论文标题
使用深神经网络具有异质传播速率的多变量Covid-19模型
Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks
论文作者
论文摘要
自2021年以来，美国许多州的COVID-19变异变异已有报道。在与Covid-19的斗争中，必须研究在存在药物和非药物缓解措施的情况下，在存在药物和非药物的情况下，每个变体的时变传播速率的异质性。我们开发了一个易感性的感染的经过反射的数学模型，以突出显示B.1.617.2 Delta变体和原始SARS-COV-2的传播差异。讨论了该模型的理论结果。使用了深层神经网络，并开发了深度学习算法来学习每个变体的随时间变化的异质传播率。使用数据驱动的模拟在美国佛罗里达州，阿拉巴马州，田纳西州和密苏里州的COVID-19变体中，使用误差指标显示了该模型算法的准确性。使用长期的短期记忆神经网络和自适应神经模糊的推理系统，证明了每日病例的短期预测。
Mutating variants of COVID-19 have been reported across many US states since 2021. In the fight against COVID-19, it has become imperative to study the heterogeneity in the time-varying transmission rates for each variant in the presence of pharmaceutical and non-pharmaceutical mitigation measures. We develop a Susceptible-Exposed-Infected-Recovered mathematical model to highlight the differences in the transmission of the B.1.617.2 delta variant and the original SARS-CoV-2. Theoretical results for the well-posedness of the model are discussed. A Deep neural network is utilized and a deep learning algorithm is developed to learn the time-varying heterogeneous transmission rates for each variant. The accuracy of the algorithm for the model is shown using error metrics in the data-driven simulation for COVID-19 variants in the US states of Florida, Alabama, Tennessee, and Missouri. Short-term forecasting of daily cases is demonstrated using long short term memory neural network and an adaptive neuro-fuzzy inference system.
