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COVID-19 Pandemic in India: Chronological Comparison of the Regional Heterogeneity in the Progression of the Pandemic and Gaps in Mitigation Strategies
Journal of Clinical Trials

Journal of Clinical Trials
Open Access

ISSN: 2167-0870

+44 20 3868 9735

Research Article - (2021)

COVID-19 Pandemic in India: Chronological Comparison of the Regional Heterogeneity in the Progression of the Pandemic and Gaps in Mitigation Strategies

Satabdi Datta1, Neloy Kumar Chakroborty2, Deepinder Sharda1, Komal Attri1,3 and Diptiman Choudhury1,3*
 
*Correspondence: Diptiman Choudhury, School of Chemistry and Biochemistry, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India, Tel: +91-8196949843, Email:

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Abstract

Background: The progressions of the first and second waves of the COVID-19 pandemic in India were heterogeneous in different regions and their respective states and union territories. Our work aims to understand the major differences between the two waves and the mitigation strategies implemented during these waves.

Methods: A cross-sectional analysis of the temporal variations in new cases and fatalities in all the states of India was done for both the first wave (30th January 2020 to 31st January 2021) and second wave (1st February 2021 to 29 th May 2021) of the pandemic. Variations in different epidemiological parameters, like case fatality ratio (CFR), cumulative case ratio (CCR), and cumulative death ratio (CDR) were quantified. In the majority of states and UTs, the test-to-case ratio has been found below than the WHO recommended mark.

Results: The Southern and Western regions were the top contributors of cases and fatalities in both waves. The state of Punjab and Maharashtra reported the highest CFR (3.24 and 2.5 respectively) in the country during the first wave and Andaman and Nicobar Islands (2.6), and Punjab (2.25) reported the highest CFR during the second. Goa and Delhi respectively showed the highest CCR and CDR during the first wave, whereas Lakshadweep and Goa respectively reported the highest CCR and CDR in the second wave.

Conclusion: The study comprehends the chronological heterogeneity in the patterns of pandemic progression and the severity of the second wave over all the states of the country, highlighting the major hotspot regions and some gaps in mitigation strategies.

Keywords

COVID-19; Pandemic; First wave; Second wave; Regional heterogeneity; Hotspot regions

Abbreviations

COVID-19: Coronavirus Disease 2019; NR: Northern Region; CR: Central Region; WR: Western Region; ER: Eastern Region; NER: North Eastern Region; SR: Southern Region; UT: Union Territory; CCR: Cumulative Case Rate; CDR: Cumulative Death Rate; CFR: Case Fatality Ratio; SARS CoV-2: Severe Acute Respiratory Syndrome Coronavirus-2; LAGE: Legislative Assembly General Election; WHO: World Health Organization

Introduction

India is currently the second-largest contributor of total COVID-19 cases of the world, accounting for about 16% of the total cases and around 9% of the deaths worldwide [1]. The state of Kerala has reported the first case of COVID-19 in India on January 30th, 2020 [2]. Thereafter, from March 2020, the number of active cases started to rise at a rapid pace. Amid this crisis, the Government of India announced a nationwide lockdown with implementations of measures for public health, nevertheless, the COVID-19 cases started rising once again after the first phase of unlocking, from May 31st, 2020 [3]. In most of the states, cases started to surge from June 2020, which reached their respective maxima in the middle of September 2020 [4]. The first wave however subsided towards the end of January 2021 which led to the withdrawal of several restrictions on social and political gatherings [5]. However, the consequence of this relaxation soon turned out to be catastrophic as the second wave of COVID-19 pandemic commenced from the middle of February 2021, which compelled the state governments to implement lockdown-like restrictions again [6,7]. The emergence of new indigenous and international mutants of the SARS-CoV-2 virus was accessed as one of the major reasons for the steep rise in cases and fatalities in the second wave [8,9].

The Indian subcontinent is characterized by diverse geographical and demographical regions, populated by heterogeneous cultural, political, linguistic, and ethnic groups of people covering an area of 3.28 million square kilometers with a total population of about 138 cores [10-14]. This huge diversity is likely to contribute to the visible heterogeneity in the progression patterns of the pandemic in each region of the country, which therefore demands the detailed analysis of chronological heterogeneity in the regional and state-specific infection rates, death rates, wave patterns, and testing capacities for a clear interpretation of the progression patterns of the two wave of the COVID-19 pandemic across the country. Previous publications on the effects of COVID-19 pandemic in India have either discussed the adverse impacts of the pandemic on the social structure, food security, mental and psychological health of the people, economic growth, and healthcare infrastructure or highlighted the gaps in communication between the health agencies and the Government of India, or demonstrated the containment measures, and the progression of the pandemic during the earlier part of the first wave [15-21]. However, no former study has reported the temporal dynamics of cases, deaths, and recoveries in the states and union territories of India and their respective contributions to the nation’s COVID-19 situation throughout the first and second wave of the pandemic. In this study, for the first time, we did a comprehensive analysis of the chronological changes in the patterns of progression of the pandemic during the first and second wave in each region of the country and its respective states and union territories-India comprises 28 states and 8 unions territories, divided along 6 administrative subdivisions, the Northern Region (NR), Central Region (CR), Western Region (WR), Eastern Region (ER), North Eastern Region (NER) and the Southern Region (SR) (Table 1 and Table 2) [22,23]. The analyses, revealing the differential progression patterns of the two waves, contribute significantly to the evaluations of the real scenario of the COVID-19 pandemic across the nation as well as disclose the gaps in pandemic mitigation strategies.

States and Union Territories (UTs) Population First Report Peak TC PCC TD PDC CFR CCR CDR TR RPC TT TPC
Northern India
Haryana 29000000 4/3/2020 19-09-2020 267897 2.49 3019 1.955 1.127 923.783 10.41 263874 0.985 5170000 19.298
Himachal Pradesh 7300000 14-03-2020 28-11-2020 57536 0.535 970 0.628 1.686 788.164 13.288 56195 0.977 930000 16.164
Uttarakhand 11000000 15-03-2020 19-09-2020 96129 0.894 1644 1.065 1.71 873.9 14.945 91966 0.957 2130000 22.158
Punjab 30000000 9/3/2020 19-09-2020 173276 1.611 5621 3.64 3.244 577.587 18.737 121822 0.703 4480000 25.855
Rajasthan 77000000 3/3/2020 5/7/2020 397491 3.695 2766 1.791 0.696 516.222 3.592 312564 0.786 5850000 14.717
Chandigarh (UT) 1180000 19-03-2020 16-09-2020 20925 0.194 334 0.216 1.596 1,773.31 28.305 20426 0.976 200000 9.558
Delhi (UT) 20000000 2/3/2020 14-11-2020 635096 5.903 10853 7.028 1.709 3,175.48 54.265 622882 0.981 11000000 17.32
Jammu and Kashmir (UT) 13000000 9/3/2020 20-09-2020 124506 1.157 1936 1.254 1.555 957.738 14.892 121822 0.978 4540000 36.464
Ladakh (UT) 290000 7/3/2020 8/10/2020 9720 0.09 130 0.084 1.337 3,351.72 44.828 9523 0.98 NA NA
Central India
Chattisgarh 29000000 19-03-2020 21-09-2020 305367 2.838 3701 2.397 1.212 1,052.99 12.762 297339 0.974 4220000 13.819
Madhya Pradesh 82000000 20-03-2020 23-09-2020 255112 2.371 3810 2.467 1.493 311.112 4.646 248367 0.974 5360000 21.01
Uttar Pradesh 22500000 4/3/2020 17-09-2020 600299 5.58 8658 5.606 1.442 2,668.00 38.48 586116 0.976 28000000 46.643
Western India
Goa 1540000 25-03-2020 24-09-2020 53409 0.496 768 0.497 1.438 3,468.12 49.87 59891 1.121 450000 8.426
Gujarat 68000000 19-03-2020 3/10/2020 261540 2.431 4387 2.841 1.677 384.618 6.451 253803 0.97 11000000 42.059
Maharashtra 1.22E+08 9/3/2020 17-09-2020 2026399 18.835 51082 33.078 2.521 1,660.98 41.87 1929005 0.952 15000000 7.402
Dadra and Nagar Haveli and Daman and Diu (UT) 960000 9/4/2020 14-08-2020 3380 0.031 2 0.001 0.059 352.083 0.208 3342 0.989 1100 0.325
Eastern India
Bihar 1.2E+08 22-03-2020 15-08-2020 260719 2.423 1501 0.972 0.576 217.266 1.251 258018 0.99 21000000 80.546
Jharkhand 37000000 31-03-2020 9/9/2020 118692 1.103 1072 0.694 0.903 320.789 2.897 117067 0.986 5220000 43.979
Odhisa 44000000 16-03-2020 24-09-2020 335072 3.114 1959 1.269 0.585 761.527 4.452 332103 0.991 7710000 23.01
West Bengal 97000000 17-03-2020 22-10-2020 569998 5.298 10173 6.588 1.785 587.627 10.488 554272 0.972 8000000 14.035
North Eastern India
Arunachal Pradesh 52000000 2/4/2020 16-10-2020 16828 0.156 56 0.036 0.333 32.362 0.108 16759 0.996 390000 23.176
Assam 34000000 31-03-2020 30-09-2020 217141 2.018 1082 0.701 0.498 638.65 3.182 214178 0.986 6470000 29.796
Manipur 3100000 24-03-2020 27-10-2020 29070 0.27 371 0.24 1.276 937.742 11.968 28553 0.982 520000 17.888
Meghalaya 3220000 13-04-2020 16-10-2020 13764 0.128 146 0.095 1.061 427.453 4.534 13550 0.984 330000 23.976
Mizoram 1190000 26-03-2020 22-09-2020 4372 0.041 9 0.006 0.206 367.395 0.756 4330 0.99 210000 48.033
Tripura 3990000 7/4/2020 13-09-2020 33347 0.31 388 0.251 1.164 835.764 9.724 32915 0.987 610000 18.293
Nagaland 2150000 6/4/2020 14-08-2020 12094 0.112 144 0.093 1.191 562.512 6.698 11806 0.976 120000 9.922
Sikkim 660000 4/5/2020 26-09-2020 6090 0.057 133 0.086 2.184 922.727 20.152 5775 0.948 74700 12.266
Southern India
Andhra Pradesh 52000000 12/3/2020 2/9/2020 887836 8.252 7153 4.632 0.806 1,707.38 13.756 879405 0.991 18000000 20.274
Karnataka 66000000 9/3/2020 10/10/2020 939387 8.731 12217 7.911 1.301 1,423.31 18.511 921112 0.981 17000000 18.097
Kerala 35000000 30-01-2020 9/10/2020 929179 8.637 3744 2.424 0.403 2,654.80 10.697 854206 0.919 9630000 10.364
Tamil Nadu 76000000 7/4/2020 3/7/2020 838340 7.792 12356 8.001 1.474 1,103.08 16.258 821430 0.98 16000000 19.085
Telangana 37000000 2/3/2020 4/9/2020 294469 2.737 1599 1.035 0.543 795.862 4.322 290630 0.987 7860000 26.692
Andaman and Nicobar Islands (UT) 400000 26-03-2020 15-08-2020 4994 0.046 62 0.04 1.241 1,248.50 15.5 4928 0.987 220000 44.053
Puducherry (UT) 1500000 17-03-2020 26-09-2020 39068 0.363 648 0.42 1.659 2,604.53 43.2 38135 0.976 580000 14.846
Lakshadweep (UT) 68000 16-01-2020 13-03-2021 87 0.001 0 0 0 127.941 0 49 0.563 2300 26.437

Table 1: First wave of COVID-19 in India: An account of the impact of the first wave of COVID-19, as of January 31st, 2021, in the different regions and their respective states and UT’s of the country and testing
ratios.

States and Union Territories (UTs) Population Peak (Till Date) TC PCC TD PDC CFR CCR CDR TR RPC TT TPC
Northern India
Haryana 29000000 9/5/2021 486040 2.83 5113 2.98 1.052 1676 17.631 458837 0.944 23830000 49.029
Himachal Pradesh 7300000 13-05-2021 131068 0.76 2100 1.224 1.602 1795.452 28.767 112329 0.857 970000 7.401
Uttarakhand 11000000 15-05-2021 230983 1.34 4716 2.749 2.042 2099.845 42.873 191996 0.831 2600000 11.256
Punjab 30000000 12/5/2021 389555 2.27 8684 5.061 2.229 1298.517 28.947 384527 0.987 460000 1.181
Rajasthan 77000000 15-05-2021 538671 3.14 10213 5.953 1.896 699.573 13.264 599188 1.112 11150000 20.699
Chandigarh (UT) 1180000 10/5/2021 38815 0.22 406 0.237 1.046 3289.407 34.407 36108 0.93 300000 7.729
Delhi (UT) 20000000 28-04-2021 789550 4.6 13220 7.705 1.674 3947.75 66.1 764656 0.968 8000000 10.132
Jammu and Kashmir (UT) 13000000 13-05-2021 162178 0.94 1905 1.11 1.175 1247.523 14.654 121766 0.751 3950000 24.356
Ladakh (UT) 290000 22-04-2021 8590 0.05 57 0.033 0.664 2962.069 19.655 6994 0.814 250000 29.104
Central India

Chattisgarh 29000000 16-05-2021 662278 3.86 9278 5.408 1.401 2283.717 31.993 614413 0.928 4800000 7.248
Madhya Pradesh 82000000 11/5/2021 522237 3.04 4149 2.418 0.794 636.874 5.06 490124 0.939 5640000 10.8
Uttar Pradesh 22500000 30-04-2021 1087833 6.34 11550 6.732 1.062 4834.813 51.333 1035607 0.952 21000000 19.304
Western India

Goa 1540000 13-05-2021 101010 0.58 1829 1.066 1.811 6559.091 118.766 76875 0.761 370000 3.663
Gujarat 68000000 2/5/2021 544077 3.17 5403 3.149 0.993 800.113 7.946 503321 0.925 6000000 11.028
Maharashtra 122000000 22-04-2021 3686816 21.51 42948 25.032 1.165 3021.98 35.203 3410833 0.925 20000000 5.425
Dadra and Nagar Haveli and Daman and Diu (UT) 960000 29-04-2021 6823 0.03 2 0.001 0.029 710.729 0.208 6507 0.954 1100 0.161
Eastern India

Bihar 120000000 6/5/2021 443454 2.58 3551 2.07 0.801 369.545 2.959 420018 0.947 9000000 20.295
Jharkhand 37000000 8/5/2021 217548 1.26 3887 2.266 1.787 587.968 10.505 203269 0.934 3170000 14.571
Odhisa 44000000 23-05-2021 412071 2.4 780 0.455 0.189 936.525 1.773 326545 0.792 4290000 10.411
West Bengal 97000000 15-05-2021 784958 4.58 5095 2.97 0.649 809.235 5.253 683018 0.87 4000000 5.096
North-Eastern India

Arunachal Pradesh 52000000 24-05-2021 9950 0.05 58 0.034 0.583 19.135 0.112 5987 0.602 190000 19.095
Assam 34000000 20-05-2021 186482 1.08 2163 1.261 1.16 548.476 6.362 129905 0.697 4530000 24.292
Manipur 3100000 26-05-2021 19780 0.11 405 0.236 2.048 638.065 13.065 11491 0.581 190000 9.606
Meghalaya 3220000 20-05-2021 20684 0.12 405 0.236 1.958 642.36 12.578 12606 0.609 240000 11.603
Mizoram 1190000 25-05-2021 7287 0.04 26 0.015 0.357 612.353 2.185 4533 0.622 170000 23.329
Tripura 3990000 23-05-2021 16535 0.09 107 0.062 0.647 414.411 2.682 9493 0.574 310000 18.748
Nagaland 2150000 13-05-2021 9277 0.05 206 0.12 2.221 431.488 9.581 3608 0.389 70000 7.546
Sikkim 660000 2/5/2021 8823 0.05 114 0.066 1.292 1336.818 17.273 4841 0.549 45300 5.134
Southern India

Andhra Pradesh 52000000 30-04-2021 783906 4.57 3585 2.09 0.457 1507.512 6.894 607977 0.776 1000000 1.276
Karnataka 66000000 4/5/2021 1628062 9.5 16081 9.373 0.988 2466.761 24.365 1267952 0.779 12000000 7.371
Kerala 35000000 15-05-2021 1565207 9.13 4712 2.746 0.301 4472.02 13.463 1398299 0.893 10370000 6.625
Tamil Nadu 76000000 27-04-2021 1201376 7.01 10905 6.356 0.908 1580.758 14.349 884868 0.737 11000000 9.156
Telangana 37000000 2/5/2021 279557 1.63 1648 0.961 0.59 755.559 4.454 243232 0.87 7140000 25.54
Andaman and Nicobar Islands (UT) 400000 1/5/2021 1970 0.01 51 0.03 2.589 492.5 12.75 1732 0.879 170000 86.294
Puducherry (UT) 1500000 11/5/2021 63828 0.37 849 0.495 1.33 4255.2 56.6 50111 0.785 460000 7.207
Lakshadweep (UT) 68000 1/5/2021 7544 0.04 31 0.018 0.411 11094.11 45.588 5547 0.735 127700 16.927

Table 2: Second Wave of COVID-19 in India (Till May 29th, 2021): An account of the impact of the second wave in different regions and their respective states and union territories, and the testing ratio.

Materials and Methods

Study design and data sources

A detailed study of the COVID-19 infections and their related statistics in India was done for the periods, from January 30th, 2020 to January 31st, 2021 for the first wave, and from February 1st, 2021 to May 29th, 2021 for the second wave. The day, reporting the highest number of active cases was defined as the peak of the wave. The pandemic wave was defined by a phase of a rising number of COVID-19 cases with a definite peak, followed by a phase of the declining number of cases, or the trough period (in which the rates of new infections and active cases have declined significantly). The days of first reported infection, rising (a 10% consistent increase in the number of active cases) and declining phases of the two waves, peaks of the waves, numbers of new infections, death, recoveries along with the testing and demographical data for all the 6 administrative regions, comprising of 28 states and 8 union territories (UTs) of the country, were obtained from the online monitoring official website of the Government of India [4] and other national and international websites [24]. The numbers of monthly new cases, deaths, and recoveries for each state were calculated and the chronological (monthly) contributions of each administrative region of the subcontinent (contributions from all the states and union territories of the concerned region were summed up) to the nation's total number of new cases, deaths, and recoveries were calculated.

Statistical analysis

Extensive analysis was performed to establish the epidemiological parameters, such as CCR or cumulative case rate (cases per 1 lakh or 100000 populations), CDR or cumulative death rate (deaths per 1 lakh), CFR or case fatality ratio (number of deaths reported per number of cases reported × 100), and TPC or test per case (total number of tests/total number of cases) for both the first and second waves. These values were plotted on the Indian political map using https://mapchart.net/india.html to depict the heterogeneous progression of the pandemic in the states and UTs during the two waves. The weekly average numbers of new confirmed cases, active cases, and deaths were quantified by averaging the changes in numbers between the successive days over a week. The fold changes in average daily new cases for each month (before, during, and after LAGE) for the 5 states, which underwent elections, were calculated and compared with 5-other states that didn’t experience any major public gathering.

Discussion

The second wave of COVID-19 pandemic resulted in total unexpected disarrays in all states of the country. While the first wave caused a little over 1.08 crore infections and over 1.5 lakhs fatality in the whole country within 11 months, the ongoing second wave resulted in over 1.7 crore infections and nearly 2 lakhs fatalities in a span of merely 4 months. The daily average number of new cases during April 2021 was around 2.31 lakhs which soared to about 3.01 lakhs cases/day in May 2021, which are nearly 2.7 and 3.5 times the average daily cases (87,000 cases/day) of September 2020, when India reported its peak of the first wave. The CCR and CDR for the second wave were also found to elevate substantially in comparison to the first wave (Table 1), however, the CFR for both the waves did not show much variation for the majority of states (except for Maharashtra and Punjab) until May 29th, 2021 (Table 2). The advent of new and more infectious variants of the virus, inaccuracy in diagnosis, lack of testing, non-transparency in data sharing, low rate of vaccination, decreased rate of genome sequencing of the positive samples along with unchecked social, religious, and political gatherings, the inflexible attitude of the citizens (violating the containment protocols), and importantly, the serious lack of persistent measures to mitigate COVID-19, despite previous warnings, were held responsible for this unprecedented escalation of the pandemic in the country by many reports [15,32- 34]. Despite an increase in the daily test numbers, the test-to-case ratio has dropped in the majority of states during the second wave (Figure 3D and Table 2). Thus, the need of the hour is to accelerate the testing rates both in the rural and urban regions of the nation to get a clear picture of the infection scenario, which will aid to avert the nucleation of another catastrophic wave of the pandemic [35,36].

Our study comprehends that the Southern and Western regions were the major hotspots of the pandemic in both waves. The state of Karnataka and Tamil Nadu from the SR and Maharashtra from the WR showed consistently high numbers of infections and fatalities throughout the two waves. These results indicate that irrespective of the presence of the highly infectious B.1.617 Indian variant of the SARS-CoV-2 in these states during the second wave, shortfalls in the health systems and deficits in the implementations of COVID-19 restriction protocols may be responsible for the upsurge of the pandemic in these territories.

Although the data on demographical differences in infection are not made available to the public by the Ministry of Health and Family Welfare, Govt. of India, however, many recent reports have suggested that a notable difference between the first and second wave in the country is that the second wave affected a large portion of the pediatric group, which remained largely asymptomatic in the first wave [37]. As of the first week of April 2021 (between March 1st to April 4th , 2021), it was reported that over 79,000 children were affected by the disease from the five states which include Maharashtra, Chattisgarh, Karnataka, Uttar Pradesh, and Delhi, of which over 60,000 cases were from Maharashtra [38], although the number of hospitalization among the children was only a handful. In India, the gap between the peaks of the two waves was about 7.5 months, which was a bit larger than the average gap of around 5 months reported for the majority of countries; however, the escalation of the second wave was far higher in this country than others [37]. At present, the second wave is in its declining phase for the majority of Indian states, albeit experts have suggested that unless the vaccination rate is accelerated, the country is set to face the 3rd wave by the end of this year, which may affect the unvaccinated population in large numbers [39-42]. Thus, ramping up of the clinical trials of vaccines both for the pediatric and adult groups, strengthening of the health infrastructure of the nation, wide-scale genomic sequencing of the positive samples, monitoring of the progression of new cases, and strict implementation of COVID-19 appropriate restrictions for the public are the urgent needs of the hour to protect the future of the country and its people from another COVID-19 tsunami [43].

Results

A chronological overview of the contributions of different regions of India towards the cases and fatalities in the two waves of the COVID-19 pandemic

The chronological variations in the number of new cases and new deaths in India (Figures 1A and 1B) and the contributions of different regions towards the total cases and fatalities of the nation are noteworthy. At the beginning of the first wave, the SR accounted for nearly 40% of the nations’ total cases in March 2020. From April 2020 till June 2020, the WR remained the major contributor of COVID-19 cases of the nation (43%, 46%, and 31% of the total cases respective in these months). Subsequently from June 2020, when the first phase of unlocking (unlock-1) started in the country (Figure 1A), till the end of October 2020, the SR contributed 41%, 40%, 34%, and 41% of the total COVID-19 cases of India in the respective months. In November 2020, the NR was the major contributor (31%), whereas from December 2020, till February 2021, SR again contributed respectively 31%, 47%, and 43% of the total cases of the nation. At the beginning of the rising phase of the second wave, from March 2021 till April 2021, the WR contributed about 63%, and 30% of the nations' total cases respectively, and in May 2021, the SR became the major contributor of new cases (42%) in the country (Figure 1C).

Chronological

Figure 1: Chronological variations of cases and deaths during the first and second COVID-19 wave in India: Variations in numbers of the daily cases (A) and deaths (B) during the first and second wave of the COVID-19 pandemic (from January 30th , 2020 to May 29th , 2021) in India are represented using the bar graphs. The next set of bar graphs are representing the chronological variation, during the months of the first and second waves, in contributions of the different regions of India towards the total percentages of COVID-19 cases (C) and deaths (D). The pie charts are denoting the total contributions of the different regions of India to the total percentages of cases (E) and deaths (F) during the first and second waves of the pandemic in the country.

In terms of fatality, the WR accounted for the highest monthly fatality from March 2020 till October 2020 (contributed nearly 35%, 59%, 62%, 51%, 41%, 37%, 39%, and 31% of the total monthly deaths of the nation respectively) (Figure 1D). The NR contributed over 33% and 31% of total fatality in November and December 2020, and the WR was the highest monthly contributor of COVID-19 related fatality from January 2021 till April 2021 (30%, 40%, 39%, and 33% of the total fatality in the respective months). However, in May 2021, the SR accounted for the maximum proportion of fatality of the country (28%) (Figure 1D). Thus, it can be inferred from these results that both the Western and Southern Indian states have been the major hotspots of the COVID-19 pandemic in the Indian subcontinent (Figures 1E and 1F)

The first wave of COVID-19 in India-A detailed analysis

Apart from Kerala, most of the Indian states reported their first COVID-19 infection in March or April 2020. The Government of India has implemented a nationwide complete lockdown and strict containment protocols from March 25th, 2020 to combat the dissemination of the viral infection. The unlock process, implemented in six phases, commenced from June first, 2020 to November 30th, 2020, although the daily cases began to surge steadily, especially from the end of May 2020 to September 2020 in the majority of Indian states (Table 1) [25]. The adverse effects of unlocking can be understood from the following statistics – from the week, before the last week of lockdown (May 18th, 2020 to May 24th, 2020) to the week, after one continuous week of unlock-1 (June 8th, 2020 to June 14th, 2020), the change in weekly-average numbers of new confirmed cases, active cases, and deaths respectively showed an increase from 6369 to 11170 (1.75-fold), from 3153 to 4048 (1.28-fold), and from 144 to 340 (2.35-fold) (analysis not shown). India reported the maximum number of daily new cases on September 16th, 2020 (around 98,000) and the maximum active cases on September 17th, 2020 (1018454), which was marked as the peak of the first wave. Subsequently, the figures of daily cases started to drop steadily in the following weeks, till the end of January 2021, when the number reduced around 10,000 cases/day; marking the trough of the wave. Among the 10758629 cases reported during the first wave, the highest number of cases was reported from SR (3933360), contributing 36.5% of the total cases where the state of Karnataka was the top contributor (~8.7%) (Table 1). The WR contributed 21.6% of the nation’s total cases, where the top contributor was Maharashtra (2026399 cases, ~18.8%). The NR accounted for about 16.4% of total cases, where the highest contributor was Delhi (5.9%). The ER shared 11.8% of the total cases, where West Bengal contributed maximally (~5.3%). The CR reported 10.7% of the total cases in which Uttar Pradesh contributed the majority of cases (5.5%). The NER was the lowest contributor of cases (3%) in the first wave where Assam contributed the major number of cases (2%) (Table 1). The CCR was found to be highest in Ladakh (3352 cases/1 lakh of the population), followed by Delhi (3175) and Chandigarh (1773).

In addition to contributing to the second-highest number of COVID-19 cases, states of the WR also contributed the maximum number of deaths during the first wave (accounted for 36.4% of the nation’s total deaths of 154428). The state of Maharashtra accounted for the most number of deaths in the region (33%). The trend also remained similar for the SR, which accounted for 24.4% of the nation’s total deaths (Table 1). Contributions from the Northern states were about 17.7% to the total fatalities of the nation (Table 1), whereas, the CR and ER respectively accounted for 10.5% and ~9.5% of the total deaths (b). The NER states reported the least mortality and contributed only ~1.51% of the total deaths of the nation (Table 1). The highest CDR was however reported by the national capital, Delhi (54/1 lakh of the population), followed by Goa (50), Ladakh (45), and Maharashtra (42) (Table 1). The CFR was found to be highest in the state of Punjab (3.24) in the NR, followed by Maharashtra (2.5) in the WR with Sikkim (2.18) stands next from the NER. The state of West Bengal (1.785) accounted for the highest CFR from the ER, followed by Uttarakhand (1.71), Delhi (1.71), and Himachal Pradesh (1.69) from the NR. Among the states of Southern India, Puducherry recorded the highest CFR (1.66) (Figures 2A and 2C and Figures 3A and 3C) (Table 1).

cumulative

Figure 2: Maps of COVID-19 cumulative cases and deaths for the Indian states and UTs during the two waves: Different states and union territories (UTs) of India are color-coded to represent their different cumulative case rates during the first (A) and second wave (B) of the pandemic. Similarly, the differences in cumulative death rates for the states and UTs are represented for the first (C) and second wave (D).

fatality

Figure 3: Maps of case fatality ratio and test per case for the Indian states and UTs during the two waves: Different states and union territories (UTs) of India are color-coded to represent their different case fatality ratios during the first (A) and second wave (B) of the pandemic. Similarly, the differences in test per case (test-to-case ratio) for the states and UTs are represented for the first (C) and second wave (D).

Regarding the testing of cases, until the end of January 2021, we found that more than 228.4 million COVID-19 tests were performed all over India. The WHO has recommended an optimum of 30 test-to-case ratios (T: C) as standard; however apart from the states and UT, like Jammu and Kashmir (36.5), Uttar Pradesh (46.6), Gujarat (42), Bihar (80.5), Jharkhand (44), Assam (29.7), Mizoram (48), and Andaman and Nicobar Islands (44), all other states and union territories had a T: C ratio less than the WHO recommended mark [26] (Table 1).

The second wave of COVID-19 in India: A detailed analysis

The period between the end of February 2021 and early March 2021, marked the rising phase of the catastrophic second wave of the COVID-19 pandemic in India. During this period, the country reported more than 3 lakhs new cases/day in April that soared to over 4 lakhs cases/day and over 4000 daily deaths in the first week of May 2021, which abruptly overburdened the healthcare system of the country. On May 6th, 2021, India reported its highest number of over 414280 new cases in a day, which marked the peak of the second wave until now. Thereafter, the daily cases started to decrease gradually, however the fatality rate remained high.

Starting from February first till May 29th, 2021, India reported a total number of 17134975 cases and over 1.7 lakh deaths. Until May 29th, 2021, the SR remained the top contributor of cases (32%) and also accounted for 22% of the country’s fatality, where the state of Karnataka (9.5%) and Kerala (9.1%) together accounted for more than half of the total cases of the region, and Karnataka (9.3%) and Tamil Nadu (6.3%), like in the first wave, were also the major contributors to the nation’s fatalities (Table 2). The WR contributed over 25% and 29% of the cases and fatalities respectively during this period. The NR, CR, and ER contributed 16%, ~13%, and ~10.8% respectively to the total cases, and 27%, 14.5%, and 7.7% to the nation’s total deaths respectively. The NER reported the least number of cases (1.63%) and fatalities (2%) in the country (Table 2).

Until May 29th , 2021, the highest CFR was reported by Andaman and Nicobar Islands (2.59), followed by Punjab (2.23) from the NR and Nagaland (2.22) from the NER. The highest CDR was found for Goa (118 deaths/1 lakh), followed by Uttar Pradesh (51), and Lakshadweep (45). Lakshadweep was also found to contribute highest to the CCR (11084 cases/1lakh), followed by Uttar Pradesh (4835) and Chandigarh (3289). It is noteworthy that regarding test per case, apart from Andaman and Nicobar Islands (86.2), Haryana (49), and Ladakh (29.1), all other states and union territories were lagging behind the recommended WHO level26 (Figures 2B and 2D and Figures 3B and 3D).

One of the other major contrasts between the first and second waves is the evolution of new variants of the novel SARS-CoV-2 virus during the second wave. The three imported viral variants of concern that have been identified in India are the UK (B.1.1.7) variant, the South African (B.1.351) variant, and the Brazilian (P1) variant, among which the B.1.1.7 variant was predominantly present in Delhi and Punjab in April 2021. Among the two novel variants of India, the highly contagious B.1.617.2 (Delta) variant from Maharashtra has started evolving rapidly and overtook the B.1.618 variant in West Bengal, and eventually became the major variant in most of the states. The WHO has already designated the delta variant as a 'variant of concern' in May 2021. This variant has spread in all states across the nation and is identified as one of the major causes of the calamity associated with the second wave of COVID-19 in India, as per the study conducted by the Indian SARS-CoV-2 Genomics Consortium (INSACOG), which was launched by the Ministry of Health and Family Welfare, Govt. of India, on December 30th, 2020 [27,28].

Roles of some major mass-scale public events, during the second wave, on COVID-19-related outcomes

Mass-scale public gatherings, like Kumbh Mela 2021 (9 million participants) and the state elections (covering 243 million residents) have contributed to the escalated spreading of the second wave in the nation [29,30]. A 117-fold increase in the number of weekly- average new cases and a 266-fold increase in the weekly-average new deaths were found for the state of Uttarakhand when we compared the data between before and after the festival of Kumbh Mela 2021. Uttar Pradesh, like Uttarakhand, also showed a sharp jump in the weekly average new cases and deaths – respectively a 240-fold and 201-fold increase in the numbers of new cases and deaths after this religious festival (Table S1 and Figure S1). These numbers are much higher than the fold-increase found for the new cases (20.9-fold) and new deaths (35.4-fold) for the entire nation between the mentioned periods. Four Indian states, Assam, Kerala, Tamil Nadu, West Bengal, and one UT, Puducherry, that witnessed their ‘legislative assembly general elections’ (LAGEs) from the end of March to the end of April 2021, also reported a substantial increase in the average daily cases after the elections compared to some of the other states with no LAGE or major public gatherings during the same period (Table S2 and Figure S2). These increases in numbers are mainly attributed to the election- related rallies and mass-scale public meetings which were organized by the local political parties [31]. However, it is noteworthy that we could not dissect out the exact contributions of such gatherings from the general increase in the numbers of cases and deaths due to aggravation of the pandemic itself using the available data.

Conclusion

The study comprehends the chronological heterogeneity in the patterns of pandemic progression and the severity of the second wave over all the states of the country, highlighting the major hotspot regions and some gaps in mitigation strategies.

Funding Conclusion

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgements

SD (CSIR-RA) is thankful to the Council of Scientific and Industrial Research, Government of India, for the fellowship, Post-doctoral Research Associateship (award no. 09/677(0055)/2020-EMR-I). KA and DC are thankful to TIET-VT-CEEMS for research fellowships. NKC is thankful to Thapar School of Liberal Arts & Sciences for the Director’s Discretionary Fellowship.

Conflict of Interests

The authors declare no conflict of interests.

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Author Info

Satabdi Datta1, Neloy Kumar Chakroborty2, Deepinder Sharda1, Komal Attri1,3 and Diptiman Choudhury1,3*
 
1School of Chemistry and Biochemistry, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India
2Thapar School of Liberal arts and Sciences, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India
3Thapar Institute of Enginnering and Technology, Virginia Tech (USA) Centre of Excellence in Emerging Materials, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India
 

Citation: Datta S, Chakroborty NK, Sharda D, Attri K, Choudhury D (2021) COVID-19 Pandemic in India: Chronological Comparison of the Regional Heterogeneity in the Progression of the Pandemic and Gaps in Mitigation Strategies. J Clin Trials. S13:004.

Received: 21-Jul-2021 Accepted: 04-Aug-2021 Published: 11-Aug-2021 , DOI: 10.35248/2167-0870.21.11.004

Copyright: © 2021 Datta S et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.