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

TL;DR

This paper introduces a multi-variant COVID-19 model that employs deep neural networks to learn and forecast heterogeneous transmission rates of different virus variants, aiding in understanding and predicting pandemic dynamics.

Contribution

It develops a Susceptible-Exposed-Infected-Recovered model incorporating deep learning to estimate variant-specific transmission rates and provides short-term case forecasts.

Findings

Deep neural network accurately estimates variant transmission rates.

Model successfully predicts short-term COVID-19 case trends.

Effective differentiation between variants in transmission dynamics.

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.