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Foot-and-mouth disease virus transmission dynamics and persistence in a herd of vaccinated dairy cattle in India.

S S HayerK VanderWaalR RanjanJ K BiswalS SubramaniamJ K MohapatraG K SharmaM RoutB B DashB DasB R PrustyA K SharmaCarolina StenfeldtA PerezA H DelgadoM K SharmaLuis L RodriguezB PattnaikJonathan Arzt
Published in: Transboundary and emerging diseases (2017)
Foot-and-mouth disease (FMD) is an important transboundary disease with substantial economic impacts. Although between-herd transmission of the disease has been well studied, studies focusing on within-herd transmission using farm-level outbreak data are rare. The aim of this study was to estimate parameters associated with within-herd transmission, host physiological factors and FMD virus (FMDV) persistence using data collected from an outbreak that occurred at a large, organized dairy farm in India. Of 1,836 regularly vaccinated, adult dairy cattle, 222 had clinical signs of FMD over a 39-day period. Assuming homogenous mixing, a frequency-dependent compartmental model of disease transmission was built. The transmission coefficient and basic reproductive number were estimated to be between 16.2-18.4 and 67-88, respectively. Non-pregnant animals were more likely to manifest clinical signs of FMD as compared to pregnant cattle. Based on oropharyngeal fluid (probang) sampling and FMDV-specific RT-PCR, four of 36 longitudinally sampled animals (14%) were persistently infected carriers 10.5 months post-outbreak. There was no statistical difference between subclinical and clinically infected animals in the duration of the carrier state. However, prevalence of NSP-ELISA antibodies differed significantly between subclinical and clinically infected animals 12 months after the outbreak with 83% seroprevalence amongst clinically infected cattle compared to 69% of subclinical animals. This study further elucidates within-herd FMD transmission dynamics during the acute-phase and characterizes duration of FMDV persistence and seroprevalence of FMD under natural conditions in an endemic setting.
Keyphrases
  • pregnant women
  • magnetic resonance imaging
  • machine learning
  • magnetic resonance
  • deep learning
  • big data