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Survey of antibodies to Rift Valley fever virus and associated risk factors in one-humped camels (Camelus dromedarius) slaughtered in Maiduguri abattoir, Borno State, Nigeria.

Hassan Ismail MusaCaleb Ayuba KudiMuhammad Mamman GashuaAbubakar Sadiq MuhammadAbdulyeken Olawale TijjaniAdamu Saleh SaiduSani MohammedSaleh Mohammed JajereShuaibu Gidado Adamu
Published in: Tropical animal health and production (2021)
Rift Valley fever (RVF) is an emerging mosquito-borne zoonosis that threatens public health and animal agriculture in the endemic areas causing devastating epizootics characterized by abortion storms and high mortalities, especially in newborn animals. A cross-sectional study was conducted to determine the seroprevalence and investigate risk factors associated with exposure to the virus in camels slaughtered in Maiduguri abattoir, Borno State of Nigeria. Camels presented for slaughtered were sampled and data on age, sex, source or origin, utility, presence of post-mortem lesions, body weights and body condition score were collected. Blood samples were collected and sera were harvested and stored at - 20 °C until tested. The sera were tested using a commercial ELISA kit based on the manufacturer's instructions. The overall seroprevalence in the study was 20.7% (95% CI, 13.6-30.0). The analysis showed no significant differences between the presence of antibodies and variables that included the sex of camels (χ2 = 0.015, df = 1, p = 0.904) and the presence of post-mortem lesion on the carcass (χ2 = 0.009, df = 1, p = 0.925). There were significant differences between presence of antibodies and three variables that included the age (χ2 = 4.89, df = 1, p = 0.027), the source (χ2 = 7.077, df = 2, p = 0.029) and the main utility (χ2 = 8.057, df = 3, p = 0.045) of the camels. It was concluded that camels presented for slaughter at the Maiduguri abattoir have evidence of exposure to the RVF virus and maybe means of transmission of the virus. Regular monitoring and control of transboundary animal movements were recommended in the study area.
Keyphrases
  • public health
  • machine learning
  • big data
  • risk factors
  • zika virus
  • aedes aegypti
  • dengue virus