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. 2023;27(1):569-577.
doi: 10.1007/s00500-022-06986-0. Epub 2022 Apr 2.

Empirical evidence of effects of stringency amid Covid-19 pandemic spread

Affiliations

Empirical evidence of effects of stringency amid Covid-19 pandemic spread

R I Minu et al. Soft comput. 2023.

Retraction in

Abstract

The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R-squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people's lives in different sectors were also discussed in this paper.

Keywords: Chennai; Coronavirus; Death rate; India; Linear regression; Lockdown; Recovery rate; Stringency index.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Covid-19 heat map in India a on 30 May, b on 07 July, c on 14 July (India Tamil Nadu COVID-19 density map.svg. 2020)
Fig. 2
Fig. 2
View of Population vs Total area vs active Covid-19 cases until 14 July, 2020
Fig. 3
Fig. 3
Comparison between Active, Death, and Recovery Covid-19 cases until 14 July, 2020
Fig. 4
Fig. 4
Comparison between Death rate and Covid-19 cases growth rate until 14 July, 2020
Fig. 5
Fig. 5
Covid-19 Mortality rate between Tested positive and Death (until 24 June, 2020)
Fig. 6
Fig. 6
a Cases on yearly June. b Cases on second week of July (Liu et al. 2020)
Fig. 7
Fig. 7
Growth of active cases in Chennai and other district in Tamil Nadu
Fig. 8
Fig. 8
The overall view of Chennai active cases
Fig. 9
Fig. 9
Regression line fit plot x = date, y = Covid-19 active cases in Chennai a Line fit for A1. b Line fit for A2. c Line fit for A3
Fig. 10
Fig. 10
Normal probability plot x = sample percentile, y = Covid-19 active cases in Chennai a Plot for A1. b Plot for A2. c Plot for A3

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