants worldwide, resulting in negative impacts on cattle health and production. The dynamics of infection are driven in large part by the influence of climate and weather on free-living stages on pasture, and computer models have been developed to predict infective larval abundance and guide management strategies. Significant uncertainties around key model parameters limits effective application of these models to GIN in cattle, however, and these parameters are difficult to estimate in natural populations of mixed GIN species. In this paper, recent advances in molecular biology, specifically ITS-2 rDNA 'nemabiome' metabarcoding, are synthesised with a modern population dynamic model, GLOWORM-FL, to overcome this limitation. Experiments under controlled conditions were used to estimate rainfall constraints on migration of infective L3 larvae out of faeces, and their survival in faeces and soil across a temperature gradient, with nemabiome metabarcoding data permitting species-specific estimates for Ostertagia ostertagi and Cooperia oncophora in mixed natural populations. Results showed that L3 of both species survived well in faeces and soil between 0 and 30 °C, and required at least 5 mm of rainfall daily to migrate out of faeces, with the proportion migrating increasing with the amount of rainfall. These estimates were applied within the model using weather and grazing data and use to predict patterns of larval availability on pasture on three commercial beef farms in western Canada. The model performed well overall in predicting the observed seasonal patterns but some discrepancies were evident which should guide further iterative improvements in model development and field methods. The model was also applied to illustrate its use in exploring differences in predicted seasonal transmission patterns in different regions. Such predictive modelling can help inform evidence-based parasite control strategies which are increasingly needed due climate change and drug resistance. The work presented here also illustrates the added value of combining molecular biology and population dynamics to advance predictive understanding of parasite infections.

Seasonal epidemiology of gastrointestinal nematodes of cattle in the northern continental climate zone of western Canada as revealed by internal transcribed spacer-2 ribosomal DNA nemabiome barcoding.

BACKGROUND: Gastrointestinal nematode (GIN) epidemiology is changing in many regions of the world due to factors such as global warming and emerging anthelmintic resistance. However, the dynamics of these changes in northern continental climate zones are poorly understood due to a lack of empirical data. METHODS: We studied the accumulation on pasture of free-living infective third-stage larvae (L3) of different GIN species from fecal pats deposited by naturally infected grazing cattle. The field study was conducted on three organic farms in Alberta, western Canada. Grass samples adjacent to 24 fecal pats were collected from each of three different pastures on each farm. Internal transcribed spacer-2 nemabiome metabarcoding was used to determine the GIN species composition of the harvested larvae. The rotational grazing patterns of the cattle ensured that each pasture was contaminated only once by fecal pat deposition. This design allowed us to monitor the accumulation of L3 of specific GIN species on pastures under natural climatic conditions without the confounding effects of pasture recontamination or anthelmintic treatments. RESULTS: In seven out of the nine pastures, grass L3 counts peaked approximately 9 weeks after fecal deposition and then gradually declined. However, a relatively large number of L3 remained in the fecal pats at the end of the grazing season. Nemabiome metabarcoding revealed that Cooperia oncophora and Ostertagia ostertagi were the two most abundant species on all of the pastures and that the dynamics of larval accumulation on grass were similar for both species. Daily precipitation and temperature across the whole sampling period were similar for most of the pastures, and multiple linear regression showed that accumulated rainfall 1 week prior to sample collection had a significant impact on the pasture L3 population, but accumulated rainfall 3 weeks prior to sample collection did not. CONCLUSIONS: The results suggest that the pasture L3 population was altered by short-term microclimatic conditions conducive for horizontal migration onto grass. Overall, the results show the importance of the fecal pat as a refuge and reservoir for L3 of cattle GIN on western Canadian pastures, and provide an evidence base for the risk assessment of rotational grazing management in the region.

Constraints of using historical data for modelling the spatial distribution of helminth parasites in ruminants.

Dicrocoelium dendriticum is a trematode that infects ruminant livestock and requires two different intermediate hosts to complete its lifecycle. Modelling the spatial distribution of this parasite can help to improve its management in higher risk regions. The aim of this research was to assess the constraints of using historical data sets when modelling the spatial distribution of helminth parasites in ruminants. A parasitological data set provided by CREMOPAR (Napoli, Italy) and covering most of Italy was used in this paper. A baseline model (Random Forest, VECMAP®) using the entire data set was first used to determine the minimal number of data points needed to build a stable model. Then, annual distribution models were computed and compared with the baseline model. The best prediction rate and statistical output were obtained for 2012 and the worst for 2016, even though the sample size of the former was significantly smaller than the latter. We discuss how this may be explained by the fact that in 2012, the samples were more evenly geographically distributed, whilst in 2016 most of the data were strongly clustered. It is concluded that the spatial distribution of the input data appears to be more important than the actual sample size when computing species distribution models. This is often a major issue when using historical data to develop spatial models. Such data sets often include sampling biases and large geographical gaps. If this bias is not corrected, the spatial distribution model outputs may display the sampling effort rather than the real species distribution.

TITLE: Contraintes liées à l'utilisation de données historiques pour la modélisation de la distribution spatiale des helminthes parasites chez les ruminants. ABSTRACT: Dicrocoelium dendriticum est un trématode qui infecte les ruminants et nécessite deux hôtes intermédiaires différents pour terminer son cycle de vie. La modélisation de la distribution spatiale de ce parasite peut aider à améliorer sa gestion dans les régions à haut risque. L'objectif de cette recherche était d'évaluer les contraintes liées à l'utilisation d'ensembles de données historiques lors de la modélisation de la distribution spatiale des helminthes parasites chez les ruminants. Un ensemble de données parasitologiques fourni par CREMOPAR (Naples, Italie) et couvrant la majeure partie de l'Italie a été utilisé dans cet article. Un modèle de base (Random Forest, VECMAP®) utilisant l'ensemble des données a d'abord été utilisé pour déterminer le nombre minimal de points de données nécessaires pour construire un modèle stable. Ensuite, des modèles de distribution annuelle ont été calculés et comparés au modèle de référence. Le meilleur taux de prédiction et le meilleur résultat statistique ont été obtenus pour 2012 et le plus mauvais pour 2016, malgré le fait que la taille de l'échantillon du premier était nettement plus petite que celle du second. Nous discutons comment cela peut s'expliquer par le fait qu'en 2012, les échantillons étaient plus uniformément répartis géographiquement, alors qu'en 2016, la plupart des données étaient fortement regroupées. On conclut que la distribution spatiale des données d'entrée semble être plus importante que la taille réelle de l'échantillon lors du calcul des modèles de distribution des espèces. C'est souvent un problème majeur pour développer des modèles spatiaux quand on utilise des données historiques. Ces ensembles de données comportent souvent des biais d'échantillonnage et de grandes lacunes géographiques. Si ce biais n'est pas corrigé, les résultats du modèle de distribution spatiale peuvent représenter l'effort d'échantillonnage plutôt que la distribution réelle des espèces.

100 Questions in Livestock Helminthology Research.

An elicitation exercise was conducted to collect and identify pressing questions concerning the study of helminths in livestock, to help guide research priorities. Questions were invited from the research community in an inclusive way. Of 385 questions submitted, 100 were chosen by online vote, with priority given to open questions in important areas that are specific enough to permit investigation within a focused project or programme of research. The final list of questions was divided into ten themes. We present the questions and set them briefly in the context of the current state of knowledge. Although subjective, the results provide a snapshot of current concerns and perceived priorities in the field of livestock helminthology, and we hope that they will stimulate ongoing or new research efforts.

A mechanistic hydro-epidemiological model of liver fluke risk.

The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and have limited ability to guide interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission at a daily time step and 25 m resolution, explicitly linking the parasite life cycle to key weather-water-environment conditions. Using epidemiological data, we show that the model can reproduce observed infection levels in time and space for two case studies in the UK. To overcome data limitations, we propose a calibration approach combining Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. The simulated disease dynamics agree with information from the literature, and comparison with a widely used empirical risk index shows that the new model provides better insight into the time-space patterns of infection, which will be valuable for decision support.