ArticleHeliyon2024
A comprehensive analysis of the artificial neural networks model for predicting monkeypox outbreaks.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Monkeypox is a viral disease that causes outbreaks in various countries, significantly impacting public health and healthcare systems. Effective preparedness and response efforts require accurately predicting the severity of these outbreaks. Currently, there are no publicly released studies for nations like Chile and Mexico on monkeypox, leading to this study's creation. We use a neural network model with a time series dataset of monkeypox cases from multiple countries, including Argentina, Brazil, France, Germany, Chile, and Mexico. The Levenberg-Marquardt learning technique is employed to develop and validate single and two hidden layers artificial neural network models. We train various model architectures with different numbers of hidden layer neurons using the K-fold cross-validation early stopping method. Additionally, we use long short-term memory and gated recurrent unit models, commonly employed for time series data processing, to compare the performance of our artificial neural network model.
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