The world has turned upside down after the spread of corona virus. We all are fighting with the virus in our own ways. To practice social distancing, the world had gone to a lockdown.
It has been months. No school, no outings, no vacations, no playing in parks; we are confined to our houses. The world could not be locked down for months, so with all safety measures it is trying to get back to its workings.
With every passing day, there is so much deaths and active corona cases. It is really disheartening. Though there is no more lockdown, we prefer staying at home. We do not go out until any need.
With everything getting back to normal, a news of reopening schools was surfacing for quite some time for which parents' consent was required.
The decision of going to school required a lot of research because that could not only cost me and my friends and family getting infected but also could cost our lives.
I went on to research more about the spread of the virus. I read many articles, surfed the internet and got to know many factors, precautions and many more. I could not get the answer I was looking for. However, reading several newspapers and statistical data that has been reported collecting data from a number of companies in the cities has helped to get a clear idea about the number of individuals who were bound to go out of their houses to their work places after remission of lockdown period.
I wanted to know to what extent there is a relation between the number of individuals going out to their workplaces after lockdown and the number of individuals getting infected by COVID – 19.
This IA is about the same. This research would allow my family to decide if I should attend school or should I continue with my online classes.
The main motive of this IA is to show a correlation between the number of employees and working professionals those who are getting infected by COVID-19 with respect to the number of working professionals going out right after calling off the Lockdown. The study will show the effect of Lockdown during the COVID-19 pandemic on the rate of spreading after remission of it. It will also redirect to the fact how much it is safe for individuals to go out for work after Lockdown.
What is the relationship between the number of individuals who are going out to their workplaces after Lockdown and the number of individuals who are getting infected by COVID-19?
COVID – 19 was one of the most infectious diseases the world has ever seen. It has spread throughout the globe within a span of 3 to 5 months. Originating from the city of Yuhan in China, it has spread to different countries including the far west – The USA. One of the most affect countries is India. From the month of March to July, complete Lockdown was called by the government to cope up with the pandemic and restrict the spread. However, lockdown was called off by the government since August and emergency services, such as, banking sector, media, etc. were allowed to start their usual operation. In this process, the infection of COVID – 19 is again taking its pace in an increasing order.
In this IA, data of the number of attendees of 20 companies situated in Mumbai, Delhi, and Kolkata, the three most affected cities of India, has been collected and the number of employees who got infected were noted. This collection is done for three different age groups from 25 years old to 55 years old with an interval of 10 years. This will help to analyze the spread of infection among individuals of different age groups after lockdown.
The data collection for this correlative analysis has been done from a number of internationalized media houses and newspaper which are considered as authentic sources of information.
Sl. No. | Total number of individuals | Number of individuals infected |
---|---|---|
1 | 150 | 21 |
2 | 160 | 18 |
3 | 150 | 30 |
4 | 120 | 12 |
5 | 200 | 21 |
6 | 140 | 18 |
7 | 160 | 20 |
8 | 150 | 40 |
9 | 170 | 15 |
10 | 150 | 21 |
11 | 120 | 6 |
12 | 160 | 8 |
13 | 150 | 30 |
14 | 160 | 31 |
15 | 120 | 13 |
16 | 150 | 10 |
17 | 140 | 7 |
18 | 180 | 17 |
19 | 160 | 6 |
20 | 120 | 20 |
Sl. No. | Total number of individuals | Number of individuals infected |
---|---|---|
1 | 150 | 4 |
2 | 160 | 6 |
3 | 150 | 9 |
4 | 120 | 3 |
5 | 200 | 8 |
6 | 140 | 10 |
7 | 160 | 0 |
8 | 150 | 0 |
9 | 170 | 12 |
10 | 150 | 3 |
11 | 120 | 30 |
12 | 160 | 1 |
13 | 150 | 5 |
14 | 160 | 3 |
15 | 120 | 1 |
16 | 150 | 9 |
17 | 140 | 4 |
18 | 180 | 7 |
19 | 160 | 6 |
20 | 120 | 3 |
Sl. No. | Total number of individuals | Number of individuals infected |
---|---|---|
1 | 150 | 34 |
2 | 160 | 36 |
3 | 150 | 32 |
4 | 120 | 24 |
5 | 200 | 21 |
6 | 140 | 3 |
7 | 160 | 25 |
8 | 150 | 28 |
9 | 170 | 32 |
10 | 150 | 34 |
11 | 120 | 21 |
12 | 160 | 14 |
13 | 150 | 25 |
14 | 160 | 23 |
15 | 120 | 20 |
16 | 150 | 21 |
17 | 140 | 22 |
18 | 180 | 27 |
19 | 160 | 34 |
20 | 120 | 21 |
Sl. No. | Cumulative total number of individuals | Cumulative number of infected individuals |
---|---|---|
1 | 150 | 21 |
2 | 310 | 39 |
3 | 460 | 69 |
4 | 580 | 81 |
5 | 780 | 102 |
6 | 920 | 120 |
7 | 1080 | 140 |
8 | 1230 | 180 |
9 | 1400 | 195 |
10 | 1550 | 216 |
11 | 1670 | 222 |
12 | 1830 | 230 |
13 | 1980 | 260 |
14 | 2140 | 291 |
15 | 2260 | 304 |
16 | 2410 | 314 |
17 | 2550 | 321 |
18 | 2730 | 338 |
19 | 2890 | 344 |
20 | 3010 | 364 |
Sl. No. | Cumulative total number of individuals | Cumulative number of infected individuals |
---|---|---|
1 | 150 | 4 |
2 | 310 | 10 |
3 | 460 | 19 |
4 | 580 | 22 |
5 | 780 | 30 |
6 | 920 | 40 |
7 | 1080 | 40 |
8 | 1230 | 40 |
9 | 1400 | 52 |
10 | 1550 | 55 |
11 | 1670 | 85 |
12 | 1830 | 86 |
13 | 1980 | 91 |
14 | 2140 | 94 |
15 | 2260 | 95 |
16 | 2410 | 104 |
17 | 2550 | 108 |
18 | 2730 | 115 |
19 | 2890 | 121 |
20 | 3010 | 124 |
Sl. No. | Cumulative total number of individuals | Cumulative number of infected individuals |
---|---|---|
1 | 150 | 34 |
2 | 310 | 70 |
3 | 460 | 102 |
4 | 580 | 126 |
5 | 780 | 147 |
6 | 920 | 150 |
7 | 1080 | 175 |
8 | 1230 | 203 |
9 | 1400 | 235 |
10 | 1550 | 269 |
11 | 1670 | 290 |
12 | 1830 | 304 |
13 | 1980 | 329 |
14 | 2140 | 352 |
15 | 2260 | 372 |
16 | 2410 | 393 |
17 | 2550 | 415 |
18 | 2730 | 442 |
19 | 2890 | 476 |
20 | 3010 | 497 |
Calculation of R^{2}
x | y | x^{2} | y^{2} | xy |
---|---|---|---|---|
150 | 21 | 22500 | 441 | 3150 |
310 | 39 | 96100 | 1521 | 12090 |
460 | 69 | 211600 | 4761 | 31740 |
580 | 81 | 336400 | 6561 | 46980 |
780 | 102 | 608400 | 10404 | 79560 |
920 | 120 | 846400 | 14400 | 110400 |
1080 | 140 | 1166400 | 19600 | 151200 |
1230 | 180 | 1512900 | 32400 | 221400 |
1400 | 195 | 1960000 | 38025 | 273000 |
1550 | 216 | 2402500 | 46656 | 334800 |
1670 | 222 | 2788900 | 49284 | 370740 |
1830 | 230 | 3348900 | 52900 | 420900 |
1980 | 260 | 3920400 | 67600 | 514800 |
2140 | 291 | 4579600 | 84681 | 622740 |
2260 | 304 | 5107600 | 92416 | 687040 |
2410 | 314 | 5808100 | 98596 | 756740 |
2550 | 321 | 6502500 | 103041 | 818550 |
2730 | 338 | 7452900 | 114244 | 922740 |
2890 | 344 | 8352100 | 118336 | 994160 |
3010 | 364 | 9060100 | 132496 | 1095640 |
∑x = 31930 | ∑y = 4151 | ∑x^{2} = 66084300 | ∑y^{2} = 1088363 | ∑xy = 8468370 |
Figure 13 - Table On Processed Data For Calculation Of Correlation Coefficient R^{2} For Group 1 (25 Years To 35 Years)
\(r = \frac{n\bigg(∑xy\bigg)-(∑x)(∑y)}{\sqrt{[n∑x^2-\bigg(∑x\bigg)^2][n∑y^2-\bigg(∑y\bigg)^2]}}\)
\(r = \frac{20×8468370-(31930)(4151)}{[20×66084300-(31930)^2][20×1088363-(4151)^2]}\)
=> r^{2} = 0.9894
x | y | x^{2} | y^{2} | xy |
---|---|---|---|---|
150 | 4 | 22500 | 16 | 600 |
310 | 10 | 96100 | 100 | 3100 |
460 | 19 | 211600 | 361 | 8740 |
580 | 22 | 336400 | 484 | 12760 |
780 | 30 | 608400 | 900 | 23400 |
920 | 40 | 846400 | 1600 | 36800 |
1080 | 40 | 1166400 | 1600 | 43200 |
1230 | 40 | 1512900 | 1600 | 49200 |
1400 | 52 | 1960000 | 2704 | 72800 |
1550 | 55 | 2402500 | 3025 | 85250 |
1670 | 85 | 2788900 | 7225 | 141950 |
1830 | 86 | 3348900 | 7396 | 157380 |
1980 | 91 | 3920400 | 8281 | 180180 |
2140 | 94 | 4579600 | 8836 | 201160 |
2260 | 95 | 5107600 | 9025 | 214700 |
2410 | 104 | 5808100 | 10816 | 250640 |
2550 | 108 | 6502500 | 11664 | 275400 |
2730 | 115 | 7452900 | 13225 | 313950 |
2890 | 121 | 8352100 | 14641 | 349690 |
3010 | 124 | 9060100 | 15376 | 373240 |
∑x = 31930 | ∑y = 1335 | ∑x^{2 }= 66084300 | ∑y^{2} = 118875 | ∑xy = 2794140 |
Figure 14 - Table On Processed Data For Calculation Of Correlation Coefficient R^{2} For Group 2 (35 Years To 45 Years)
\(r = \frac{n\bigg(∑xy\bigg)-(∑x)(∑y)}{\sqrt{[n∑x^2-\bigg(∑x\bigg)^2][n∑y^2-\bigg(∑y\bigg)^2]}}\)
\(=> r = \frac{20×2794140-(31930)(1335)}{\sqrt{[20×66084300-(31930)^2][20×118875-(1335)^2]}}\)
=> r^{2} = 0.977
x | y | x^{2} | y^{2} | xy |
---|---|---|---|---|
150 | 34 | 22500 | 1156 | 5100 |
310 | 70 | 96100 | 4900 | 21700 |
460 | 102 | 211600 | 10404 | 46920 |
580 | 126 | 336400 | 15876 | 73080 |
780 | 147 | 608400 | 21609 | 114660 |
920 | 150 | 846400 | 22500 | 138000 |
1080 | 175 | 1166400 | 30625 | 189000 |
1230 | 203 | 1512900 | 41209 | 249690 |
1400 | 235 | 1960000 | 55225 | 329000 |
1550 | 269 | 2402500 | 72361 | 416950 |
1670 | 290 | 2788900 | 84100 | 484300 |
1830 | 304 | 3348900 | 92416 | 556320 |
1980 | 329 | 3920400 | 108241 | 651420 |
2140 | 352 | 4579600 | 123904 | 753280 |
2260 | 372 | 5107600 | 138384 | 840720 |
2410 | 393 | 5808100 | 154449 | 947130 |
2550 | 415 | 6502500 | 172225 | 1058250 |
2730 | 442 | 7452900 | 195364 | 1206660 |
2890 | 476 | 8352100 | 226576 | 1375640 |
3010 | 497 | 9060100 | 247009 | 1495970 |
∑x = 31930 | ∑y = 5381 | ∑x^{2 }= 66084300 | ∑y^{2} = 1818533 | ∑xy = 10953790 |
Figure 15 - Table On Processed Data For Calculation Of Correlation Coefficient R^{2} For Group 2 (45 Years To 55 Years)
\(r = \frac{n\bigg(∑xy\bigg)-(∑x)(∑y)}{\sqrt{[n∑x^2-\bigg(∑x\bigg)^2][n∑y^2-\bigg(∑y\bigg)^2]}}\)
\(=> r = \frac{20×10953790-(31930)(5381)}{\sqrt{[20×66084300-(31930)^2][20×1818533-(5381)^2]}}\)
=> r^{2} = 0.9968
x | y | \( x-\bar x\) | \(y-\bar y\) | \((x-\bar x)(y-\bar y)\) | \((x-\bar x)^2\) | \((y-\bar y)^2\) |
---|---|---|---|---|---|---|
150 | 21 | 1596.5 | 207.55 | -1446.5 | -186.55 | 269844.575 |
310 | 39 | 1596.5 | 207.55 | -1286.5 | -168.55 | 216839.575 |
460 | 69 | 1596.5 | 207.55 | -1136.5 | -138.55 | 157462.075 |
580 | 81 | 1596.5 | 207.55 | -1016.5 | -126.55 | 128638.075 |
780 | 102 | 1596.5 | 207.55 | -816.5 | -105.55 | 86181.575 |
920 | 120 | 1596.5 | 207.55 | -676.5 | -87.55 | 59227.575 |
1080 | 140 | 1596.5 | 207.55 | -516.5 | -67.55 | 34889.575 |
1230 | 180 | 1596.5 | 207.55 | -366.5 | -27.55 | 10097.075 |
1400 | 195 | 1596.5 | 207.55 | -196.5 | -12.55 | 2466.075 |
1550 | 216 | 1596.5 | 207.55 | -46.5 | 8.45 | -392.925 |
1670 | 222 | 1596.5 | 207.55 | 73.5 | 14.45 | 1062.075 |
1830 | 230 | 1596.5 | 207.55 | 233.5 | 22.45 | 5242.075 |
1980 | 260 | 1596.5 | 207.55 | 383.5 | 52.45 | 20114.575 |
2140 | 291 | 1596.5 | 207.55 | 543.5 | 83.45 | 45355.075 |
2260 | 304 | 1596.5 | 207.55 | 663.5 | 96.45 | 63994.575 |
2410 | 314 | 1596.5 | 207.55 | 813.5 | 106.45 | 86597.075 |
2550 | 321 | 1596.5 | 207.55 | 953.5 | 113.45 | 108174.575 |
2730 | 338 | 1596.5 | 207.55 | 1133.5 | 130.45 | 147865.075 |
2890 | 344 | 1596.5 | 207.55 | 1293.5 | 136.45 | 176498.075 |
3010 | 364 | 1596.5 | 207.55 | 1413.5 | 156.45 | 221142.075 |
Figure 16 - Table On Processed Data Table For Calculation Of Pearson’s Correlation Coefficient In Group 1
Calculation:
\(\bar x=\frac{∑x}{N}=\frac{31930}{20}=1596.5\)
\(\bar y=\frac{∑y}{N}=\frac{4151}{20}=207.55\)
\(∑(x-\bar x)(y-\bar y)=1841298.5\)
\(∑(x-\bar x)^2=15108055\)
\(∑(y-\bar y)^2=226822.95\)
Let, the Pearson’s Correlation Coefficient be ℜ.
\(R = \frac{∑(x-\bar x)(y-\bar y)}{∑(x-\bar x)^2\times∑(y-\bar y)^2}\)
\(=> R = \frac{1841298.5}{15108055226822.95}\)
R = 0.998
x | y | \( x-\bar x\) | \(y-\bar y\) | \((x-\bar x)(y-\bar y)\) | \((x-\bar x)^2\) | \((y-\bar y)^2\) |
---|---|---|---|---|---|---|
150 | 4 | 1596.5 | 66.75 | -1446.5 | -62.75 | 90767.875 |
310 | 10 | 1596.5 | 66.75 | -1286.5 | -56.75 | 73008.875 |
460 | 19 | 1596.5 | 66.75 | -1136.5 | -47.75 | 54267.875 |
580 | 22 | 1596.5 | 66.75 | -1016.5 | -44.75 | 45488.375 |
780 | 30 | 1596.5 | 66.75 | -816.5 | -36.75 | 30006.375 |
920 | 40 | 1596.5 | 66.75 | -676.5 | -26.75 | 18096.375 |
1080 | 40 | 1596.5 | 66.75 | -516.5 | -26.75 | 18096.375 |
1230 | 40 | 1596.5 | 66.75 | -366.5 | -26.75 | 9803.875 |
1400 | 52 | 1596.5 | 66.75 | -196.5 | -14.75 | 2898.375 |
1550 | 55 | 1596.5 | 66.75 | -46.5 | -11.75 | 546.375 |
1670 | 85 | 1596.5 | 66.75 | 73.5 | 18.25 | 1341.375 |
1830 | 86 | 1596.5 | 66.75 | 233.5 | 19.25 | 4494.875 |
1980 | 91 | 1596.5 | 66.75 | 383.5 | 24.25 | 9299.875 |
2140 | 94 | 1596.5 | 66.75 | 543.5 | 27.25 | 14810.375 |
2260 | 95 | 1596.5 | 66.75 | 663.5 | 28.25 | 18743.875 |
2410 | 104 | 1596.5 | 66.75 | 813.5 | 37.25 | 30302.875 |
2550 | 108 | 1596.5 | 66.75 | 953.5 | 41.25 | 39331.875 |
2730 | 115 | 1596.5 | 66.75 | 1133.5 | 48.25 | 54691.375 |
2890 | 121 | 1596.5 | 66.75 | 1293.5 | 54.25 | 70172.375 |
3010 | 124 | 1596.5 | 66.75 | 1413.5 | 57.25 | 80922.875 |
Calculation
\(\bar x=\frac{\sum x}{N}=\frac{31930}{20} = 1596.5\)
\(\bar y=\frac{\sum y}{N}=\frac{1335}{20} = 66.75\)
\(\sum(x-\bar x)(y-\bar y)=662812.5\)
\(\sum(x-\bar x)^2=15108055\)
\(\sum(y-\bar y)^2=29763.75\)
Let, the Pearson’s Correlation Coefficient be ℜ.
\(R=\frac{\sum(x-\bar x)(y-\bar y)}{\sqrt{\sum(x-\bar x)^2\times\sum(y-\bar y)}}\)
\(=>R=\frac{662812.5}{\sqrt{15108055\times29763.75}}\)
R = 0.988
x | y | \(x-\bar x\) | \(y-\bar y\) | \((x-\bar x)(y-\bar y)\) | \((x-\bar x)^2\) | \((y-\bar y)^2\) |
---|---|---|---|---|---|---|
150 | 34 | 1596.5 | 269.05 | -1446.5 | -235.05 | 339999.825 |
310 | 70 | 1596.5 | 269.05 | -1286.5 | -199.05 | 256077.825 |
460 | 102 | 1596.5 | 269.05 | -1136.5 | -167.05 | 189852.325 |
580 | 126 | 1596.5 | 269.05 | -1016.5 | -143.05 | 145410.325 |
780 | 147 | 1596.5 | 269.05 | -816.5 | -122.05 | 99653.825 |
920 | 150 | 1596.5 | 269.05 | -676.5 | -119.05 | 80537.325 |
1080 | 175 | 1596.5 | 269.05 | -516.5 | -94.05 | 48576.825 |
1230 | 203 | 1596.5 | 269.05 | -366.5 | -66.05 | 24207.325 |
1400 | 235 | 1596.5 | 269.05 | -196.5 | -34.05 | 6690.825 |
1550 | 269 | 1596.5 | 269.05 | -46.5 | -0.05 | 2.325 |
1670 | 290 | 1596.5 | 269.05 | 73.5 | 20.95 | 1539.825 |
1830 | 304 | 1596.5 | 269.05 | 233.5 | 34.95 | 8160.825 |
1980 | 329 | 1596.5 | 269.05 | 383.5 | 59.95 | 22990.825 |
2140 | 352 | 1596.5 | 269.05 | 543.5 | 82.95 | 45083.325 |
2260 | 372 | 1596.5 | 269.05 | 663.5 | 102.95 | 68307.325 |
2410 | 393 | 1596.5 | 269.05 | 813.5 | 123.95 | 100833.325 |
2550 | 415 | 1596.5 | 269.05 | 953.5 | 145.95 | 139163.325 |
2730 | 442 | 1596.5 | 269.05 | 1133.5 | 172.95 | 196038.825 |
2890 | 476 | 1596.5 | 269.05 | 1293.5 | 206.95 | 267689.825 |
3010 | 497 | 1596.5 | 269.05 | 1413.5 | 227.95 | 322207.325 |
Calculation
\(\bar x=\frac{\sum x}{N}=\frac{31930}{20} = 1596.5\)
\(\bar y=\frac{\sum y}{N}=\frac{5381}{20}= 269.05\)
\(\sum(x-\bar x)(y-\bar y)=2363023.5\)
\(\sum(x-\bar x)^2=15108055\)
\(\sum(y-\bar y)^2=370774.95\)
Let, the Pearson’s Correlation Coefficient be ℜ.
\(R=\frac{\sum (x-\bar x)(y-\bar y)}{\sqrt{\sum(x-\bar x)^2\times\sum(y-\bar y)^2}}\)
\(=> R=\frac{2363023.5}{\sqrt{15108055370774.95}}\)
R = 0.998
In this IA, a correlation has been developed between the total number of individuals who are going to their respective work places after calling off of Lockdown in India and thereby getting infected by COVID-19. For the first group, i.e., between the age of 25 years and 35 years, working individuals are getting infected by COVID-19 in an increasing fashion. The trendline shows an increasing direct relationship between the number of individuals going out for their work and the ones those who are getting infected. The equation of the trendline is shown below:
y = 0.1219x + 12.976
The value of R^{2} correlation coefficient for the graph is 0.9894 which validates the fact that the relation is linear and increasing. Furthermore, the value of Pearson’s Correlation Coefficient for this graph is 0.998. As the value is very close to 1, it justifies the claim that the relationship is linear and being a positive value, it satisfies the claim that the relationship is increasing or direct. The reason behind such a correlation is assumed to be the work load that is there on the individuals of this age group. On the other hand, being on the lower side of the age group, their immunity is comparatively less strong than that of the other age groups. Thus, the spread of infection is taking a significant number in this age group.
For the second group, i.e., between the age of 35 years and 45 years, working individuals are getting infected by COVID-19 is showing a direct increasing relation. The equation of the trendline is shown below:
y = 0.0439x - 3.2908
The value of R^{2} correlation coefficient for the graph is 0.977 which validates the fact that the relation is linear and increasing. Furthermore, the value of Pearson’s Correlation Coefficient for this graph is 0.988. As the value is very close to 1, it justifies the claim that the relationship is linear and being a positive value, it satisfies the claim that the relationship is increasing or direct. The spread is comparatively less in this group as the immunity of the individuals of this age group is considerably more and with an increase in age, people often tend to be more cautious and aware and take significant preventive measures to protect them from the disease.
Finally, in the third group, i.e., between the age of 45 years and 55 years, working individuals are getting infected by COVID-19 is showing a direct increasing relation. The equation of the trendline is shown below:
y = 0.1564x + 19.344
The value of R^{2} correlation coefficient for the graph is 0.9968 which validates the fact that the relation is linear and increasing. Furthermore, the value of Pearson’s Correlation Coefficient for this graph is 0.998. As the value is very close to 1, it justifies the claim that the relationship is linear and being a positive value, it satisfies the claim that the relationship is increasing or direct. This group has shown maximum infection than that of the other groups. It is due to the fact that, this age group is quite close to the limit of senior citizens. According to the doctors, people aged more than 50 are more vulnerable to the disease which is significantly proved in its correlative study.