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Johne's disease is a chronic disease with great concern in ruminants and caused by Mycobacterium avium subsp. paratuberculosis (MAP). A cross-sectional study was conducted from February 2019 to January 2020 to estimate the prevalence of MAP infection among camels which are kept in three governorates in Nile Delta of Egypt. A total of 440 serum samples were examined by ELISA for detection of MAP antibodies. The multivariable logistic regression model was performed to determine the associated risk factors for MAP infection in examined camels. Overall, the seroprevalence of MAP infection was found to be 7.5% among examined camels. The multivariable logistic regression model was performed to determine the associated risk factors for MAP infection in examined camels. The main findings revealed that the risk of getting MAP infection increased among elder camels (>10 years old) with signs of diarrhea, having communal water source and in camels grazing in the same pasture (odds ratio >1). However, geographic location, sex and contact with cattle had not significant impact regarding to seroprevalence of MAP infection in camels. The present findings confirm presence of MAP among camels which is a potential risk factor for contamination of environment and spreading of infection. Therefore, further studies for detection of infected animals in early stage are needed beside the estimated risk factors in this study to build an efficient control program. [ABSTRACT FROM AUTHOR]

Machine learning approaches have been increasingly utilized in the field of medicine. Brucellosis is one of the most common contagious zoonotic diseases with significant impacts on livestock health, reproduction, production, and public health worldwide. Therefore, our objective was to determine the seroprevalence and compare the logistic regression and Classification and Regression Tree (CART) data-mining analysis to assess risk factors associated with Brucella infection in the densest cattle populated Egyptian governorates. A cross-sectional study was conducted on 400 animals (383 cows, 17 bulls) distributed over four Governorates in Egypt's Nile Delta in 2019. The randomly selected animals from studied geographical areas were serologically tested for Brucella using iELISA, and the animals' information was obtained from the farm records or animal owners. Eight supposed risk factors (geographic location, gender, herd size, age, history of abortion, shared equipment, and disinfection post-calving) were evaluated using multiple stepwise logistic regression and CART machine-learning techniques. A total of 84 (21.0%; 95% CI 17.1–25.3) serum samples were serologically positive for Brucella. The highest seroprevalence of Brucella infection was reported among animals raised in herd size > 100 animals (65.5%), with no disinfection post-calving (61.7%), with a history of abortion (59.6%), and with shared equipment without thorough cleaning and disinfection (57.1%). The multiple stepwise logistic regression modeling identified herd size, history of abortion, and disinfection post-calving as important risk factors. However, CART modeling identified herd size, disinfection post-calving, history of abortion, and shared equipment as the most potential risk factors for Brucella infection. Comparing the two models, CART model showed a higher area under the receiver operating characteristic curve (AUROC = 0.98; 95% CI 0.95 – 1.00) than the binary logistic regression (AUROC = 0.89; 95% CI 0.73 – 0.92). Our findings strongly imply that Brucella infection is most likely to spread among animals raised in large herds (>100 animals) with a history of abortions and bad hygienic measures post-calving. The CART data-mining modeling provides an accurate technique to identify risk factors of Brucella infection in cattle. [ABSTRACT FROM AUTHOR]