1. Introduction
Decisions to initiate a public health program in the community are largely based on accurate estimates of the burden of disease. Abortion in sheep, including early foetal loss and stillbirth of lambs, is a major cause of economic loss for farm workers and farming communities. Naturally, abortion can be a common source of infection in humans and therefore of public health importance when caused by zoonotic microorganisms [
1]. While many infectious causes of abortions occur worldwide, etiologic data based on robust laboratory-confirmed diagnoses are subject to studies of farming systems in high- and middle-income countries with demographic characteristics not observed in low-income settings [
2]. Comparison of etiologies between countries and geographic areas is also not feasible for use in public health policy since epidemiological studies vary in methodologies and in the prevalence of the specific pathogens that are investigated. Therefore, it is necessary to implement methods that balance the economic costs allocated to the programs by increasing the accuracy and feasibility (efficiency) of the sampling [
3].
In Mexico, as in other developing countries, data on the prevalence of zoonotic infectious agents are generally obtained through cross-sectional studies. Due to the limited availability of sampling frames and the high costs of transport to the sampling site it is impossible and impractical to select a simple random sample (SRS) of the animals in the population Solution for most of these studies is to obtain a cluster sample by randomly selecting flocks from a checklist and then randomly reselecting a defined number of animals within each herd [
4]. In addition, new epidemiological surveillance tools, such as artificial intelligence and satellite geoprocessing, will greatly complement public health systems [
5,
6].
The relationship between infectious agents and host to human transmission is influenced by many environmental factors.
Leptospira,
Brucella and
Chlamydia are some of those endemic infectious agents in many countries without the availability of epidemiological surveillance systems or adequate diagnostic laboratories [
7,
8]. Rural areas tend to be a higher risk compared to urban areas due to a larger number of animal reservoirs in agricultural and forestall areas, as well as a higher level of transmission between domestic and wild animals [
9,
10]. On the contrary, urban leptospirosis, for example, is relatively easier to control through the implementation of anti-epizootic measures such as controlling the reproduction of rats by avoiding their availability of food and shelter [
11]. A dirty environment and rodents will always be associated with the transmission mechanisms of leptospirosis due to the possible presence of garbage, and contaminated water and soil [
12,
13,
14]. Many studies have reported that rural areas with limited access to clean drinking water and sanitation are more conducive to human infection [
15]. Furthermore, leptospirosis has been identified as an occupational disease where humans acquire the infection mainly through exposure to livestock, agricultural and military activities [
16].
Understanding the risk of infection in agropastoral settings where herds mix with each other and where different pathogens coexist with ease of transmission to humans living with sheep remains a major challenge. Field studies in this area have reported risk factors for within-flock transmission of
Leptospira in the valley region, as well as detection of
Chlamydia abortus by molecular testing in fetuses and abortive products in sheep [
17,
18]. The purpose of the study is to estimate the unidentified abortion burden from
Leptospira serovars, smooth
Brucella species (smooth
Brucella spp.),
Brucella ovis (
B. ovis) and
Chlamydia abortus (
C. abortus), as well as the identification of putative factors of abortion in sheep. This is intended to determine risk areas to identify possible new outbreaks towards the development of a regional zoonotic disease surveillance program.
4. Discussion
Among the etiologies of abortion in sheep there are the presence of infectious agents, including many zoonotic microorganisms, and non-infectious causes such as nutritional, genetic, hormonal, toxic and clinical (trauma, dystocia, prolapse) [
28]. The results of our study demonstrate the infectious etiology in 82.6% (285/345) of the ewes with a history of abortions in which the serological diagnoses were determined.
Seroprevalence of abortive microorganisms may vary by geographic region, it is widely accepted that zoonotic pathogens such as
Leptospira spp., smooth
Brucella spp.,
B. ovis, and
C. abortus are among the most frequent infectious causes of abortion in sheep [
29,
30]. Although many abortive pathogens of sheep are mandatory notification to the World Organization for Animal Health (OIE) due to restrictions on international trade, in Mexico, there is a lack of an effective epidemiological surveillance system that allows the development of strategies to prevent and control reproductive losses due to abortions and stillbirths of lambs, as well as to assess the prevalence of endemic diseases according to the diversity of ecosystems, with the subsequent reduction of risks to public health.
Data obtained in our study has allowed the successful implementation of an mANN to model mixed infection of four-abortive agents in sheep with a history of abortion as in other epidemiological studies [
31,
32]. Compared to stochastic models, the multilayer perceptron algorithm provided adequate prediction of abortion cases without the need for prior statistical correlations, or the assumptions required by common epidemiological models. Multilayer perceptron is a neural network that learns the relationship between linear and non-linear data and is considered an easy tool for the prediction of different diseases. The multilayer perceptron algorithm allowed to identify the statistical patterns among the infinite non-linear combinations related to abortion in sheep after training and validation. This is the first article in a series of possible ones that will use deep machine learning in the prognosis of diseases in animals. Deep learning has gained attention in recent decades for its innovative application in areas such as image classification using only pixels and labels as input layers, speech recognition, and automatically translate text from one language to another without human involvement.
The overall seroprevalence of microorganisms causing abortion in sheep appear to be very high, with 70.7% of animals testing positive for smooth
Brucella spp. (
B. melitensis,
B. abortus, and
B. suis) and 55.2% of animals positive for
Leptospira spp., followed by the seroprevalence of
C. abortus (21.9%) and
B. ovis (7.4%). These result, demonstrating previous exposure is surprising compared to previously reported seroprevalences of brucellosis in countries, such as Iran (29.1%), Egypt (16.3%), and less than 1% in the Arabian Gulf region, countries characterized by desert climates in summer and mild in winter [
33,
34,
35].
Leptospira serovar-specific antibodies have been detected in 24.7% of ewes with a history of abortions in Brazil, 8.5% in Iran, and 4.5% in Italy [
36,
37,
38].
Leptospirosis and brucellosis are the most widespread neglected diseases throughout the world, except Antarctica [
39]. Climate changes, changes in ecological niches, the appearance of new potential maintenance hosts could represent the most important factors involved in the epidemiology of abortifacient microorganisms. The environmental and geographical characteristics of Southern region of the State of Mexico can be considered as the optimal conditions for
Leptospira spp. and
Brucella spp. spreading among sheep and other animals, and eventually among humans.
The high seroprevalence of leptospirosis and brucellosis in ewes is not consistent with the small number of cases of human leptospirosis and brucellosis in State of Mexico, Mexico. Mexican population data from 2012-2022, showed 17 human confirmed cases with
Leptospira positive serological reaction and 655 confirmed cases with
Brucella were recorded by the Mexican Ministry of Health [
40]. Owing to the lack of diagnostic laboratories and a limited reporting system, leptospirosis and brucellosis are one of several neglected diseases in Mexico and this may be one of the reasons why few cases were identified over this period, despite the high carriage of multi microorganisms in animals. The occurrence of human leptospirosis cases is more common in the tropics, especially in South America and Asia [
41,
42], and in regions where brucellosis is endemic, deleterious effects are seen in both humans and domestic animals in the developing nations of Africa, South/Southeast Asia, and Latin America [
43]. The appearance of zoonotic disease in new localizations, as well as the sources of transmission between wild and domestic animals, is of great importance in terms of the epidemiological dimension. For many years, small ruminants had been considered as accidental hosts of leptospires, but several studies have shown that leptospiral infection in goats and sheep is common and these species can also act as only maintenance hosts for serovars and carriers of leptospires eliminating the microorganisms on the environment for long time periods [
44]. The maintenance hosts tend to be infected by serovars that colonize the kidneys and are shed in the urine. This hosts may act as chronic selective carriers of Leptospira serovars in a range of ecosystems and possibly transmit the pathogen to accidental hosts [
45]. Detection of serovar Canicola 20.3% of animals sampled and Portland-vere type strain in 3.5% observed in our study suggests the presence of a selective host such as dogs that can cause infection in sheep and possibly cause accidental infections in humans. It should be noted that the clinical differences of the disease in dogs are based on the signs associated with non-icterogenic Canicola serovar like that observed in humans as "Stuttgart disease" [
46,
47,
48]. Leptospirosis and brucellosis in sheep pose major threats to public health from direct contact with infected animals or their contaminated biological secretions (e.g., fetal, or vaginal fluids and aborted fetuses or placentae), as well as consumption of meat, unpasteurized milk and dairy products produced with consequent economic loss from restrictions on contaminated dairy products [
49].
Previous epidemiological investigations reported the circulation of
Leptospira serovars in this mountainous region with an overall seroprevalence of 54.5% and detection the most likely infecting serovars as Icterohaemorrhagiae (54.5%), Bratislava (40%), Canicola (19%), and Tarassovi (15.8%) [
17]. In this study, the overall seroprevalence of 55.2% is consistent with previously reported; but serological detection against the serovars as Pomona (5.2%), Grippotyphosa (3.8%), Pyrogenes (3.5%), and Portland-vere type strain (3.5%) suggest the possibility to investigating new serovars from wild reservoirs or sheep of other environmental settings. Wild rodents are the main reservoirs for pathogenic
Leptospira species as serovar Grippotyphosa, which cause leptospirosis in sheep [
50]. Transmission of
Leptospira serovars requires a continuous enzootic circulation of the organism between animals, although with the possibility of introduction of new serovars from animal reservoirs, both wildlife and domestic animals [
45]. According to Guedes et al. [
51], the microscopic agglutination test is a good serological technique for the detection of antibodies against
Leptospira serovars, but cross-reactions and paradoxical reactions are frequently observed with MAT. Serological paradoxical reactions and cross-reactions between serogroups were observed using MAT in our study, but the presence of high-titre
Leptospira seropositivity (>1:200) in 36.6% of Bratislava seropositive sheep, 31.3% of Icterohaemorrhagiae, 14.5% of Canicola, 4.9% of Tarassovi, 2.0% of Grippotyphosa, 1.4% of Pomona, and 1.3% of Pyrogenes suggests the possibility of infection with these serovars. Antibody titers >1:100 detected in these animal sera probably resulted in an overestimation of overall seroprevalence of leptospirosis.
Methodological strategy based on machine learning algorithms allowed the identification of the preprocessed variables associated with abortion in sheep. The percentage of predictive values in the training and test performance in the aborted sheep classification from the multilayer perceptron algorithm was 89.4% and 88.2%, respectively. The adequate performance of the algorithm was obtained by the ROC curve that demonstrated an area under the curve to correctly predict abortion in sheep of 86.2%. Based on the machine learning, the normalized importance values were obtained, which served to integrate the variables of GLM initial model. The final GLM appeared to fit the data well (overdispersion coefficient statistic = .83). If value of overdispersion coefficient is >1, this would show that the GLM is not appropriate goodness of fit. The area under the ROC curve (.89) was significantly different from 0.5, since the p value < .001, indicating that the GLM classified the group of aborted ewes significantly better than chance. The final GLM showed a high predictive capacity (89%), in the other words, 307 of the 345 sheep sampled were correctly classified. The result obtained by GLM allows us to know the exact extent of the abortion burden of zoonotic diseases in the region of the trans-Mexican neo-volcanic belt. The 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 remained independently associated with abortion in ewes.
The strength of our study includes complete information on the management of the herd and the individual animal based on the factors or characteristics that have been related to abortion in sheep, as well as the laboratory results of the serological samples obtained. Epidemiological indicators of seroprevalence of microorganisms that cause abortion in sheep to achieve its goal of providing factual, objective, reliable and comparable information with high precision based on clustered sampling. Also, the main factors and the less important factors in the prediction of abortion in sheep are reported by artificial intelligence learning algorithms using the multilayer perceptron model. The limitations of our study were those related to the detection of other foodborne zoonoses such as Salmonella abortusovis, Campylobacter spp., Toxoplasma gondii, Listeria spp. and Yersinia pseudotuberculosis which can be disseminated among animals causing abortion, as well as contaminate vegetables and fruits for human consumption. Other difficulties were related to the budget; however, the application of the cluster sampling design balanced feasibility of the abortion research project with precision of the epidemiological impact measures, complemented by the data analysis based on the machine learning algorithm.