Alnaji Lulah
Department of Mathematics, College of Science, University of Hafr Al Batin, Hafr Al Batin, 39524, Saudi Arabia.
Heliyon. 2024 Sep 3;10(17):e37274. doi: 10.1016/j.heliyon.2024.e37274. eCollection 2024 Sep 15.
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.
猴痘是一种病毒性疾病，在多个国家引发疫情，对公共卫生和医疗系统造成重大影响。有效的防范和应对措施需要准确预测这些疫情的严重程度。目前，智利和墨西哥等国家尚未公开有关猴痘的研究，因此开展了本项研究。我们使用了一个神经网络模型，其数据集为来自多个国家（包括阿根廷、巴西、法国、德国、智利和墨西哥）的猴痘病例时间序列。采用Levenberg-Marquardt学习技术来开发和验证单隐藏层和双隐藏层人工神经网络模型。我们使用K折交叉验证早期停止方法，训练具有不同数量隐藏层神经元的各种模型架构。此外，我们还使用常用于时间序列数据处理的长短期记忆模型和门控循环单元模型，来比较我们的人工神经网络模型的性能。
