A machine learning approach for modelling the occurrence of Galba truncatula as the major intermediate host for Fasciola hepatica in Switzerland

Water temperature and microenvironmental factors predict the presence and detection of the snail host of Fasciola hepatica in Andean Patagonia

The transmission of Fasciola hepatica occurs only where there are -or recently were- aquatic or amphibious snails of the Lymnaeidae family, the intermediate host of this parasite. Direct detection of these snails is time-consuming and imprecise, hindering accurate and detailed mapping of transmission risk. To identify which microenvironmental factors could be used as proxies for the occurrence of the lymnaeid snail Galba viator, a major intermediate host in South America, a total of 183 1-m2 quadrants across diverse water bodies in an endemic area in Andean Patagonia were manually timed-searched for snails and microenvironmental variables were registered. Data was analyzed using a Bayesian hierarchical occupancy model that assessed the effects of the microenvironmental variables on the presence of snails while considering imperfect snail detection. The model estimated that G. viator predominantly inhabits shallow aquatic environments, in the presence of grasses, where snails of the genus Biomphalaria are also detected, and with scarce tree canopy cover. Physical factors affecting occupancy presumably act as proxies for the average water temperature, while the temperature at the time of sampling was found to affect snail detectability. The identified variables are easy, fast, and inexpensive to measure, and can complement management decisions and risk maps based on coarser remote-sensing data, particularly relevant in a context of growing resistance to anthelminthic drugs.

Identification of freshwater snail species and survey of their trematode infections in Ordos, China

In order to investigate and study the species and distribution of freshwater snails in Ordos area of Inner Mongolia, as well as the trematode infection in different periods, and to provide a scientific basis for the effective prevention and control of livestock trematodiasis. In this paper, freshwater snails distributed in Ordos were widely collected for morphological identification, and PCR amplification of freshwater snails COI gene and ITS2 gene was carried out with the help of molecular biology. At the same time, microscopic examination was used to observe the trematode infection of freshwater snails in two different periods from May to July and July to September, and the molecular biology of the trematodes was identified. The results showed that the 1796 freshwater snails collected belonged to two orders, three families and four genera, i.e. Bellamya, Radix, Galba, and Gyraulus. Microscopic examination of snails showed that the infection rate of trematode larvae from July to September was significantly higher than that from May to July. The collected trematodes were identified as five species, namely Cotylurus marcogliesei, Fasciola hepatica, Fasciola gigantica, Paramphistomum cervi, and Parastrigea robusta. The combination of freshwater snail species in Ordos and the infection of trematode in snails showed that a large number of freshwater snails were infected with trematodes, especially from July to September, when there is more rain and suitable climate, which causes serious harm to local livestock.

Random forest classification as a tool in epidemiological modelling: Identification of farm-specific characteristics relevant for the occurrence of Fasciola hepatica on German dairy farms

Fasciola hepatica is an internal parasite of both human and veterinary relevance. In order to control fasciolosis, a multitude of attempts to predict the risk of infection such as risk maps or forecasting models have been developed. These attempts mainly focused on the influence of geo-climatic and meteorological features. Predicting bovine fasciolosis on farm level taking into account farm-specific settings yet remains challenging. In the present study, a new methodology for this purpose, a data-driven machine learning approach using a random forest classification algorithm was applied to a cross-sectional data set of farm characteristics, management regimes, and farmer aspects within two structurally different dairying regions in Germany in order to identify factors relevant for the occurrence of F. hepatica that could predict farm-level bulk tank milk positivity. The resulting models identified farm-specific key aspects in regard to the presence of F. hepatica. In study region North, farm-level production parameters (farm-level milk yield, farm-level milk fat, farm-level milk protein), leg hygiene, body condition (prevalence of overconditioned and underconditioned cows, respectively) and pasture access were identified as features relevant in regard to farm-level F. hepatica positivity. In study region South, pasture access together with farm-level lameness prevalence, farm-level prevalence of hock lesions, herd size, parity, and farm-level milk fat appeared to be important covariates. The stratification of the analysis by study region allows for the extrapolation of the results to similar settings of dairy husbandry. The local, region-specific modelling of F. hepatica presence in this work contributes to the understanding of on-farm aspects of F. hepatica appearance. The applied technique represents a novel approach in this context to model epidemiological data on fasciolosis which allows for the identification of farms at risk and together with additional findings in regard to the epidemiology of fasciolosis, can facilitate risk assessment and deepen our understanding of on-farm drivers of the occurrence of F. hepatica.

Effects of temperature on the life history traits of intermediate host snails of fascioliasis: A systematic review

BACKGROUND

The impact of climate change has led to variations in various biological processes, leading to altered transmission dynamics of infectious diseases, including snail-borne diseases (SBDs). Fascioliasis is one of the neglected zoonotic tropical snail-borne diseases caused by the trematode of the genus Fasciola. This review focused on laboratory experimental and model studies that evaluate the potential effect of temperature change on the ecology and biology of the intermediate host snails (IHS) of Fasciola.

METHODS

A literature search was conducted on Google Scholar, EBSCOhost, and PubMed databases using predefined medical subject heading terms, Boolean operators, and truncation symbols in combination with direct keywords: Fasciolosis AND Temperature, Lymnaea OR Austropeplea OR Radix OR Galba OR Fossaria OR Pseudosuccinea AND growth, fecundity, AND survival at the global scale. Other search terms used were (Fascioliasis AND Temperature), (Lymnaea AND Temperature), (Austropeplea AND Temperature), (Fossaria AND Temperature), (Galba AND Temperature), (Pseudosuccinea AND Temperature), and (Radix AND Temperature).

RESULTS

The final synthesis included thirty-five published articles. The studies reviewed indicated that temperature rise may alter the distribution, and optimal conditions for breeding, growth, and survival of IHS, ultimately resulting in changing the transmission dynamics of fascioliasis. The literature also confirmed that the life history traits of IHS and their interaction with the liver fluke parasites are driven by temperature, and hence climate change may have profound outcomes on the population size of snails, parasite density, and disease epidemiology.

CONCLUSION

We concluded that understanding the impact of temperature on the growth, fecundity, and survival of IHS may broaden our knowledge of the possible effects of climate change and hence inform fascioliasis control programs.

Multinomial logistic regression based on neural networks reveals inherent differences among dairy farms depending on the differential exposure to Fasciola hepatica and Ostertagia ostertagi

Fasciola hepatica and Ostertagia ostertagi are cattle parasites with worldwide relevance for economic outcome as well as animal health and welfare. The on-farm exposure of cattle to both parasites is a function of host-associated, intrinsic, as well as environmental and farm-specific, extrinsic, factors. Even though knowledge on the biology of both parasites exists, sophisticated and innovative modelling approaches can help to deepen our understanding of key aspects fostering the exposure of dairy cows to these pathogens. In the present study, multiple multinomial logistic regression models were fitted via neural networks to describe the differences among farms where cattle were not exposed to either F. hepatica or O. ostertagi, to one parasite, or to both, respectively. Farm-specific production and management characteristics were used as covariates to portray these differences. This elucidated inherent farm characteristics associated with parasite exposure. In both studied regions, pasture access for cows, farm-level milk yield, and lameness prevalence were identified as relevant factors. In region 'South', adherence to organic farming principles was a further covariate of importance. In region 'North', the prevalence of cows with a low body condition score, herd size, hock lesion prevalence, farm-level somatic cell count, and study year appeared to be of relevance. The present study broadens our understanding of the complex epidemiological scenarios that could predict differential farm-level parasite status. The analyses have revealed the importance of awareness of dissimilarities between farms in regard to the differential exposure to F. hepatica and O. ostertagi. This provides solid evidence that dynamics and relevant factors differ depending on whether or not cows are exposed to F. hepatica, O. ostertagi, or to both.