1 Introduction

Coronavirus (Covid-19) 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.

2 Covid-19 spread in India

The spread of coronavirus had become unpredictable in recent days in India, heat map of spread was shown in Fig. 1 (India Covid-19 July 2020). By 1 June, 2020 the larger states of India like Tamil Nadu, Karnataka, Andhra Pradesh, and Telengana there was a fall and increase in the growth. The average case growth was got reduce from 3.43% to 2.62% by 14 July till 30 June, 2020. By 1 June, there were 12 states/UTs without any Death report due to Coronavirus. The national wise average was 3.36% on 1 June was it got reduced to 2.62% by 14 July. The cases of Indian states can be compared with individual countries count for instate, the largest state Maharashtra can be compared with the Saudi Arabia case which is around 170,000. The other largest affected state like Tamil Nadu and Delhi can be compared with China. A detailed view is shown in Table 1 (National informatics July 2020, The Hindu, WHO). The table shows that most of the Indian states have as many as more confirmed cases now than countries that were once in the news for their outbreak. For Instant, the state like Maharashtra, Tamil Nadu, and Delhi had crossed the level with China 1 (National informatics July 2020, The Hindu, WHO).

Fig. 1
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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)

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Table 1 Indian state vs. Countries Covid-19 case comparison
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3 How India handling the death rate due to Covid-19

Most of all the countries are trying to avoid the death rate due to Covid-19. In general India’s death rate due to Covid-19 has fallen after rising at a very steep rate. India’s case fatality rate (CFR) has dipped from 3 to 2.9 recently. But still few states the number is quite high [The Hindu, The times of India]. The number of confirmed cases had increased tremendously in India in recent days, is because the number of tests done has increased a lot. Test per 1000 people is 7.1 in India [The Hindu, The times of India] whereas in the USA and Russia are 105.3 and 142.2, respectively. Even though the states in India tries to control the new confirmed cases in India through repeated lockdown and new rules there is a slight increase in death rate in a few state. In Maharashtra one of the second largest states in India and second largest populated state in India, by 1 June their death rate is around 42.4% but by July first week it was 45%. Even though the recovery rate of Maharashtra is 55%, out of 100 Covid-19 patients 4 are unfortunately passing away (National informatics July 2020, The Hindu, WHO). Whereas some smaller states in India such as Assam, Kerala, Chhattisgarh, Goa, Tripura, and Nagaland has a better recovery rate. But one of the smaller state Delhi (14833 Km2) with a moderate population of 19,814,000 was affected a lot both in death rate and active cases. To visualize some of the state’s strategies we had plotted the comparison view of population, area and active cases Covid-19 cases as of 14 June, 2020 in Fig. 2. 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.

Fig. 2
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View of Population vs Total area vs active Covid-19 cases until 14 July, 2020

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Fig. 3
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Comparison between Active, Death, and Recovery Covid-19 cases until 14 July, 2020

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Fig. 4
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Comparison between Death rate and Covid-19 cases growth rate until 14 July, 2020

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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).

Fig. 5
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Covid-19 Mortality rate between Tested positive and Death (until 24 June, 2020)

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5 Impact of lockdown in India

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.

Fig. 6
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a Cases on yearly June. b Cases on second week of July (Liu et al. 2020)

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Fig. 7
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Growth of active cases in Chennai and other district in Tamil Nadu

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Fig. 8
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The overall view of Chennai active cases

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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 × 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 R-squared 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.

Table 2 Statistical analysis of three test data
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Fig. 9
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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

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Fig. 10
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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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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.

7 Conclusion

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.