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Rabies is a major zoonotic disease legally notifiable in Morocco and elsewhere. Given the burden of rabies and its impact on public health, several national control programs have been implemented since 1986, without achieving their expected objectives. The aim of this study was to design a predictive analysis of rabies in Morocco. The expected outcome was the construction of probabilistic diagrams that can guide actions for the integrated control of this disease, involving all stakeholders, in the country. Such modeling is an essential step in operational epidemiology to optimize expenditure of public funds allocated to the integrated strategy for fighting this disease. The methodology employed combined the use of geospatial analysis tools (kriging) and artificial intelligence models (Machine Learning). In order to investigate the link between the risk of rabies within a territorial municipality (commune) and its socio-economic situation, the following data were analyzed: (1) health data: reported animal cases of rabies between 2004 and 2021 and data obtained through the ArcGIS kriging tool (Geospatial data); (2) demographic and socio-economic data. We compared several Machine Learning models. Of these, the "Imbalanced-Xgboost" model associated with kriging yielded the best results. After optimizing this model, we mapped our results for better visualization. The obtained results complement and consolidate previous study in this field with greater accuracy, showing a strong correlation between a commune's socio-economic status, its geographical location and its risk level of rabies. From this, 399 out of the 1546 communes have been identified as high-risk areas, accounting for 25.8% of the total number of communes. Under this risk-based approach, the results of these analyses make it practical to take targeted decisions for rabies prevention and control, as well as canine population control, in a territorial commune according to its risk level. Such an approach allows obvious optimized distribution of financial resources and adaptation of the control actions to be taken. The study highlights also the importance of using innovative technologies to refine epidemiological approaches and fill gaps in field data. Through this study, we hope to contribute to eradication of rabies in Morocco by providing reliable data and practical recommendations for control actions against rabies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Avian influenza (AI) is a zoonotic disease significant to both public and animal health, caused by influenza virus A, and affects domestic poultry, wild birds, and mammals including humans. Aquatic birds are considered the natural reservoir of this virus. In 2016, Morocco experienced the first occurrence of low pathogenic H9N2 avian influenza virus (AIV) in poultry; however, no cases were reported in wild birds. The present study aimed to monitor the presence of AIV in wild birds in Morocco in order to trace the possible sources of the viruses affecting poultry. Between 2016 and 2019, 967 samples obtained from 480 birds representing 56 different wild bird species, 20 families, and 8 orders, mostly from Charadriiformes, Anseriformes, Pelecaniformes, and Passeriformes, were collected from various wetlands and relevant ornithologic sites in Morocco. These field samples consisted of 374 cloacal swabs, 321 tracheal swabs, 54 fecal samples, and 218 organ pools including the trachea, lung, liver, spleen, heart, intestine, and brain. The samples were examined for the presence of AIV using TaqMan-based real-time reverse transcriptase-PCR (rRT-PCR) targeting the matrix gene, followed by further subtyping rRT-PCR tests targeting the H1-H16 genes. The AI matrix gene was detected in 18 out of 967 samples (1.86%); positive samples were detected in 17 birds belonging to 10 bird species: two redshanks ( Tringa totanus ), one little stint ( Calidris minuta ), one ruddy turnstone ( Arenaria interpres ), one common snipe ( Gallinago gallinago ), one common greenshank ( Tringa nebularia ), one black-winged stilt ( Himantopus himantopus ), two black-headed gulls ( Chroicocephalus ridibundus ), one slender-billed gull ( Chroicocephalus genei ), six cattle egrets ( Bubulcus ibis ), and one Eurasian coot ( Fulica atra ). AIV was detected in 2 wetlands and 1 ornithologic site (Sidi Moussa Oualidia Complex, Smir lagoon and El Jadida Coast) and the highest positivity was revealed in fresh fecal samples (11.1%), indicating the suitability of this matrix for wild bird surveillance. Our results highlight that waders, gulls, and cattle egrets are the most affected species and may represent a potential risk for AI introduction in the poultry sector in Morocco. Regular monitoring of wild birds in Morocco, focusing in particular in the areas and species identified in this study as a high risk of virus circulation, should be implemented to anticipate and prevent possible AIV spread.

This study aims to characterize the spatial distribution of animal rabies in Morocco in order to provide appropriate control approaches. Descriptive analyses of the epidemiological data show that the number of reported canine rabies cases greatly underestimates the true incidence of the disease. Underreporting subsequently affects the coherence of its spatial distribution. To perform accurate geographic distribution mapping of the disease based on interpolation methods, a data set was created using data between 2000 and 2018 to compare the derived disease cases with known true values in order to identify disease clusters. The subsequent interpolation was conducted using Ordinary Kriging regression methods and the semi variogram to focus on short distances and reduce uncertainty. The estimated clusters of rabies were evaluated using a cross validation step which revealed predicted cases close to the true values. To improve the precision of analysis, the authors displayed georeferenced dog and human rabies cases reported during the last three years, demonstrating reliable results that correspond to the estimated cluster areas similar to the true disease incidence on the field. This work highlights a strong correlation between infrastructure projects (i.e. railways, roads, facilities) and rabies epizootics for several specific locations. This study is the first attempt to use geostatistics to build upon the understanding of animal rabies in Morocco and shed light on the most appropriate strategies to sustainably reduce and mitigate the risk of rabies. There has been little literature on the use of kriging methods in animal health research. Thus, this study also aimed to explore a novel method in the veterinary sciences to establish kriging as a valid and coherent analysis tool to identify the extent to which the geostatistic area can objectively support understanding on animal rabies and saw it as being highly instrumental in coping with gaps in the data., Competing Interests: The authors declare no conflict of interest., (© 2021 Published by Elsevier Ltd.)