Open AccessA study of the COVID‐19 epidemic in India using the SEIRD model
Author(s) -
Banerjee Rudra,
Bhattacharjee Srijit,
Varadwaj Pritish Kumar
Publication year - 2021
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.15302/j-qb-021-0260
Subject(s) - tamil , covid-19 , uttar pradesh , pandemic , geography , population , demography , socioeconomics , medicine , sociology , philosophy , linguistics , disease , pathology , infectious disease (medical specialty)
Background The coronavirus pandemic (COVID‐19) is causing a havoc globally, exacerbated by the newly discovered SARS‐CoV‐2 virus. Due to its high population density, India is one of the most badly effected countries from the first wave of COVID‐19. Therefore, it is extremely necessary to accurately predict the state‐wise and overall dynamics of COVID‐19 to get the effective and efficient organization of resources across India. Methods In this study, the dynamics of COVID‐19 in India and several of its selected states with different demographic structures were analyzed using the SEIRD epidemiological model. The basic reproductive ratio R 0 was systemically estimated to predict the dynamics of the temporal progression of COVID‐19 in India and eight of its states, Andhra Pradesh, Chhattisgarh, Delhi, Gujarat, Madhya Pradesh, Maharashtra, Tamil Nadu, and Uttar Pradesh. Results For India, the SEIRD model calculations show that the peak of infection is expected to appear around the middle of October, 2020. Furthermore, we compared the model scenario to a Gaussian fit of the daily infected cases and obtained similar results. The early imposition of a nation‐wide lockdown has reduced the number of infected cases but delayed the appearance of the infection peak significantly. Conclusion After comparing our calculations using India’s data to the real life dynamics observed in Italy and Russia, we can conclude that the SEIRD model can predict the dynamics of COVID‐19 with sufficient accuracy.