Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks

• URL: http://arxiv.org/abs/2205.06834v1
• Date: Fri, 13 May 2022 18:02:38 GMT
• Title: Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks
• Authors: K.D. Olumoyin, A.Q.M. Khaliq, K.M. Furati
• Abstract summary: 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. A Deep neural network is utilized and a deep learning algorithm is developed to learn the time-varying heterogeneous transmission rates for each variant.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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