key: cord-0801267-pq49vwtv authors: Minu, R. I.; Nagarajan, G.; Mary, A. Viji Amutha; Selvan, Mercy Paul; Saravanan, T. R. title: Empirical evidence of effects of stringency amid Covid-19 pandemic spread date: 2022-04-02 journal: Soft comput DOI: 10.1007/s00500-022-06986-0 sha: 13a042adfc8a58f553a7adb8963c7da42f3b2d8b doc_id: 801267 cord_uid: pq49vwtv 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. Coronavirus infection is an infectious disease, this disease was declared a pandemic, its outbreak might happen in late 2019. By 17 July, 2020, there are about 13,575,158 confirmed cases and 584,940 people died due to this infection globally. The prediction of this virus growth can be mathematically model using R-value (Liu et al. 2020) , only if it reaches the peak value.As the value are not uniformly distributed developing a model will not be effective as it will affected by many evidence value. To control the spread of this pandemic diseases each government have imposed different restriction, the commonly used one is stringency, lockdown and quarantines. After the Covid-19 outbreak, the world requires a major revolution in technology development to prevent these kinds of infectious pandemic situations in the Future (Anser et al. 2020) . The WHO had already declared that this virus will not be completely eradicated from the world for more than a few years. So, the Government of all Country need to educated there people to learn how to leave with this situation, a complete lockdown will not be a solution (Harmooshi et al. 2020) . So, one of the mere solutions to handle this situation is to bring changes in all sectors with the help of advanced technologies. This paper analyses the strategies which was prevailing and adopted in India. The objective of this paper is to study (i) The Covid-19 spread in India before and after lockdown (ii) Analysing the case growth, active and death rate in different states of India (iii) Age-wise case analysis (iv) Impact of lockdown to control Covid-19 spread (v) Economic impact due to lockdown in India. The spread of coronavirus had become unpredictable in recent days in India, heat map of spread was shown in Fig. 1 Figure 3 shows the comparison of active cases, death rate, and recovery rate of some of the large state, small state, and heavily populated states. Figure 4 shown the Death and growth rate of Covi-19. By visualizing all this data, it was clear that it would be difficult to derive a prediction model to fit all this data as the distribution of the data was not uniform also the correlation of this data with respect to the depended variable such as population or Area is inevitable to derive due to the Cases like small state more cases like Delhi. Highly populated less comparative cases like Uttar Pradesh. 4 Age-wise factor in Covid-19 People in the age group of 45 and above were considered as more risk from Coronavirus in terms of fatalities. But in India, 43% of deaths have taken place in the relatively younger age bands of 30-44 and 45-59 (National informatics July 2020, The Hindu, WHO). AS per the Indian statistics, the Indian population comprising 25% of 45 year age people. As per there calculation, 85% of them may be counting for infection. But the report says most of 71% of death reported is around 45-59 year age, which comprising of 37% of Indian Population. So, in India, 43% of deaths due to coronavirus are coming under the age band of 30-59 years. Figure 5 shown the age-wise affected and death rate. It can be seen that the rate was gradually increasing from the age of 30 itself rather than 45 Bonardi et al. (2020) . India is one of the first countries which had reported to hit the upper stringency index (Max et al. 2020 ) limit of 100 more than a single day. Most of the country tried to impose such a strictest lockdown Inoue and Todo (2020) only after more death cases but in India when it imposes its first 21 days strict lockdown the death was only 4. Still, India is in its second phase of lockdown, depending upon the death and active rate the state had imposed a different set of lockdown amid to national level lockdown to prevent the spread. This intensifying lockdown will eventually affect the epidemiological and economic growth cited by Mahato et al. (2020) . So, the Government has created so many scaling metric factors to unlock the state boundaries but yet the spread continues. The main objective which has to be inferred from this point is whether the lockdown helps. In Chennai, the capital city of Tamil Nadu was affected a lot due to Covid-19 crises, in the first week of June 91% of the state count of Tamil Nadu is from Chennai. So, by the end of June, they imposed a complete lockdown from 20 June till 1 July. This lockdown was only implemented in those district which had more number of active case growth. Due to this in the second week of July, it contributes 69% of total Covid-19 active cases. Whereas the other city which had less count had started contributing more. The City Madurai, in the month of June it has less than 0.5% case, wherein July it has more than 5% cases. So, to control the count again they impose another set of lockdown in that city. The test positivity rate number had dropped from 35 to 16.52% (Covid19India July 2020; Health & Family July 2020) due to this second intensive lockdown (As per city corporation record). The prediction of cases through statistic is not possible at this stage as per mathematician the prediction fails in each month, due to many factors. Yet the intensive lockdown has reduced the doubling period rate. The doubling period had increased from 10 to 15 days. Which means the infection rate is reduced in the lockdown cities, but the count in the state increases as the infection rate in other state got increased due to relaxation in lockdown which can be seen in Fig. 6 (Max et al. 2020) . By 12 June, 2020, the doubling time of Chennai had gone up to 18 days. The dip in the growth of active cases in Chennai (where the lockdown was imposed) can be seen in the graph shown in Figs. 7 and 8. So for this lockdown, 88% of people were in favour of it as Chennai residents need to have a Covid-19 free environment. But this lockdown imposes many problems for entrepreneurs and job-seekers as specified by Atalan (2020) . To empirically evaluate the stringency effect on Chennai, three different kinds of analysis is done. The data are gathered from (Vega 2020; Covid19India July 2020; Health & Family July 2020). In analysis one (A1) the entire active cases in Chennai from 29 March, 2020 till 16 July, 2020 were taken. In analysis two (A2) the active cases in Chennai from 1 July till 16 July, 2020 were taken. In analysis three (A3) the active cases from 19 June till 16 July, 2020 were taken. The test A1 given the statistical evaluation for the entire period from the initial outbreak till now. To understand the effect of stringency the A3 data was taken (After the severe lockdown). The linear regression was apply were, variable 9 represents-Date and variable y represent-Covid-19 case count. The results were shown in Table 2 . The effect of stringency had given a moderate R squared and p value. As the strict lockdown was imposed in the month of June, the drop in cases can be justified linearly by this regression prediction as the Rsquared value had been dropped the same as the p value. The regression line fit curve for three cases is shown in Fig. 9 . From the graph, it can be noted that A2 (July month cases) has some prediction fit. Still, to determine the distribution of the data, the normal probability plot for all three cases was determined as shown in Fig. 10 . There is no s-curved plot, which shows the value is not normally distributed. But the plot in Fig. 10a (A1 analysis) shows a right-skewed distribution. The bell-shaped distribution will arise only if we have a peak value. As A1 analysis includes most of the data from the month of March 2020 to till-date, may be Chennai city gets its bell curve distribution if the value consistency maintained amid all the factors. Empirical evidence of effects of stringency amid Covid-19 pandemic spread 6 Impact of lockdown on economy Due to subsequent lockdown, the Micro small medium enterprises (MSME) had a very bad impact. They suffered from REVENUE loss, delayed payment, cancellation of the order, and so on. Nearly 78% of MSMEs were shut down due to this lockdown. Among them, only 27% of enterprises were able to manage the situation for about three months. The core problem faced by that 50% of industries is that cancellation of orders and the demand reduces. Even with the finished products, 38% cited the inability to deliver the order due to a lack of finance and transportation. The private bank sector in India stated that due to lockdown there was an increase of 21% loan [The Hindu, The time of India]. Also, there was a high increase in deposit due to the flight of safety. The loan growth has been almost twice as fast for the bank. After the unlock the economic activity in terms of food arrivals at wholesales, market status, labour force participation and so had started increased in some of the states in India. The mobility has decreased at least 10% in most affected states like Maharashtra, Delhi, Tamil Nadu, Gujarat, and Uttar Pradesh. The debit card transactions are still just 7% of the pre-lockdown level. In the Agriculture sector, this lockdown was not pushed to the Darken spot. The most affected sectors are Fishing and Horticulture. The food grains and livestock output were not affected as crops can be broken down into food grains and horticulture. The food grain is non-perishable and can be stored. Where this is not the case with horticulture product like fruits, flowers and vegetable. The livestock consumption and processing were also not that much affected, as the demand declined those products can be converted to other stored product. Thus the agriculture sector can be protected by its diverse output. In India, nearly 1 million people are affected by this infectious Coronavirus disease and nearly 25,000 people die due to this disease. But, still, the Government had taken the utmost care to sustain the infection and to drop the curve. With repeated lockdown and stringency, the R-value of Chennai had dropped below 0.78 (below 0.5 is required) but still this can be good news with consistent in a low number of cases. With cautions and at most care decisions of Government this pandemic situation can be survived with responsible citizens. Social and administrative issues related to the COVID-19 pandemic in Pakistan: better late than never Is the lockdown important to prevent the COVID-9 pandemic? Effects on psychology, environment and economyperspective Fast and local: how did lockdown policies affect the spread and severity of the covid-19 Environmental concern regarding the effect of humidity and temperature on 2019-nCoV survival: fact or fiction Wikimedia Commons, the free media repository Wikimedia Commons, the free media repository The propagation of economic impacts through supply chains: the case of a mega-city lockdown to prevent the spread of COVID-19 The reproductive number of COVID19 is higher compared to SARS coronavirus Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi Published online at OurWorldInData The Times of India, Chennai WHO Health Emergency Dashboard WHO (Covid-19) htps:// Covid19.who.int/region/searo/country Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Empirical evidence of effects of stringency amid Covid-19 pandemic spread Acknowledgements We acknowledge the Indian Government in providing us the required in their websites.Author contributions Minu and Nagarajan had designed the model and the computational framework and wrote the paper. Viji Amutha Mary, Mercy Paul and Saravanan had collected all the required data for this paper.Funding The authors declare that they have no funding. Not applicable.Availability of data and materials The data used in this paper was extracted from following website National informatics Centre, MyGov cell, Government of India https://www.mygov.in/Covid-19 (accessed 10-16 July, 2020). WHO Health Emergency Dashboard WHO (Covid-19) htps://Covid19.who.int/region/searo/country/in (accessed 10-16 July, 2020). Conflict of interest The authors declare that they have no competing interests.Ethics approval and consent to participate This paper does not contain any human or animal study content. 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