| Autor: |
Rodríguez-Escolar, Iván, Balmori-de la Puente, Alfonso, Collado-Cuadrado, Manuel, Bravo-Barriga, Daniel, Delacour-Estrella, Sarah, Enrique Hernández-Lambraño, Ricardo, Sánchez Agudo, José Ángel, Morchón, Rodrigo |
| Předmět: |
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| Zdroj: |
Frontiers in Veterinary Science; 2024, p1-11, 11p |
| Abstrakt: |
Canine leishmaniosis, caused by the protozoan parasite Leishmania infantum, is a cosmopolitan vector-borne zoonosis, transmitted principally by Phlebotomus perniciosus in Spain and Portugal, where it is considered an endemic disease. Ecoinformatics tools such as ecological niche models (ENM) have been successfully tested to model the distribution of the risk of infection of different parasitosis as they take into account environmental variables vital for their survival. The risk map proposed in this study combines the potential distribution of Ph. perniciosus in the Iberian Peninsula and the calculation of the infection rate of the parasite in the vector to model the risk of contracting the disease in a more realistic way. In fact, this weighting strategy improves the predictive power of the resulting model (R² = 0.42, p = < 0.01) compared to the Ph. perniciosus ENM model alone (R² = 0.13, p > 0.05). The places with the highest risk of transmission are the southwest and central peninsular area, as well as the Mediterranean coast, the Balearic Islands and the Ebro basin, places where the ideal habitat of Ph. perniciosus and the infection rate is also high. In the case of future projections under climate change scenarios, an increase in the risk of infection by L. infantum can be observed in most of the territory (4.5% in 2040, 71.6% in 2060 and 63% in 2080), mainly in the northern part of the peninsula. The use of ENMs and their weighting with the infection rate in Ph. perniciosus is a useful tool in predicting the risk of infection for L. infantum in dogs for a given area. In this way, a more complete model can be obtained to facilitate prevention and control. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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