Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model

Abstract

Abstract In the year 2019, during the month of December, the first case of SARS-CoV-2 was reported in China. As per reports, the virus started spreading from a wet market in the Wuhan City. The person infected with the virus is diagnosed with cough and fever, and in some rare occasions, the person suffers from breathing inabilities. The highly contagious nature of this corona virus disease (COVID-19) caused the rapid outbreak of the disease around the world. India contracted the disease from China and reported its first case on January 30, 2020, in Kerala. Despite several counter measures taken by Government, India like other countries could not restrict the outbreak of the epidemic. However, it is believed that the strict policies adopted by the Indian Government have slowed the rate of the epidemic to a certain extent. This article proposes an adaptive SEIR disease model and a sequence-to-sequence (Seq2Seq) learning model to predict the future trend of COVID-19 outbreak in India and analyze the performance of these models. Optimization of hyper parameters using RMSProp is done to obtain an efficient model with lower convergence time. This article focuses on evaluating the performance of deep learning networks and epidemiological models in predicting a pandemic outbreak.