Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks. (arXiv:2205.06834v1 [q-bio.PE])

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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