The outputs of epidemiological models are strongly related to the structure of the model and input parameters. The latter are defined by fitting theoretical concepts to actual data derived from field or experimental studies. However, some parameters may remain difficult to estimate and are subject to uncertainty or sensitivity analyses to determine their variation range and their global impact on model outcomes. As such, the evaluation of immunity duration is often a puzzling issue requiring long-term follow-up data that are, most of time, not available. The present analysis aims at characterizing the kinetics of antibodies against Porcine Reproductive and Respiratory Syndrome virus (PRRSv) from longitudinal data sets. The first data set consisted in the serological follow-up of 22 vaccinated gilts during 21 weeks post-vaccination (PV). The second one gathered the maternally derived antibodies (MDAs) kinetics in piglets from three different farms up to 14 weeks of age. The peak of the PV serological response against PRRSv was reached 6.9 weeks PV on average with an average duration of antibodies persistence of 26.5 weeks. In the monitored cohort of piglets, the duration of passive immunity was found relatively short, with an average duration of 4.8 weeks. The level of PRRSv-MDAs was found correlated with the dams' antibody titer at birth, and the antibody persistence was strongly related to the initial MDAs titers in piglets. These results evidenced the importance of PRRSv vaccination schedule in sows, to optimize the delivery of antibodies to suckling piglets. These estimates of the duration of active and passive immunity could be further used as input parameters of epidemiological models to analyze their impact on the persistence of PRRSv within farms.
CITATION STYLE
Andraud, M., Fablet, C., Renson, P., Eono, F., Mahé, S., Bourry, O., & Rose, N. (2018). Estimating parameters related to the lifespan of passively transferred and vaccine-induced Porcine Reproductive and Respiratory syndrome virus type I antibodies by modeling field data. Frontiers in Veterinary Science, 5(JAN). https://doi.org/10.3389/fvets.2018.00009
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