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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2020 INDIACLEN. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Background: Since the onset of the COVID-19 in China, forecasting and projections of the epidemic based on epidemiological models have been in the centre stage. Researchers have used various models to predict the maximum extent of the number of cases and the time of peak. This yielded varying numbers. This paper aims to estimate the effective reproduction number (R) for COVID-19 over time using incident number of cases that are reported by the government. Methods: Exponential Growth method to estimate basic reproduction rate R0, and Time dependent method to calculate the effective reproduction number (dynamic) were used. “R0” package in R software was used to estimate these statistics. Results: The basic reproduction number (R0) for India was estimated at 1.379 (95% CI: 1.375, 1.384). This was 1.450 (1.441, 1.460) for Maharashtra, 1.444 (1.430, 1.460) for Gujarat, 1.297 (1.284, 1.310) for Delhi and 1.405 (1.389, 1.421) for Tamil Nadu. In India, the R at the first week from March 2–8, 2020 was 3.2. It remained around 2 units for three weeks, from March 9–29, 2020. After March 2020, it started declining and reached around 1.3 in the following week suggesting a stabilisation of the transmissibility rate. Conclusion: The study estimated a baseline R0 of 1.379 for India. It also showed that the R was getting stabilised from first week of April (with an average R of 1.29), despite the increase in March. This suggested that in due course there will be a reversal of epidemic. However, these analyses should be revised periodically.
Author(s): Marimuthu S, Joy M, Malavika B, Nadaraj A, Asirvatham ES, Jeyaseelan L
Publication type: Article
Publication status: Published
Journal: Clinical Epidemiology and Global Health
Year: 2021
Volume: 9
Pages: 57-61
Print publication date: 01/01/2021
Online publication date: 30/06/2020
Acceptance date: 26/06/2020
Date deposited: 28/07/2023
ISSN (print): 2452-0918
ISSN (electronic): 2213-3984
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.cegh.2020.06.012
DOI: 10.1016/j.cegh.2020.06.012
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