Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks
This work addresses the need for accurate COVID-19 variant modeling for public health planning, but it is incremental as it applies existing deep learning methods to a new epidemiological context.
The authors tackled the problem of modeling COVID-19 variants with heterogeneous transmission rates by developing a SEIR model and using deep neural networks to learn these rates, achieving accuracy in data-driven simulations for US states like Florida and Alabama.
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.