Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Modelling the Unidentified Abortion Burden from Four Infectious Pathogenic Microorganisms in Ewes based on Artificial Neural Networks Approach: The Epidemiological Basis for a Control Policy

Gabriel Arteaga-Troncoso
ORCID logo ,
Miguel Luna-Alvarez
,
Laura Hernández-Andrade
,
Juan Manuel Jiménez-Estrada
,
Víctor Sánchez-Cordero
ORCID logo ,
Francisco Botello
ORCID logo ,
Roberto Montes de Oca-Jiménez
,
Marcela López-Hurtado
,
Fernando Martín Guerra-Infante
* ORCID logo
Version 1 : Received: 26 July 2023 / Approved: 26 July 2023 / Online: 27 July 2023 (02:29:59 CEST)

A peer-reviewed article of this Preprint also exists.

Arteaga-Troncoso, G.; Luna-Alvarez, M.; Hernández-Andrade, L.; Jiménez-Estrada, J.M.; Sánchez-Cordero, V.; Botello, F.; Montes de Oca-Jiménez, R.; López-Hurtado, M.; Guerra-Infante, F.M. Modelling the Unidentified Abortion Burden from Four Infectious Pathogenic Microorganisms (Leptospira interrogans, Brucella abortus, Brucella ovis, and Chlamydia abortus) in Ewes Based on Artificial Neural Networks Approach: The Epidemiological Basis for a Control Policy. Animals 2023, 13, 2955. Arteaga-Troncoso, G.; Luna-Alvarez, M.; Hernández-Andrade, L.; Jiménez-Estrada, J.M.; Sánchez-Cordero, V.; Botello, F.; Montes de Oca-Jiménez, R.; López-Hurtado, M.; Guerra-Infante, F.M. Modelling the Unidentified Abortion Burden from Four Infectious Pathogenic Microorganisms (Leptospira interrogans, Brucella abortus, Brucella ovis, and Chlamydia abortus) in Ewes Based on Artificial Neural Networks Approach: The Epidemiological Basis for a Control Policy. Animals 2023, 13, 2955.

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 artificial neural networks approach and 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: Overall seroprevalence of Brucella was 70.7%, while Leptospira spp. was 55.2%, C. abortus 21.9% and B. ovis 7.4%. Serological detection with the 4 abortion-causing microorganisms was determined only in 0.87% of sheep sampled. The best GLM is integrated by serological detection of serovar Hardjo and Brucella ovis in animals of the slopes with elevation between 2600 to 2800 meters above sea level from municipality of Xalatlaco, as well water supply, sheep pen built with materials of metal grids and untreated wood, with dirt and concrete floors, and bed of straw that remained independently associated with infectious abortion in ewes. 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 are worthwhile to further support the One Health initiative.

Keywords

Machine learning; Leptospira spp.; smooth Brucella spp.; Brucella ovis; Chlamydia abortus; Zoonoses

Subject

Public Health and Healthcare, Public Health and Health Services

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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