Newcastle disease virus transmission dynamics in wild peridomestic birds in the United Arab Emirates
Abstract To understand the dynamics of a pathogen in an animal population, one must assess how the infection status of individuals changes over time. With wild animals, this can be very challenging because individuals can be difficult to trap and sample, even more so since they are tested with imper...
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Autores principales: | , , , , , |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/e9b6a8a791bf48cba898f89d0278cf33 |
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Sumario: | Abstract To understand the dynamics of a pathogen in an animal population, one must assess how the infection status of individuals changes over time. With wild animals, this can be very challenging because individuals can be difficult to trap and sample, even more so since they are tested with imperfect diagnostic techniques. Multi-event capture-recapture models allow analysing longitudinal capture data of individuals whose infection status is assessed using imperfect tests. In this study, we used a two-year dataset from a longitudinal field study of peridomestic wild bird populations in the United Arab Emirates during which thousands of birds from various species were captured, sampled and tested for Newcastle disease virus exposure using a serological test. We developed a multi-event capture-recapture model to estimate important demographic and epidemiological parameters of the disease. The modelling outputs provided important insights into the understanding of Newcastle disease dynamics in peridomestics birds, which varies according to ecological and epidemiological parameters, and useful information in terms of surveillance strategies. To our knowledge, this study is the first attempt to model the dynamics of Newcastle disease in wild bird populations by combining longitudinal capture data and serological test results. Overall, it showcased that multi-event capture-recapture models represent a suitable method to analyse imperfect capture data and make reliable inferences on infectious disease dynamics in wild populations. |
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