首页|Institut Agronomique et Veterinaire Hassan II Reports Findings in Artificial Int elligence (Contribution of artificial intelligence for understanding animal rabi es epidemiology in Morocco: What are the perspectives of an innovative and ...)
Institut Agronomique et Veterinaire Hassan II Reports Findings in Artificial Int elligence (Contribution of artificial intelligence for understanding animal rabi es epidemiology in Morocco: What are the perspectives of an innovative and ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Rabat, Morocco, by NewsRx editors, research stated, “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.” Our news journalists obtained a quote from the research from Institut Agronomiqu e et Veterinaire Hassan II, “The aim of this study was to design a predictive an alysis of rabies in Morocco. The expected outcome was the construction of probab ilistic diagrams that can guide actions for the integrated control of this disea se, involving all stakeholders, in the country. Such modeling is an essential st ep 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 intellige nce models (Machine Learning). In order to investigate the link between the risk of rabies within a territorial municipality (commune) and its socio-economic si tuation, the following data were analyzed: (1) health data: reported animal case s of rabies between 2004 and 2021 and data obtained through the ArcGIS kriging t ool (Geospatial data); (2) demographic and socio-economic data. We compared seve ral Machine Learning models. Of these, the ‘Imbalanced-Xgboost’ model associated with kriging yielded the best results. After optimizing this model, we mapped o ur results for better visualization. The obtained results complement and consoli date previous study in this field with greater accuracy, showing a strong correl ation between a commune’s socio-economic status, its geographical location and i ts risk level of rabies. From this, 399 out of the 1546 communes have been ident ified 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 w ell as canine population control, in a territorial commune according to its risk level. Such an approach allows obvious optimized distribution of financial reso urces and adaptation of the control actions to be taken. The study highlights al so the importance of using innovative technologies to refine epidemiological app roaches and fill gaps in field data.”
RabatMoroccoAfricaArtificial Intel ligenceCyborgsEmerging TechnologiesEpidemiologyHealth and MedicineMach ine LearningMononegavirales InfectionsRNA VirusesRabiesRhabdoviridae Inf ectionsRisk and Prevention
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