Occurrence and risk factors of brucellosis among domestic animals: an artificial neural network approach.

Abstract

Brucellosis is a zoonotic infectious disease with a worldwide distribution. This cross-sectional study aimed to determine the occurrence of Brucella in milk and blood samples of ruminants and the importance factors associated with animal brucellosis in Eastern Iran. A total of 200 paired samples, including blood (100) and milk (100), were obtained from the goats, sheep, and cows in Eastern Iran. Serum agglutination (SAT) and 2-mercapto ethanol (2-ME) tests were performed on the sera. A multiplex-polymerase chain reaction (m-PCR) assay was performed to identify the following species of Brucella, including B. abortus biovar 1, 2, 4, 3b, 5, 6, and 9, B. abortus S19, B. melitensis, and B. melitensis Rev.1 vaccine strain. B. abortus RB51 vaccine strain was also investigated in a second PCR assay. Risk factors for infection with Brucella spp. including the effect of abortion, contact with the wild animals, herd type, age, and previous vaccination in predicting blood contamination with B. abortus biovar 1, 2, and 4 were modeled by use of the artificial neural network. A total of 23 samples were seropositive regarding SAT and 2-ME tests. In total, B. abortus was detected in 35% and 15% of blood and milk samples, respectively, by the m-PCR assay. One sample of each of milk and blood was detected to have B. melitensis. Some samples were simultaneously contaminated with two Brucella species or two biovars of B. abortus. B. abortus S19 and B. melitensis Rev.1 vaccine strains were also detected in milk and blood samples. The sensitivity and specificity of the ANN model were 81% and 62%, respectively. In conclusion, B. abortus had higher frequency than B. melitensis in blood and milk samples. ANN determined herd type, previous vaccination, and age of the animal as the largest predictors of blood contamination with B. abortus.