Abstract
The use of neural networks in predicting and preventing the spread of infectious diseases has gained significant attention in recent years. This article explores the application of neural network-based models in epidemiology, focusing on their ability to analyze complex datasets and predict the spread of diseases across populations. We discuss the various types of neural networks used in disease spread prediction, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and deep learning models. Additionally, the article examines the challenges and ethical considerations associated with using AI models in public health, such as data privacy, model interpretability, and bias.
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