Research on the Fluctuation Trend of the Spread of New Coronary Pneumonia Epidemic under Non-Strict Prevention and Control Conditions: Analysis Based on the Data of India s Epidemic

Research Article

Austin J Infect Dis. 2021; 8(4): 1058.

Research on the Fluctuation Trend of the Spread of New Coronary Pneumonia Epidemic under Non-Strict Prevention and Control Conditions: Analysis Based on the Data of India’s Epidemic

Liu HL¹*, Liang Q² and Wang W³

1Associate Researcher, Health and Family Planning Statistics Information Center and Northwest Population Information Center Gansu, China

2Associate Researcher, Health Care Security Administration Gansu, China

3Health Care Security Administration Gansu, China

*Corresponding author: Hong Liang Liu, Associate Researcher, Health and Family Planning Statistics Information Center and Northwest Population Information Center Gansu, China

Received: September 06, 2021; Accepted: October 08, 2021; Published: October 15, 2021

Abstract

After a long time of joint efforts, the Covid-19 pandemic in some countries and regions has been effectively controlled, but since April 2021, the outbreak of the epidemic in India has posed new challenges for the prevention and control of the global epidemic. How to understand and interpret the rebound trend of the epidemic in India is an important reference for other regions to prevent and control the epidemic. Based on time-series analysis, this paper uses the system dynamics epidemic model to treat the population in the region as a whole and decomposes the spread of the Covid-19 in India into 4 fluctuating transmission processes. These processes show that the spread of the Covid-19 may have annual periodic characteristics and long-term trends. There is a critical period of about 45 days before the outbreak of the epidemic. The global prevention and control requires the joint efforts of all countries to end as soon as possible.

Keywords: Covid-19; Fluctuating trend; India; Non-strict Prevention and Control Conditions

Introduction

With the implementation of stricter isolation prevention and control measures and the wider application of vaccine, the prevention and control of the Covid-19 has achieved decisive progress in some countries, and the spread of the epidemic has been effectively controlled [1]. However, with the relaxing regional vigilance against the virus and the emergence of new mutations, the epidemic in some regions has a trend of secondary outbreaks, the most typical of which is India.

Since 2020, the epidemic situation in India has undergone several major stages: rapid spread, initial control, and easing. However, since late April 2021, with the holding of several large festivals such as the Big Pot Festival in India and the emergence of new mutant viruses, the Covid-19 in India is showing a trend of second outbreak. In the face of complex situations, quickly judging the trend of the epidemic and identifying key links in epidemic prevention and control are important for government to make effective decisions in the postepidemic era [2,3].

Based on limited bulletins and demographic data. This paper uses the system dynamics method to establish an estimation model of the spread of the epidemic and make a theoretical estimate of the long-term fluctuating trend of the spread of the Covid-19 under nonstrictly controlled conditions in India which has important reference value for in-depth understanding of the severity and extent of the spread of the epidemic.

Data Source

This study uses the daily updated Indian epidemic progress data published in the real-time big data report of the new coronavirus pneumonia epidemic on the Baidu platform. The population and other data come from the Indian census data (Table 1) [4].

Table 1: Statistics on the number of newly infected people in India from April 8, 2020 to May 5, 2021.





  

    
Date

    
Number of    newly infected 

      people

    
Date

    
Number of    newly infected 

      people

    
Date

    
Number of    newly infected 

      people

    
Date

    
Number of    newly infected people 

    
Date

    
Number of    newly infected 

      people

    
Date

    
Number of newly infected

      people

    
Date

    
Number of    newly infected people 

  

  

    
4.8

    
565

    
6.4

    
9551

    
7.31

    
57704

    
9.26

    
72404 

    
11.2

    
63702

    
1.18

    
5952

    
3.16

    
6490

  

  

    
4.9

    
809

    
6.5

    
9361

    
8.1

    
59225

    
9.27

    
125885 

    
11.2

    
41822

    
1.19

    
9975

    
3.17

    
62246

  

  

    
4.1

    
875

    
6.6

    
10157

    
8.2

    
52783

    
9.28

    
45816 

    
11.2

    
23157

    
1.2

    
23568 

    
3.18

    
36330

  

  

    
4.11

    
846

    
6.7

    
10607

    
8.3

    
50681

    
9.29

    
69268 

    
11.3

    
51126

    
1.21

    
13388 

    
3.19

    
42635

  

  

    
4.12

    
759

    
6.8

    
9803

    
8.4

    
70385

    
9.3

    
88682 

    
11.3

    
45960

    
1.22

    
14811 

    
3.2

    
38393

  

  

    
4.13

    
1248

    
6.9

    
9300

    
8.5

    
57258

    
10.1

    
77843 

    
11.3

    
17683

    
1.23

    
11166 

    
3.21

    
17175

  

  

    
4.14

    
1034

    
6.1

    
11311

    
8.6

    
62815

    
10.2

    
115721 

    
11.3

    
84288

    
1.24

    
15558 

    
3.22

    
74892

  

  

    
4.15

    
835

    
6.11

    
12247

    
8.7

    
61455

    
10.3

    
70948 

    
11.3

    
39036

    
1.25

    
10897 

    
3.23

    
43924

  

  

    
4.16

    
1060

    
6.12

    
8950

    
8.8

    
71338

    
10.4

    
63762 

    
11.3

    
42098

    
1.26

    
17167 

    
3.24

    
53793

  

  

    
4.17

    
922

    
6.13

    
11417

    
8.9

    
69947

    
10.5

    
76778 

    
12.1

    
21252

    
1.27

    
1077

    
3.25

    
14250

  

  

    
4.18

    
1370

    
6.14

    
7114

    
8.1

    
45334

    
10.6

    
73924 

    
12.2

    
39172

    
1.28

    
12451 

    
3.26

    
66265

  

  

    
4.19

    
1250

    
6.15

    
11703

    
8.11

    
78320

    
10.7

    
40330 

    
12.3

    
33203

    
1.29

    
18241 

    
3.27

    
105259

  

  

    
4.2

    
924

    
6.16

    
11636

    
8.12

    
49563

    
10.8

    
77103 

    
12.4

    
36807

    
1.3

    
19338 

    
3.28

    
24422

  

  

    
4.21

    
1541

    
6.17

    
11685

    
8.13

    
59240

    
10.9

    
104785 

    
12.5

    
31601

    
1.31

    
9915

    
3.29

    
99523

  

  

    
4.22

    
1290

    
6.18

    
12228

    
8.14

    
74689

    
10.1

    
51254 

    
12.6

    
41795

    
2.1

    
13953 

    
3.3

    
20817

  

  

    
4.23

    
1669

    
6.19

    
13542

    
8.15

    
24696

    
10.1

    
66103 

    
12.7

    
22218

    
2.2

    
4814

    
3.31

    
93260

  

  

    
4.24

    
1408

    
6.2

    
15448

    
8.16

    
103313

    
10.1

    
96850 

    
12.8

    
25006

    
2.3

    
19145 

    
4.01

    
25837

  

  

    
4.25

    
1836

    
6.21

    
21041

    
8.17

    
50058

    
10.1

    
45118 

    
12.9

    
42302

    
2.4

    
10888 

    
4.02

    
90046

  

  

    
4.26

    
1607

    
6.22

    
8943

    
8.18

    
47904

    
10.1

    
69665 

    
12.1

    
23876

    
2.5

    
6766

    
4.03

    
156632

  

  

    
4.27

    
1561

    
6.23

    
18905

    
8.19

    
81939

    
10.2

    
73702 

    
12.1

    
33578

    
2.6

    
10357 

    
4.04

    
32141

  

  

    
4.28

    
1873

    
6.24

    
15940

    
8.2

    
59016

    
10.2

    
67248 

    
12.1

    
30258

    
2.7

    
15132 

    
4.05

    
116537

  

  

    
4.29

    
1738

    
6.25

    
15626

    
8.21

    
52164

    
10.2

    
59034 

    
12.1

    
37035

    
2.8

    
14749 

    
4.06

    
107822

  

  

    
4.3

    
1800

    
6.26

    
20685

    
8.22

    
112676

    
10.2

    
61197 

    
12.1

    
20905

    
2.9

    
2017

    
4.7

    
114706

  

  

    
5.1

    
2394

    
6.27

    
25874

    
8.23

    
41912

    
10.2

    
37398 

    
12.2

    
22800

    
2.1

    
11012 

    
4.8

    
130458

  

  

    
5.2

    
2442

    
6.28

    
20416

    
8.24

    
69834

    
10.2

    
70812 

    
12.2

    
8935

    
2.11

    
19701 

    
4.9

    
186103

  

  

    
5.3

    
2806

    
6.29

    
14303

    
8.25

    
62089

    
10.2

    
56386 

    
12.2

    
32969

    
2.12

    
2036

    
4.1

    
97141

  

  

    
5.4

    
3932

    
6.3

    
12469

    
8.26

    
74664

    
10.2

    
25924 

    
12.2

    
20983

    
2.13

    
13844 

    
4.11

    
183468

  

  

    
5.5

    
2963

    
7.1

    
18777

    
8.27

    
91396

    
10.2

    
54457 

    
12.2

    
28024

    
2.14

    
15951 

    
4.12

    
119717

  

  

    
5.6

    
3587

    
7.2

    
18783

    
8.28

    
76605

    
10.2

    
47480 

    
12.2

    
31158

    
2.15

    
9027

    
4.13

    
149858

  

  

    
5.7

    
3364

    
7.3

    
20895

    
8.29

    
22737

    
10.3

    
44438

    
12.2

    
20065

    
2.16

    
11876 

    
4.14

    
246155 

  

  

    
5.8

    
3342

    
7.4

    
31107

    
8.3

    
106557

    
10.3

    
44438

    
12.2

    
27605

    
2.17

    
11456 

    
4.15

    
307867 

  

  

    
5.9

    
3113

    
7.5

    
23272

    
8.31

    
65832

    
10.3

    
56861

    
12.2

    
16455

    
2.18

    
13234 

    
4.16

    
197356 

  

  

    
5.1

    
4353

    
7.6

    
25160

    
9.1

    
66292

    
10.3

    
31377

    
12.2

    
29959

    
2.19

    
13048 

    
4.17

    
266277 

  

  

    
5.11

    
3607

    
7.7

    
26726

    
9.2

    
94694

    
10.3

    
64249

    
12.3

    
16688

    
2.2

    
20438 

    
4.18

    
308056 

  










Table 1: Statistics on the number of newly infected people in India from April 8, 2020 to May 5, 2021.

Table 1 of 1: 





  

    
5.12

    
3475

    
7.8

    
21115

    
9.3

    
93883

    
10.3

    
24047

    
12.3

    
20689

    
2.21

    
8153

    
4.19

    
199100 

  

  

    
5.13

    
3763

    
7.9

    
29888

    
9.4

    
88904

    
10.3

    
84009

    
12.3

    
30133

    
2.22

    
10844 

    
4.2

    
288956 

  

  

    
5.14

    
3942

    
7.1

    
27998

    
9.5

    
99138

    
11.1

    
30129

    
12.3

    
16072

    
2.23

    
19449 

    
4.21

    
315802 

  

  

    
5.15

    
3787

    
7.11

    
28928

    
9.6

    
67943

    
11.2

    
42177

    
12.3

    
12320

    
2.24

    
15811 

    
4.22

    
332503 

  

  

    
5.16

    
4864

    
7.12

    
23924

    
9.7

    
76468

    
11.3

    
54316

    
12.3

    
23501

    
2.25

    
13008 

    
4.23

    
326769 

  

  

    
5.17

    
5050

    
7.13

    
27181

    
9.8

    
76168

    
11.4

    
47251

    
12.3

    
22006

    
2.26

    
21024 

    
4.24

    
350013 

  

  

    
5.18

    
4463

    
7.14

    
34770

    
9.9

    
104421

    
11.5

    
28216

    
1.1

    
10404

    
2.27

    
16292 

    
4.25

    
246812 

  

  

    
5.19

    
3131

    
7.15

    
26543

    
9.1

    
76839

    
11.6

    
58655

    
1.2

    
17750

    
2.28

    
8697

    
4.26

    
402732 

  

  

    
5.2

    
4527

    
7.16

    
41870

    
9.11

    
111760

    
11.7

    
39300

    
1.3

    
26291

    
3.1

    
18240 

    
4.27

    
348279 

  

  

    
5.21

    
6659

    
7.17

    
34634

    
9.12

    
82321

    
11.8

    
52731

    
1.4

    
8049

    
3.2

    
15266 

    
4.28

    
307219 

  

  

    
5.22

    
9595

    
7.18

    
37407

    
9.13

    
100123

    
11.9

    
45269

    
1.5

    
24396

    
3.3

    
15675 

    
4.29

    
386888 

  

  

    
5.23

    
6433

    
7.19

    
50203

    
9.14

    
89449

    
11.1

    
25248

    
1.6

    
18504

    
3.4

    
19039 

    
4.3

    
402110 

  

  

    
5.24

    
7102

    
7.2

    
36810

    
9.15

    
85055

    
11.1

    
57576

    
1.7

    
17079

    
3.5

    
19485 

    
5.01

    
392562 

  

  

    
5.25

    
3620

    
7.21

    
44165

    
9.16

    
97721

    
11.1

    
31108

    
1.8

    
21310

    
3.6

    
13528 

    
5.02

    
370059 

  

  

    
5.26

    
9085

    
7.22

    
48987

    
9.17

    
81087

    
11.1

    
60633

    
1.9

    
21727

    
3.7

    
24109 

    
5.03

    
355828 

  

  

    
5.27

    
4507

    
7.23

    
42903

    
9.18

    
86573

    
11.1

    
39506

    
1.1

    
12045

    
3.8

    
13702 

    
5.04

    
382691 

  

  

    
5.28

    
8300

    
7.24

    
60135

    
9.19

    
95429

    
11.2

    
46277

    
1.11

    
13517

    
3.9

    
18760 

    
5.05

    
412618 

  

  

    
5.29

    
9239

    
7.25

    
59701

    
9.2

    
93367

    
11.2

    
30820

    
1.12

    
11724

    
3.1

    
5466

    
    
  

  

    
5.3

    
8262

    
7.26

    
41030

    
9.21

    
100327

    
11.2

    
19130

    
1.13

    
12050

    
3.11

    
21327

    
    
  

  

    
5.31

    
9144

    
7.27

    
41857

    
9.22

    
62685

    
11.2

    
38480

    
1.14

    
27982

    
3.12

    
39586

    
    
  

  

    
6.1

    
5072

    
7.28

    
63594

    
9.23

    
89324

    
11.2

    
49443

    
1.15

    
14913

    
3.13

    
26583

    
    
  

  

    
6.2

    
8023

    
7.29

    
49587

    
9.24

    
96134

    
11.2

    
46110

    
1.16

    
15819

    
3.14

    
28898

    
    
  

  

    
6.3

    
11804

    
7.3

    
54966

    
9.25

    
77605

    
11.2

    
44281

    
1.17

    
10536

    
3.15

    
21669

    
    
  










Table 1 of 1:

Model Construction

Analysis of epidemic spread in India

The growth of the cumulative number of confirmed cases in India can be roughly divided into three main stages. The first stage is the period from India’s reporting of the number of infections to July 12, 2020. At this stage, the cumulative number of confirmed infections remains below 1 million, and the cumulative scale of infections is exponentially distributed. The exponential function is used to estimate the scale growth. Goodness of fitR2 = 0.9798, the function form is:

Pi1 = 94289e0.023i (i = 1, 2,…,96) (1)

The second stage is from July 13, 2020 to April 2, 2021. In this stage, the cumulative number of confirmed diagnoses gradually declines after experiencing a rapid increase in a slightly normal distribution. The epidemic control form is basically stable. The cumulative infection scale increases as a function of a long quadratic curve which is used to estimate the scale growth, the goodness of fitR2 = 0.9994, and the function form is:

Pi2 = 0.0115i4 - 5.9487i3 + 801.21i2 + 35756i + 895069 (i = 1, 2, …, 264) (2)

The third stage is from April 3, 2021 to May 5, 2021 (Number of changes in the scale of May 5 notified on May 6). At this stage, the cumulative number of confirmed infections returns to an exponential growth pattern. Using the double-exponential function to estimate the scale growth, the goodness of fitR2 = 0.9997, the function form is:

Pi3 = 1.954E6e-0.1129i + 1.075E7e0.0204i (i = 1, 2, …, 33) (3)

From the analysis of the changes in the scale of the cumulative number of confirmed infections, since April 2021 Covid-19 in India has entered a new round of explosive growth (Figure 1).

Figure 1: Statistics of the cumulative number of confirmed cases in India from April 8, 2020 to May 5, 2021.

    

    
    

    

    Figure 1: Statistics of the cumulative number of confirmed cases in India from April 8, 2020 to May 5, 2021.

Figure 2: Statistics of daily changes in the proportion of the cumulative number of confirmed diagnoses in India to the uninfected population.

    

    
    

    

    Figure 2: Statistics of daily changes in the proportion of the cumulative number of confirmed diagnoses in India to the uninfected population.

Establishing a trend-fitting function based on the daily change of the proportion of the cumulative number of confirmed diagnose to the uninfected population

In order to more intuitively observe and analyze the trend of changes in the scale of the cumulative number of confirmed cases in India, this paper uses the bulletin data and the total population of India (1.354 billion people) in the census bulletin to calculate the next day’s growth rate of the cumulative number of confirmed cases to the uninfected population since April 8, 2020, and analyze its distribution [5]. A clear trend of fluctuations can be observed. Using Matlab to establish a general model of sum of Sin4 for fitting, Goodness of fit R² = 0.8818, the function form is:

Pit = a1 sin (b1i + c1) + a2 sin (b2i + c2) + a3 sin (b3i + c3) + a4 sin (b4i + c4) (i = 1, 2, …, 392) (4)

a1=0.02354, b1=0.009094, c1=-0.466; a2=0.02698, b2=0.01779, c2=0.2827; a3=0.008255, b3=0.03032, c3=0.9764; a4=0.004568, b4=0.03796, c4=3.267.

Based on the analysis of the distribution of daily changes in the proportion of the cumulative number of diagnosed people in the uninfected population, the development of the new case in India has experienced three main processes of rise, fall, and slowdown. Now it has a clear trend of entering the rising stage again.

Predictions of the future 60-day change

Assuming that from May 6, 2021, the Indian government continues to adopt stricter epidemic prevention and control measures after July 13, 2020, and can continue to provide medical assistance services, then it can be assumed that the spread of Covid-19 is likely to follow the pattern of spread since July 13, 2020 in India within the next 60 days. The fitting function (4) can be used to estimate the daily change of the cumulative number of confirmed diagnoses to the number of uninfected population in the next 60 days, and then to get the daily newly diagnosed number and cumulative number of confirmed diagnoses. The steps are as follows:

Ki = K(i-1)(1 + Pt i) (i = 393, 394,…, 454)

(i = 393, 394, …, 454) (6)

(i = 393,394,…,454) (7)

In formulas (5)-(7), Ki, Pti, PAi, P2i are the proportion of the daily cumulative number of confirmed diagnoses to the number of uninfected people, the change the proportion of the daily cumulative number of confirmed diagnoses to the number of uninfected people calculated by the fitting function (4), the daily number of newly diagnosed and daily uninfected people.

Results

The spread of the Covid-19 in India has entered a new round of rapid growth

If there is no effective epidemic prevention and control to prompt or immediately stop the interpersonal transmission of the virus and treatment methods, India will face an extremely complicated process of explosive transmission in the next 60 days. The total number of confirmed cases after 60 days is likely to exceed 40 million (Table 2).

Table 2: Prediction of the number of new and cumulative diagnoses in India within 60 days after May 6, 2021.





  

    
Date

    
Number of diagnoses per day

    
Cumulative    confirmed number 

    
Date

    
Number of diagnoses per day

    
Cumulative    confirmed number 

    
Date

    
Number of diagnoses per day

    
Cumulative    confirmed number 

  

  

    
5.6 

    
350719

    
21421571

    
5.26

    
404774

    
29413790

    
6.15

    
323210

    
35801920

  

  

    
5.7 

    
510110

    
21931681

    
5.27

    
355302

    
29769092

    
6.16

    
257365

    
36059285

  

  

    
5.8 

    
375741

    
22307422

    
5.28

    
528189

    
30297281

    
6.17

    
118326

    
36177610

  

  

    
5.9 

    
456995

    
22764417

    
5.29

    
323830

    
30621112

    
6.18

    
248560

    
36426170

  

  

    
5.1 

    
408275

    
23172692

    
5.3

    
291031

    
30912143

    
6.19

    
216715

    
36642885

  

  

    
5.11

    
388220

    
23560911

    
5.31

    
350441

    
31262584

    
6.2 

    
202830

    
36845715

  

  

    
5.12

    
446031

    
24006943

    
6.1

    
337414

    
31599997

    
6.21

    
202830

    
37048545

  

  

    
5.13

    
370108

    
24377051

    
6.2

    
184080

    
31784077

    
6.22

    
259533

    
37308078

  

  

    
5.14

    
395148

    
24772199

    
6.3

    
351924

    
32136001

    
6.23

    
143215

    
37451293

  

  

    
5.15

    
435570

    
25207769

    
6.4

    
478274

    
32614275

    
6.24

    
293254

    
37744547

  

  

    
5.16

    
426158

    
25633927

    
6.5

    
233940

    
32848215

    
6.25

    
109759

    
37854305

  

  

    
5.17

    
457926

    
26091854

    
6.6

    
301716

    
33149932

    
6.26

    
383445

    
38237750

  

  

    
5.18

    
286115

    
26377969

    
6.7

    
442056

    
33591987

    
6.27

    
137519

    
38375269

  

  

    
5.19

    
407705

    
26785674

    
6.8

    
205934

    
33797921

    
6.28

    
192510

    
38567779

  

  

    
5.2 

    
438788

    
27224461

    
6.9

    
317974

    
34115896

    
6.29

    
247916

    
38815696

  

  

    
5.21

    
354215

    
27578677

    
6.1

    
336401

    
34452296

    
6.3 

    
215669

    
39031365

  

  

    
5.22

    
330476

    
27909153

    
6.11

    
306942

    
34759239

    
7.1 

    
128787

    
39160152

  

  

    
5.23

    
574581

    
28483734

    
6.12

    
269451

    
35028690

    
7.2 

    
267721

    
39427873

  

  

    
5.24

    
209120

    
28692854

    
6.13

    
279324

    
35308013

    
7.3 

    
179378

    
39607252

  

  

    
5.25

    
316162

    
29009016

    
6.14

    
170697

    
35478710

    
7.4 

    
240682

    
39847934

  

  

    
Unit: Person. 

  










Table 2: Prediction of the number of new and cumulative diagnoses in India within 60 days after May 6, 2021.

The number of deaths will continue to increase in India

Calculated based on the estimated death rate of India’s Covid-19 reported on May 5, 2021, which is about 1%, if the cumulative number of confirmed diagnoses in India exceeds 40 million, as the pressure of treatment continues to increase, the death toll may exceed 400000 in the next 60 days people.

Adopting strict isolation and control measures is the first choice for India’s current response strategy

Based on the experience of epidemic prevention and control in various countries around the world, to maintain moderate social distance and implement strict epidemic prevention and control measures before effective protection measures for vaccination are effective measures for India.

Discussion

From the perspective of time series, the spread of the Covid-19 may have annual periodic characteristics and long-term transmission trends. If we put aside the concern about the transmission pattern of how specific cases were infected, and treat the population in the region as a whole, the transmission process of Covid-19 in India can be approximately decomposed into four different cases in a time series (Figure 3). The main reason for the large-scale spread of the epidemic in India since April 2021 can be seen as the superimposed effect caused by the fluctuation trends 2, 3, and 4 in the figure, which have entered the upward space. Among them, the trend 2 that may represent inter-annual fluctuations is the main aspect. From a practical perspective, it can be considered that the increase in the spread of the epidemic has become a reality due to the loosening of prevention and control measures.

Figure 3: Decomposition of fluctuation trends in the spread of the epidemic in India.

    

    
    

    

    Figure 3: Decomposition of fluctuation trends in the spread of the epidemic in India.

The outbreak trend has a critical window period of approximately 45 days for prevention and control. Observing the daily changes in the proportion of the number of infected people in the uninfected population in India, we can find that the critical turning point is the 315th day, and the exact date is February 17, 2021.It took 45 days from this day to April 2nd. During these 45 days, if strict prevention and control measures continue to be implemented, it is entirely possible that the Indian epidemic will continue to maintain a steady decline. Unfortunately, India has relaxed its epidemic prevention and control efforts during this critical period. The rebound trend of the epidemic in India has also provided warnings to other countries and regions where the epidemic is basically under control. After the epidemic is stable and controllable, strict prevention and control measures should be maintained for at least 45 days, especially in areas with strong population mobility. Normalization and strict control will be the most efficient prevention and control strategy.

The predicted value of the model may reflect the serious consequences that India will have if it does not adhere to strict prevention and control measures, but the severity of the consequences is not limited to the upper limit predicted by the model. The prevention and control of the epidemic should not only focus on densely populated urban communities, but rural areas with inconvenient transportation and insufficient medical service provision should be the key areas for strict prevention.

Faced with the risk that the new crown virus may continue to mutate, rapidly expanding the scope of effective vaccines is a permanent solution to the global pandemic of the new crown pneumonia epidemic. As long as the epidemic is still spreading in some areas and the movement of people between regions cannot be stopped, the danger of another outbreak still exists at all times. From this perspective, no matter how different the region, ethnic group, culture, system is, human beings as a group should unite and deal with it together.

The shortcomings of this study are also obvious. It did not systematically summarize the evidence of the previous research, did not do too much authenticity inference about the data source, did not list the cluster analysis process and results due to space limitations, and did not explicitly give the estimated results and process of the scale of the cured population and the dead population. These problems will be solved in detail in other follow-up studies.

Funding

Monitoring and Evaluation of the Implementation of the Gansu Population Development Plan (2016-2030) (WTHT2020-XXZX001); Gansu Health Industry Research Plan Project: Epidemic Monitoring, Prevention and Control and Emergency Management Decision Support Based on Health and Medical Big Data (GSWSKY2020-34).

References

  1. “WHO: The new crown pneumonia epidemic poses a global pandemic threat ‘very real’ People’s Daily. 2020.
  2. Hong Liang Liu, Hong Wen Jia, et al. System Dynamics Model Estimation Method and Evaluation of the Early Spreading Scale of New Coronary Pneumonia-A Case Study of Gansu Province. Journal of Electronic Science University. 2020.
  3. Yan Wang, Jiyu Wu, et al. Model construction and analysis of the growth law of the mid-term scale of the spread of new coronary pneumonia--a study based on the Italian epidemic data. Northwest Population Journal. 2020: 114- 126.
  4. Baidu Epidemic.
  5. India population.

Citation: Liu HL, Liang Q and Wang W. Research on the Fluctuation Trend of the Spread of New Coronary Pneumonia Epidemic under Non-Strict Prevention and Control Conditions: Analysis Based on the Data of India’s Epidemic. Austin J Infect Dis. 2021; 8(4): 1058.