Assessment of individual and population-based sampling for detection of Influenza A virus RNA in breeding swine herds — D.C.A. Moraes (2024) | RDL Network
Assessment of individual and population-based sampling for detection of Influenza A virus RNA in breeding swine herds
Article 2024 en
Authors
DM
D.C.A. Moraes
PG
Phillip C. Gauger
OO
Onyekachukwu Osemeke
Abstract
1 min read
This study compared the probability of IAV RNA detection between selected individual and population-based samples. A 3500-sow breeding herd was sampled for matched sets (n = 57) of FOF, udder wipes, sow nasal wipes, individual piglet nasal wipes, and drinker wipes and tested by RT-rtPCR for IAV RNA detection. Overall, 57.9% (33/57) of FOF samples, 49.1% (28/57) of udder wipes, 28.1% (16/57) of sow nasal wipes, 15.8% (9/57) of drinker wipes and 66.6% (38/57) of the individual piglet nasal wipes tested positive for IAV RNA. FOF had a Kappa value of 0.81, a near-perfect agreement compared to individual piglet nasal wipes. Udder wipes revealed a Kappa value of 0.65, a substantial agreement with individual piglet nasal wipes. The other sample types had a fair agreement with individual piglet nasal wipes (Kappa values <0.28). These results validate FOF as an efficient alternative population-based sample type for IAV surveillance in the breeding herd. The proportion of positive piglets within litter by room A had 91% positivity (20/22), room B had 70% (17/24), and room C had 9% (1/11) positivity. The findings also highlight the importance of testing different farrowing rooms in the same breeding herd to strengthen IAV surveillance.
Daniel de Almeida Moraes, Phillip C. Gauger, Onyekachukwu Osemeke, Cesar A. A. Moura, Isadora F. Machado, Guilherme Cezar, Rodrigo C. Paiva, Mafalda Pedro Mil-Homens, Marcelo Nunes de Almeida, Alexandro D. Ramirez, Gustavo S Silva, Daniel Linhares
Onyekachukwu Osemeke, Guilherme Cezar, Rodrigo C. Paiva, Daniel C. A. Moraes, Isadora F. Machado, Edison Magalhaes, Ana Paula S. Poeta Silva, Mafalda Pedro Mil-Homens, Peng Li, Bharat Jayaraman, Giovani Trevisan, Gustavo S Silva, Phillip C. Gauger, Daniel Linhares
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