Spatiotemporal Analysis of COVID-19 Pandemic and Predictive Models based on Artificial Intelligence for different States of India

• Published in: Journal of the Institution of Engineers (India). Series B, Electrical Engineering, Electronics and telecommunication engineering, Computer engineering Vol. 102; no. 6; pp. 1265 - 1274
• Main Author: Guha, Paramita
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 2021; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Communications Engineering; Coronaviruses; COVID-19; Engineering; Networks; Original Contribution; Pandemics; Prediction models; Source code; Viruses; India; COVID-19; LSTM; RNN artificial intelligence; Spatiotemporal analysis; SIHRD; Predictive model
• ISSN: 2250-2106; 2250-2114
• DOI: 10.1007/s40031-021-00617-2

Geographical and spatial diversities play important roles in dynamics of spread of COVID-19 virus. These phenomena are not properly addressed in the literature yet. In this paper, COVID data of various states of India are collected. The data had been processed and analysed using an open-source software. A framework based on Susceptible, Infectious, Hospitalised, Recovered and Deaths model to determine the effects of geographical diversities of Indian states on COVID-19 pandemic has been developed. The confirmed, cured and death cases due to the virus have been analysed for different state. Reasons behind the differences in number of cases in different states are identified. An improved Long-Short-Term-Memory algorithm has been developed to forecast the virus spread and recovery of patients for the next one month. Numerical results along with discussions are given.