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DOI 10.1017/s0031182021001761

Медицинская библиотека · Parasitology · 2021

Exploration of factors associated with spatial-temporal veterinary surveillance diagnoses of rumen fluke (Calicophoron daubneyi) infections in ruminants using zero-inflated mixed modelling.

Jones Rhys Aled, Williams Hefin Wyn, Mitchell Sian, Robertson Sara, Macrelli Michele

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

Rumen fluke (Calicophoron daubneyi) has emerged as a prominent parasite of ruminants in Europe over the past decades. Epidemiological questions remain regarding this observed increase in prevalence as well as the prospect for future paramphistomosis risk. This study aimed to identify factors associated with the temporal−spatial prevalence of rumen fluke as measured by veterinary surveillance in a temperate region using zero-inflated negative binomial mixed modelling. Modelling revealed that summer rainfall, raindays and sunshine hours and mean winter temperature as significant positively associated climate variables for rumen fluke prevalence over space and time (P < 0.05). Rumen fluke prevalence was also higher in counties with higher cattle/sheep densities and was positively associated with rumen fluke case rates in the previous years (P < 0.05). Equivalent models for fasciolosis prevalence revealed no significant association with winter temperature and sunshine hours, (P > 0.05). These results confirm a strong association between rainfall and the prevalence of both fluke species in a temperate environment, likely due to the role of Galba truncatula as their intermediate snail host. It also highlights the potential added importance of winter temperature and sunshine hours in rumen fluke epidemiology when compared to liver fluke.

Для цитирования: Jones Rhys Aled, Williams Hefin Wyn, Mitchell Sian, Robertson Sara, Macrelli Michele. Exploration of factors associated with spatial-temporal veterinary surveillance diagnoses of rumen fluke (Calicophoron daubneyi) infections in ruminants using zero-inflated mixed modelling.. Parasitology. 2021. DOI: 10.1017/s0031182021001761.

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