Multivariable binary logistic regression model to predict risk factors of peste des petits ruminants in goat and sheep

Abstract

Peste des petits ruminants (PPR) highly contagious illness that affects domestic and wild small ruminants, causing significant economic losses. The goal of this study was to use a multivariable logistic regression model to determine risk factors for PPR. A total of 113 healthy non-vaccinated goats and sheep (63 goats and 50 sheep) more than five months (1st group from 5-12 months,2nd group above 12 months), subject to a seroprevalence study by competitive ELISA which was used to detect antibodies against PPRV antibodies in serum sample during the period between April 2018 and March 2019. The incidence of PPR in autumn was significantly increased compared with the spring. Additionally, animals had a 4.08 more likelihood of being infected with PPR in the autumn compared with the spring season. There was a significant difference between male and female groups; The female group had 5.236 times increased odds of being infected with PPR than the male group. Moreover, the old age group had 2.771 times higher odds of being infected with PPR than the young age group. On the other hand, the test model found no evidence to support any significant differences between sheep and goat species. According to finding, PPR is common in females and mature small ruminants. Furthermore, throughout the spring season, the incidence of PPR was significantly reduced. Indeed, the current study may help plan an effective vaccination program against the PRP disease in Egypt.