Prediction of COVID-19 Trend in India and Its Four Worst-Affected States Using Modified SEIRD and LSTM Models

Abstract

Since the beginning of COVID-19 (corona virus disease 2019), the Indian government implemented several policies and restrictions to curtail its spread. The timely decisions taken by the government helped in decelerating the spread of COVID-19 to a large extent. Despite these decisions, the pandemic continues to spread. Future predictions about the spread can be helpful for future policy-making, i.e., to plan and control the COVID-19 spread. Further, it is observed throughout the world that asymptomatic corona cases play a major role in the spread of the disease. This motivated us to include such cases for accurate trend prediction. India was chosen for the study as the population and population density is very high for India, resulting in the spread of the disease at high speed. In this paper, the modified SEIRD (susceptible–exposed–infected–recovered–deceased) model is proposed for predicting the trend and peak of COVID-19 in India and its four worst-affected states. The modified SEIRD model is based on the SEIRD model, which also uses an asymptomatic exposed population that is asymptomatic but infectious for the predictions. Further, a deep learning-based long short-term memory (LSTM) model is also used for trend prediction in this paper. Predictions of LSTM are compared with the predictions obtained from the proposed modified SEIRD model for the next 30 days. The epidemiological data up to 6th September 2020 have been used for carrying out predictions in this paper. Different lockdowns imposed by the Indian government have also been used in modeling and analyzing the proposed modified SEIRD model.

Temporal deep learning architecture for prediction of COVID-19 cases in India

“…Deep learning models such as LSTM and CNN are well suited for understanding and predicting the dynamical trend of COVID-19 spread and have recently been used in prediction by several researchers ( Bedi et al, 2021 , Dairi et al, 2021 , Devaraj et al, 2021 , Iqbal et al, 2021 , Nabi et al, 2021 , Shastri et al, 2020 , Wang et al, 2020 ). Chandra et al (2021) used the LSTM and its variants for ahead prediction of COVID-19 spread for India with split the training and testing data as static and dynamics.…”

GIS-based AHP analysis to recognize the COVID-19 concern zone in India

“…However, India also starts free of charge vaccination programme and total vaccination 663037334 as 02 September 2021. The various lockdowns imposed by the government of India have also been employed in modelling and analyzing the proposed different modified models (Bedi et al, 2021 ).all the data in this study were collected from Indian health ministry from 7th of February 2021 (Fig. 1 ).…”

Machine learning predictions of COVID-19 second wave end-times in Indian states

“…Indeed, such predictions have been attempted in a wide range of cases, such as financial time series, weather data, medical applications and many other physical systems [12][13][14][15]. There have been multiple earlier attempts in using machine learning approaches for predicting epidemic spreading in the context of COVID-19 (see e.g., [19,20]) as well. However, to have a proper estimate, a large set of training data is needed to be fed to the supervised learning algorithm.…”