Modelling the Unidentified Abortion Burden from Four Infectious Pathogenic Microorganisms (<i>Leptospira interrogans</i>, <i>Brucella abortus</i>, <i>Brucella ovis</i>, and <i>Chlamydia abortus</i>) in Ewes Based on Artificial Neural Networks Approach: The Epidemiological Basis for a Control Policy

Abstract

Unidentified abortion, of which leptospirosis, brucellosis, and ovine enzootic abortion are important factors, is the main cause of disease spread between animals and humans in all agricultural systems in most developing countries. Although there are well-defined risk factors for these diseases, these characteristics do not represent the prevalence of the disease in different regions. This study predicts the unidentified abortion burden from multi-microorganisms in ewes based on an artificial neural networks approach and the GLM. Methods: A two-stage cluster survey design was conducted to estimate the seroprevalence of abortifacient microorganisms and to identify putative factors of infectious abortion. Results: The overall seroprevalence of Brucella was 70.7%, while Leptospira spp. was 55.2%, C. abortus was 21.9%, and B. ovis was 7.4%. Serological detection with four abortion-causing microorganisms was determined only in 0.87% of sheep sampled. The best GLM is integrated via serological detection of serovar Hardjo and Brucella ovis in animals of the slopes with elevation between 2600 and 2800 meters above sea level from the municipality of Xalatlaco. Other covariates included in the GLM, such as the sheep pen built with materials of metal grids and untreated wood, dirt and concrete floors, bed of straw, and the well water supply were also remained independently associated with infectious abortion. Approximately 80% of those respondents did not wear gloves or masks to prevent the transmission of the abortifacient zoonotic microorganisms. Conclusions: Sensitizing stakeholders on good agricultural practices could improve public health surveillance. Further studies on the effect of animal–human transmission in such a setting is worthwhile to further support the One Health initiative.

Keywords

• machine learning
• <i>Leptospira</i> spp.
• smooth <i>Brucella</i> spp.
• <i>Brucella ovis</i>
• <i>Chlamydia abortus</i>
• zoonoses