Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models | Zendy

B. Malavika | Zendy; S. Marimuthu | Zendy; Melvin Joy | Zendy; Ambily Nadaraj | Zendy; Edwin Sam Asirvatham | Zendy; Lakshmanan Jeyaseelan | Zendy

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Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models

Author(s) -

B. Malavika,

S. Marimuthu,

Melvin Joy,

Ambily Nadaraj,

Edwin Sam Asirvatham,

Lakshmanan Jeyaseelan

Publication year - 2020

Publication title -

clinical epidemiology and global health

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.361

H-Index - 13

eISSN - 2452-0918

pISSN - 2213-3984

DOI - 10.1016/j.cegh.2020.06.006

Subject(s) - logistic regression , incidence (geometry) , covid-19 , logistic function , demography , outbreak , statistics , geography , medicine , mathematics , disease , virology , infectious disease (medical specialty) , geometry , sociology

Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models.

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