3066-280XISSN (Online)
Volume 1 , Issue 2 , PP: 48-58, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed El-Sayed Saqr 1 * , Ahmed M. Elshewey 2 *
Doi: https://doi.org/10.54216/MOR.010205
The present research investigates the role of machine learning models in forecasting the course of Lyme disease and improving diagnostics by looking for environmental, host and anthropogenic factors contributing to the rise and fall of the tick population and disease outbreaks. With the popularization of ecological models and artificial intelligence-based techniques such as neural networks and random forests, it has become possible to efficiently and accurately over various risk maps that relate to ticks' location and distribution, which is an essential aspect of improving public health management issues. These models integrate climate and demographic data as well as host-pathogen interaction data and help understand the distribution of high-risk areas and the dynamics of the diseases, thus facilitating the management of tick-borne illness. This approach also illustrates the significance of predictive diagnostics for early disease detection, allowing for interventions and preventive measures only on relevant population sub-groups. Ultimately, this study considers the possibilities machine learning offers in managing Lyme disease, articulating the implications of these conclusions for the preparedness for health emergencies on a more global scale.
Automated learning , Lyme borreliosis , Forecasting actual behavior , Health concerns transmitted by insects , Tests and procedures , Community health
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