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Background: The second wave of COVID-19 is affecting most of the world. The scenario is very grim in India where the daily count on April 15, 2021 itself is double of the first peak. The epidemic evolution there is quite complex due to regional inhomogeneities. In this paper, we characterize the virus spread in the ongoing second wave in India, as well as study the dynamical evolution of epidemic from the beginning of the pandemic. Methods: Variations in the effective reproduction number (Rt) in India are taken as a quantifiable measure of the virus transmissibility and are compared with those of other countries where the second wave is already over. Further, characteristics of COVID-19 spread are analyzed for Indian states by estimating test positivity and case fatality rates. Finally, forecasts and actionable inputs are provided based on mathematical and epidemiological models. Results: Effective reproduction number for almost every state in India has value greater than 1 indicating the presence of the second wave. An exponential fit on recent data indicates that the infection rate is much higher than the first wave however the case fatality rate is lower. Preliminary estimates with the SIR model suggest the peak for the second wave to occur in mid-May 2021 with daily count exceeding 0.35 million. Conclusions: The spread of the second wave is much faster than the first wave. Hence, quick and effective administrative intervention is needed to arrest the rapid growth of the epidemic.
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Characterization of the Second Wave of COVID-19 in India
Rajesh Ranjan ·Aryan Sharma ·Mahendra K. Verma
April 21, 2021
Background: The second wave of COVID-19 is affecting most of the world. The scenario is very grim in India
where the daily count on April 20, 2021 itself is about triple of the first peak. The epidemic evolution there is
quite complex due to regional inhomogeneities. In this paper, we characterize the virus spread in the ongoing
second wave in India, as well as study the dynamical evolution of epidemic from the beginning of the pandemic.
Methods: Variations in the effective reproduction number (Rt) in India are taken as a quantifiable measure of
the virus transmissibility and are compared with those of other countries where the second wave is already over.
Further, characteristics of COVID-19 spread are analyzed for Indian states by estimating test positivity and case
fatality rates. Finally, forecasts and actionable inputs are provided based on mathematical and epidemiological
Results: Effective reproduction number for almost every state in India has value greater than the threshold
value of 1. An exponential fit on recent data shows that the infection rate is much higher than the first wave
however the case fatality rate is lower. Preliminary estimates with the SIR model suggest the peak for the
second wave to occur in mid-May 2021 with daily count exceeding 0.4 million.
Conclusions: Data shows that the second wave has reached the rural area, causing devastation considering
poor healthcare infrastructure in these regions. Further, the spread of the second wave is much faster than the
first wave. Hence, quick and effective administrative intervention is needed to arrest the rapid growth of the
Keywords SARS-CoV-2 ·COVID-19 ·Epidemic Evolution ·Second Wave ·Coronavirus ·Reproduction
This material is based partly upon work supported by a SERB MATRICS project SERB/F/847/2020-2021.
Rajesh Ranjan
Department of Aerospace Engineering
Indian Institute of Technology
Kanpur, India 208016
Aryan Sharma
Department of Physics
Indian Institute of Technology
Kanpur, India 208016
Mahendra K. Verma
Department of Physics
Indian Institute of Technology
Kanpur, India 208016
2 Rajesh Ranjan et al.
1 Introduction
More than one year since COVID-19 was declared a pandemic on March 11, 2020, by World Health Organization
(WHO), the deadly SARS-CoV-2 virus continues to disrupt public life across the world. Although the lockdown
norms are relaxed in most places, social life is still far from normal. Recently, multiple vaccines developed by
Oxford–AstraZeneca (Covishield/Vaxzevria), Pfizer–BioNTech (Comirnaty), Moderna, Johnson & Johnson’s
Janssen, Bharat Biotech (Covaxin), Gamaleya Research Institute of Epidemiology and Microbiology (Sputnik
V) etc. have been approved in several countries and are given on priority to susceptible populations and those
with co-morbidities. However, the production and distribution of vaccines at a massive scale to cover a very
large population remain a formidable challenge. Meanwhile, in order to arrest the spread of the virus during the
vaccination drive, intervention measures such as wearing masks, social distancing guidelines, partial lockdowns,
and restricted store hours are still in place in most places.
While nations are taking extensive measures to accelerate the vaccination drive in order to control the
pandemic at the earliest, a public health challenge has appeared due to mutations of the SARS-CoV-2 virus
which make it highly contagious. For example, the SARS-CoV-2 lineage B.1.1.7, which was first detected in
the United Kingdom (UK) in November 2020, is estimated to be 40-80% more transmissible than the wild type
SARS-CoV-2 (Davies et al 2021; Volz et al 2021). Similarly, strains from South Africa (B.1.351), Brazil (P.1),
and India (B.1.617) are also significantly more contagious (Chen et al 2020) than the variants in early 2020.
There is no clear evidence on severity of the new mutations (Davies et al 2021), however the challenge is to
prepare for health response especially when the number of infections is exceedingly large.
Figure 1(a,b) shows the temporal variations of daily COVID-19 cases and deaths in the top six countries
which are most affected in terms of the maximum number of cumulative cases as of April 19, 2021. The data
has been taken from the repository maintained by ‘Our World in Data’. Among the countries shown, the United
States (US) has maximum cumulative cases with a total of about 32.5 million, followed by India that has 15.6
million cases. Although if we compare the epidemic growth in these countries as shown in the figure, India
recently has almost tripled the number of daily cases compared to the US. The plot also shows the peaks of
the first and second waves in different countries where they were observed. The US, for example, had the first
peak in mid-July 2020 following which the cases subsided. However, the infection counts started increasing in
October and a much larger peak in the second wave in December 2020 with daily cases of up to 0.25 million.
In the UK, the first and second peaks were separated by only a couple of months with the second wave largely
attributed to a more infectious mutant (Leung et al 2021). For Russia, the infection curve is relatively shallower
with the number of daily cases never crossing 30,000. In the curve for Brazil, there are large fluctuations which
may be due to insufficient testing, however the number of deaths per infection (typically called Case Fatality
Rate (CFR)) is very large, as can be inferred from figure 1(b). The curve for the death cases typically follows
the infections. Note however that the CFR was very high at the beginning of the pandemic (around April 2020)
for the US and European countries (Borghesi et al 2021).
For India, as seen in figures 1(a) and (b), the first and second waves are separated by about 5 months.
The peak of the first wave was in September 2020 with the daily cases of around 0.1 million. The daily cases
decreased until mid-February after which it exhibited a sharp increase. The end of the first wave was likely a
result of a combination of factors – effective implementation of government interventions, increase in awareness,
and, most importantly, the experience gained by medical professionals in treating the disease over the initial
months. On April 15, 2021, the number of new cases was about 0.2 million which is already more than double
of the first peak value. The sudden surge in the number of cases after a relatively long ‘cooling’ time is baffling
although it may be attributed to highly infectious double mutant variant of SARS-CoV-2 (B.1.617 lineage), to
negligent behaviour of the population, and to the relaxation of interventions (Xu and Li 2020). The number of
daily deaths is also rising recently, but the CFR is low compared to the first wave; this aspect will be further
discussed later. Note that the study on B.1.617 mutant is limited and there is no clear evidence on whether the
mutant virus is less severe than its predecessor.
We also look at the regional distribution of COVID-19 spread to further characterize the second wave.
Figure 2(a,b) shows the daily numbers of cases in 16 key states in India in linear and log scales respectively.
In figure 2(a), we note that all the states are showing a surge in the number of cases since 13 February, 2021.
Further, the slopes of the growth curve are very high in the second wave compared to the first. The daily
number of cases in Maharashtra, which also leads in the daily as well as cumulative infections, went from
daily cases of 652 on Feb 11, 2021 to about 63,000 in two months (as on April 11, 2021). This exponential
growth is also observed in other states (see figure 2(b)), albeit the number of daily cases is fewer than that in
Maharashtra. The growth curve in the second wave can be further divided into relatively slow and fast growth
Second Wave of COVID-19 in India 3
(a) Daily cases. (b) Daily deaths.
Fig. 1: Comparison of COVID-19 spread in India with five other severely-affected countries. Smoothened data
from OWID are used for the plots.
phases as shown by green and red shadows of figure 2(a,b). In the first region, until the first week of March,
all states except Maharashtra exhibited a slow increase in the number of cases. However, in the second region,
most states show a sudden spurt in the number of infections propelling India’s total daily count to about 0.2
At the outset, the second wave in India looks much more precarious than the first wave and the situation
could quickly get out of control unless stringent measures are taken. The vaccination drive need to be enhanced
to include more vaccine candidates spread over younger population, say up to 25 years of age. However consid-
ering the large population of India and the current spread of the virus mutants into remote locations of India
(discussed later), these strategies may not be sufficient to stem the spread of the virus. The purpose of this
paper is to characterize and model the second wave so as to create awareness about the present grim situation
and also sensitize the public about the need for social distancing. We also use the data to make informed
projections of the epidemic growth based on an epidemiological model.
2 Materials & Method
The present study is based on COVID-19 data from the beginning of the pandemic to April 19, 2021. COVID-
19 data for countries around the globe and Indian regional states are respectively taken from repositories
maintained by ourworldindata (OWID) and OWID compiles data from the European Centre
for Disease Prevention and Control (ECDC).
In order to identify the first and second waves, we study the effective Reproduction number (Rt; Fraser
et al (2004)) as a marker for the decrease or surge in infections. Rtprovides real-time feedback on the spread
of pandemic as the Rt>1 indicates a growth in infection, thus the goal is to implement social interventions
to bring down Rtbelow 1 and close to 0 as much as possible. Time-varying Rtcan be calculated using time
series of the infections and generation time distribution (Cori et al 2013). We use the approach developed by
Thompson et al (2019) for the estimation of effective Reproduction numbers using the R-package EpiEstim.
A MATLAB implementation of this package, developed by Batista (2021), is used for the current study. The
generation time distribution requires serial interval as an input, which indicates the time between the onset
of symptoms of a primary case and the onset of symptoms of secondary cases. Several studies (Du et al 2020;
Knight and Mishra 2020; Rai et al 2020; Nishiura et al 2020) have estimated serial interval for COVID-19. We
4 Rajesh Ranjan et al.
(a) Daily cases. (b) Daily cases (log-scale).
Fig. 2: Comparison of COVID-19 spread in most-impacted 16 states of India in terms of total cumulative
numbers of cases as on April, 19, 2021. Data is smoothened by taking 7-day averaging.
take the mean serial interval as 4.7 days (95% Credible Interval [CI]: 3.7, 6.0) with the standard deviation (SD)
of the serial interval as 2.9 days (95% CI: 1.9, 4.9) based on Nishiura et al (2020).
The available dataset for the second wave has also been used for the predictions using the popular compart-
mental Susceptible-Infected-Recovered (SIR) model (Hethcote 2000). The incidence data between 27 February
2021 and 17 April 2021 have been used for best-fit functions. Note that the pandemic is still in the exponential-
growth phase, as further expanded in the next section. Therefore, there is large uncertainty in the estimation
of the peak of the daily cases, as well as the duration of the second wave. The details about the implementation
of SIR model are given in Ranjan (2020a).
3 Results & Discussion
Figure 3 exhibits the daily confirmed cases of COVID-19 and Rtin India. The effective reproduction number
trend is commensurate with the infection rate as shown in the panel below. The Rtvalue decreased from about
1.37 (95% CI 1.25-1.52) on 17 April 2020 to 1.09 (95% CI 1.07-1.11) on 10 September 2020. Rtwent below
the self-sustaining threshold of 1 for the first time on 23 Sep 2020 and remained there for the next 5 months,
except for a minor flare up on 29 Nov for a couple of days. After this relatively long quiet interval, Rtstarted
rising on 19 February 2021, which is taken as the arrival date for the second wave in India. The Rthas been
increasing since then; as on April 19, 2021, Rthas reached approximately 1.37 (95% CI 1.28-1.47).
In figure 4 we exhibit region-wise variations of the effective reproduction numbers for most-impacted states
as on April 15, 2021, while the most recent value of Rtfor all the states are reported in table 1. As evident in
the figure, Rtcurve crossed the threshold first in Maharashtra and about a week later in other states. However,
very recently there has been a slight decline in the Rtvalue in Maharashtra (and also in Chhatisgarh, which is
highly impacted). Highly populated states like Uttar Pradesh and Bihar, which were some of the least impacted
states during the first wave, are in high growth phase with Rtvalues as 1.75 and 1.84 respectively. This is very
concerning as there is a large rural population in these states where the healthcare system is inadequate for such
a scale of pandemic. With the spread of the virus in these remote and rural areas, an effective administrative
intervention is required to minimize the impact of the pandemic.
The incidence curves for these states show that the current daily cases are already higher than that of the
first peak except for Andhra Pradesh. All the states in India (see Table 1) has Rt>1 indicating the arrival of
the second wave. It appears that mutant viruses are playing some role in the second wave.
Second Wave of COVID-19 in India 5
Fig. 3: Incidence and variation of the effective reproduction number in India.
In table 2, we list the Rtvalues for the most impacted countries on April 19, 2021. Except for UK, where
the pandemic is in decline (Rt'0.90), the other four countries have Rtclose to the threshold value. The
vaccinated population per 100 persons is also listed in the table. Among all countries, the US and UK have the
highest vaccinations per 100 persons; these countries are possibly least susceptible to another wave provided
the vaccines can provide protection against change in virus phenotype due to mutations (Grubaugh et al 2020).
To further characterize the second wave, we employ the following well-known ratios:
Test Positivity Rate (TPR) = Total infections
Total tests
Case Fatality Rate (CFR) = Total deaths
Total infections
These ratios can be defined based on the cumulative data or daily data. While the estimates based on cumulative
data are more smooth, the daily ratios reflects sudden changes more prominently. Therefore, we employ both
definitions to illustrate different aspects, although the daily ratios are obtained after 7-day moving average to
remove large fluctuations due to reporting delays and other uncertainties.
The Test positivity rate (TPR) typically indicates whether the number of tests is enough to contain the
spread of the virus by isolating and quarantining the positive cases. Figure 5(a) illustrates the temporal vari-
ations of these parameters based on cumulative date for both first and second waves. India shows an increase
in cumulative TPR during the acceleration phase of the first wave and it starts declining from August 2020.
Recently, the TPR curve shows an upward trend commensurate with the spurt of cases, with a TPR value of
5.72% as on April 19, 2021. This increase in TPR in the second wave is better reflected in the daily estimates
as shown in figure 5(b). The daily TPR curve shows sharp spike in its value since late March 2021. As on April
19, 2021, the daily TPR value for India is about 15%. WHO recommends that this TPR value should be less
than 5% for at least two weeks so that the transmission can be brought under control. Sudden increase in daily
TPR to a very high value indicates alarming situation and necessitates ramping up of daily testing capacity.
Further, there are large variations among different regions in India as shown in Table 1. Ten states have
alarming daily TPR of more than 20% as highlighted in red. Maharashtra, which shows slight decline in Rt
6 Rajesh Ranjan et al.
Fig. 4: Incidences and variations of the effective reproduction number for the most-impacted states in India.
value recently, has a very high TPR of 26.6% indicating very high transmission. Therefore, the actual number
of infections is likely to be higher than that being reported due to a limited diagnostics capacity. States which
have high Rtvalue as well as high daily TPR (>10%) are also at significant risk.
Next, we report the case fatality rate for India in figure 5 (a) and (b) based on cumulative and daily data
respectively. Both the CFR curves show downward trends with time. The cumulative CFR curve goes from 3.5%
in mid-April 2020 to 1.2% in mid-April 2021 with minor fluctuations. Interestingly, although the second wave
shows the virus to be more infectious, the decline in the CFR curve suggests a silver lining of a relatively less
fatal mutant. However, considering an exponential increase of cases at a very high rate, it is expected that soon
the healthcare facilities will be fully throttled resulting in the unavailability of hospital beds and ventilators to
those in critical needs. This may result in an increase in CFR. Even otherwise, in terms of absolute numbers,
the daily deaths are already higher than the level of peak values in the first wave (see figure 1(b)). Table 1
lists statewise data of cumulative and daily CFRs, with Punjab having the highest value and Kerala the least
among severely-affected states.
Finally we describe the vaccination data in states as listed in Table 1. Among the most-impacted states
with sizeable population, Kerala and Chhatishgarh have highest vaccination per 100 people. Uttar Pradesh and
Bihar, which have very high Rt, have the lowest level of vaccination per capita. This further suggests the need
for strong interventions in the North Indian states while vaccination capacity is increased simultaneously with
prioritized allocations based on social contacts Chen et al (2021).
After characterization of the second wave, we employ mathematical and epidemiological models to under-
stand the dynamics and to provide actionable insights. As discussed earlier (see figure 2), the second wave
Second Wave of COVID-19 in India 7
Table 1: Regional characteristics of the epidemic spread in Indian states and Union Territories as on April 19,
Region CFR (%) TPR (%) CFR (%) TPR (%) RtVac/100 Population
Cumulative Daily (in Cr)
Andhra Pradesh 0.77 6.16 0.45 15.79 1.53 9.1 5.2
Arunachal Pradesh 0.33 4.05 0.00 5.59 2.52 12.0 0.15
Assam 0.51 2.86 0.51 2.09 1.88 4.9 3.4
Bihar 0.54 1.31 0.55 8.98 1.84 4.9 12
Chandigarh 1.21 9.45 0.65 20.36 1.21 12.7 0.118
Chhattisgarh 1.09 8.45 1.26 28.42 1.20 17.4 2.9
Delhi 1.41 5.38 1.01 26.12 1.72 13.4 2
Goa 1.32 11.46 1.81 34.48 1.43 16.5 0.154
Gujarat 1.32 2.58 1.03 7.20 1.48 15.6 6.8
Himachal Pradesh 1.52 5.61 0.77 30.04 1.36 10.9 2.9
Haryana 0.95 5.28 0.48 21.20 1.50 18.2 0.73
Jammu and Kashmir 1.39 2.18 0.40 4.45 1.35 13.0 1.3
Jharkhand 0.90 2.59 1.07 11.27 1.54 7.6 3.7
Karnataka 1.15 4.96 0.92 12.81 1.48 11.4 6.6
Kerala 0.40 8.73 0.15 15.63 1.65 17.2 3.5
Madhya Pradesh 1.10 5.91 0.61 25.32 1.53 9.2 8.2
Maharashtra 1.56 16.19 0.60 26.59 1.10 10.4 12.2
Manipur 1.27 5.01 1.85 5.58 1.93 5.1 0.31
Meghalaya 1.03 3.43 0.91 8.17 1.77 5.4 0.322
Mizoram 0.24 1.79 0.00 7.41 1.77 13.9 0.119
Nagaland 0.75 8.94 0.00 8.55 1.31 6.9 0.215
Odisha 0.54 3.87 0.09 12.29 1.80 11.5 4.4
Puducherry 1.48 6.55 0.88 15.85 1.4 11.2 0.15
Punjab 2.62 4.59 1.80 14.59 1.15 8.4 3
Rajasthan 0.75 5.47 0.44 29.78 1.63 14.5 7.7
Sikkim 2.03 8.01 0.00 - 1.74 25.2 0.066
Tamil Nadu 1.31 4.71 0.40 9.80 1.40 6.3 7.6
Telangana 0.52 3.01 0.35 4.82 1.48 8.5 3.7
Tripura 1.15 5.11 0.00 2.30 1.51 22.8 0.399
Uttar Pradesh 1.14 2.29 0.59 14.07 1.75 4.8 22.5
Uttarakhand 1.50 3.75 1.11 7.12 1.59 14.8 1.1
West Bengal 1.59 6.79 0.45 20.01 1.58 9.3 9.7
Table 2: Characteristics of the epidemic spread in six most-affected countries as on April 19, 2021.
Country CFR (%) TPR (%) CFR (%) TPR (%) RtVac/100 Population
Cumulative Daily (in Cr)
US 1.79 7.54 0.94 3.18 1.01 63.3 33.2
India 1.18 5.72 0.68 15.65 1.37 9.5 139.1
Brazil 2.68 - 4.64 - 1.012 15.9 21.4
France 1.91 7.29 1.49 7.11 0.93 25.1 6.5
Russia 2.25 3.74 4.03 1.43 0.99 11.1 14.6
UK 2.90 3.05 1.0 0.18 0.90 63.5 6.8
started slowly near 11 February 2021 in Maharashtra and Punjab, but the epidemic spread quite rapidly in
other states from early March. Therefore we use two exponential curves to fit the incidence (daily cases) data.
Further, we compare this fit with that of the first wave, starting from a date when the number of infections
was similar to that in the second wave. Figure 6 exhibits these exponential fits, while the coefficients as well as
statistical fitness data are listed in Table 3. The first exponential fit of the second wave has a lower exponent
than the first wave (compare b1and b2in table 3), although it has relatively low value of the co-efficient of
determination (adjusted R2'0.62). However, the exponent of the second fit on more recent data in the second
wave is more than twice of the first wave that explains the rapid growth of the pandemic. Fits for both the
first wave and rapid second wave are statistically significant with adjusted R2greater than 0.95.
We now employ the popular SIR model for an approximate prediction of the progression of the pandemic as
it takes both the incidence and recovery data to estimate the peak and eventual decline. Note that an accurate
estimation with SIR typically requires data beyond exponential growth (such as power-law variation (Asad
8 Rajesh Ranjan et al.
(a) Cumulative parameters. (b) Daily parameters.
Fig. 5: Temporal variations of characterizing parameters- Test positivity rate and Case fatality rate.
Fig. 6: Comparison between first and second waves in India. Data for the first wave was taken from 10 June
2020 to 16 August 2020, and from 11 February 2021 to 19 April 2021 for the second wave.
et al 2020; Verma et al 2020)). Nonetheless, the results presented below should be taken as an approximate
scenario in terms of case count, provided no new interventions are introduced. This will help policymakers to
prepare for the future and plan necessary actions. Figure 7 shows the predictions using the data between 27
February 2021 and 17 April 2021. Based on this model, the peak of the second wave is estimated in the middle
of May with the peak number of daily cases of more than 0.4 million. Furthermore, even with a low daily CFR
of 0.68% as of April 19, 2021, India will have about 3,000 deaths per day at its peak. Note that SIR model
Second Wave of COVID-19 in India 9
Table 3: Exponential regression model for initial growth in first and second waves: y=aexp(bt).
Wave Coefficients Estimate 95% CI SE tStat pValue adjusted R2
First a11.22×1041.12×1041.33×104531 23.0 9.79×1032 0.955
b10.0278 0.0261 0.0296 0.000896 31.0 4.36×1039
Second(1) a21.11×1040.99×1041.23×104578 19.2 1.72×1016 0.616
b20.0194 0.0131 0.0256 0.00305 6.35 1.18×1006
Second(2) a32.65×1032.23×1033.07×103208 12.75 1.13×1015 0.991
b30.0689 0.0662 0.0715 0.00129 53.3 8.77×1039
may not be very accurate because it does not include many factors such as asymtomatic patients, lockdowns,
vaccination, etc. However, accurate modelling of the second wave will require more data, which is not available
at present. We plan to employ universal curve (Sharma et al 2021) and refined epidemiological models (Ranjan
2020b; Shayak et al 2020) for improved prediction of the second wave in India.
Fig. 7: Predictions of the COVID-19 epidemic in India using SIR model. Data between 27 February 2021 to 17
April 2021 are taken for fitting of parameters.
4 Concluding Remarks
The second COVID-19 wave in India, which began on February 11, 2021, presents a grim situation as the
number of cases crossed 0.29 million a day on April 20, 2021. The data suggests that at present the virus is
much more infectious than the first wave, but the number of daily deaths per infection is lower. However, with
an inordinate increase in the number of cases and over-stretched healthcare system, the daily death count may
increase substantially. The effective reproduction number (Rt) is estimated for India, as well as for the Indian
states. At present, almost every state show Rt>1 suggesting that the second wave has spread everywhere
including rural areas which were largely untouched during the first wave. This includes populous states like
Uttar Pradesh, Bihar, and West Bengal: each has Rtvalue greater than 1.58. With rural areas impacted, it
may be necessary to take aggressive lockdown measures to arrest the further spread while sufficient amount of
vaccine becomes available. The SIR model suggests the peak of the epidemic to occur in the middle of May
2021 with approximate daily infections exceeding 0.4 million.
10 Rajesh Ranjan et al.
In summary, using the available infection data, we analyze the second COVID-19 wave in India. We observe
that the epidemic is creating unprecedented havoc in the population. We hope that the appropriate adminis-
trative intervention, aggressive vaccination drive, and people’s participation will help flatten the curve earlier
than the grim forecast of the epidemic.
Disclosure statement
The authors declare that they have no conflict of interest.
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... The emergence of SARS-CoV-2 variants has accelerated the global spread of COVID-19 [1]. In February 2021, the SARS-CoV-2 Delta variant (Phylogenetic Assignment of Named Global Outbreak lineage: B.1.617.2) was first detected in India [2]. Subsequently, major outbreaks seeded by the Delta variants have been reported in various regions [3,4]. ...
Background: As of August 25, 2021, Jiangsu province, China experienced the largest COVID-19 outbreak in eastern China that was seeded by SARS-CoV-2 Delta variants. As one of the key epidemiological parameters characterizing the transmission dynamics of COVID-19, the incubation period plays an essential role in informing the public health measures for epidemic control. The incubation period of COVID-19 could vary by different age, sex, disease severity, and study settings. However, the impacts of these factors on the incubation period of Delta variants remained uninvestigated. Objective: The objective of this study was to characterize the incubation period of Delta variant using detailed contact tracing data. The effects of age, sex, and disease severity on the incubation period were investigated by multivariate regression analysis, and subgroup analysis. Methods: We extracted contact tracing data of 353 laboratory-confirmed cases of SARS-CoV-2 Delta variants' infection in Jiangsu province, China, from July to August 2021. The distribution of incubation period of Delta variants was estimated by using likelihood-based approach with adjustment for interval-censored observations. The effects of age, sex, and disease severity on the incubation period were expiated by using multivariate logistic regression model with interval-censoring accounted. Results: The mean incubation period of Delta variant was estimated at 6.64 days (95% credible interval [CrI]: 6.27-7.00). We found that female cases and cases with severe symptoms had relatively longer mean incubation periods than males and those with non-severe symptoms, respectively. Per one-day increase in the incubation period of Delta variants was associated with a weak decrease in the probability of having severe illness in terms of an adjusted odds ratio (OR) of 0.88 (95% CrI: 0.71-1.07). Conclusions: In this study, the incubation period was found to vary across different levels of sex, age, and disease severity of COVID-19. These findings provided additional information on the incubation period of Delta variants, and highlighted the importance of continuing surveillance and monitoring of the epidemiological characteristics of emerging SARS-CoV-2 variants as they evolve.
... (4) This wave of COVID-19, which began in February 2021, had hit India very hard with the daily cases reaching nearly triple the first peak value as on April 19, 2021, and the country just was slightly away from touching the 5 million daily case mark. (5) The country faced the third wave caused by the new strain Omicron which started in the last week of Dec 2021 had a sharp rise and declined gradually by the end of February 2022. The doubling rate of the Omicron wave was 2 days approximately, but the severity of infection and related mortality was much lesser than the previous 2 waves. ...
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It has been two years since the first case of Coronavirus Disease-2019 (COVID-19) was detected in India in the state of Kerala in March 2020. (1) The Government and the citizens of India have united together to combat the virus since then. India is the largest democracy in the world and the second highest populous country with an estimated 1.36 billion population. The country has witnessed three major waves of the COVID-19 pandemic in the past 3 years, the second being the worse. In the month of June 2022, India has reported a cumulative total of approximately 4.34 crore confirmed cases of COVID-19 and 511,903 deaths. The state of Maharashtra has been the worst effected in all three waves. Presently the recovery rate from COVID-19 in India has crossed 98%. (2)
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The world has not yet completely overcome the fear of the havoc brought by SARS-CoV-2. The virus has undergone several mutations since its initial appearance in China in December 2019. Several variations (i.e., B.1.616.1 (Kappa variant), B.1.617.2 (Delta variant), B.1.617.3, and BA.2.75 (Omicron variant)) have emerged throughout the pandemic, altering the virus’s capacity to spread, risk profile, and even symptoms. Humanity faces a serious threat as long as the virus keeps adapting and changing its fundamental function to evade the immune system. The Delta variant has two escape alterations, E484Q and L452R, as well as other mutations; the most notable of these is P681R, which is expected to boost infectivity, whereas the Omicron has about 60 mutations with certain deletions and insertions. The Delta variant is 40–60% more contagious in comparison to the Alpha variant. Additionally, the AY.1 lineage, also known as the “Delta plus” variant, surfaced as a result of a mutation in the Delta variant, which was one of the causes of the life-threatening second wave of coronavirus disease 2019 (COVID-19). Nevertheless, the recent Omicron variants represent a reminder that the COVID-19 epidemic is far from ending. The wave has sparked a fervor of investigation on why the variant initially appeared to propagate so much more rapidly than the other three variants of concerns (VOCs), whether it is more threatening in those other ways, and how its type of mutations, which induce minor changes in its proteins, can wreck trouble. This review sheds light on the pathogenicity, mutations, treatments, and impact on the vaccine efficacy of the Delta and Omicron variants of SARS-CoV-2.
Music sharing trends have been shown to change during times of socio-economic crises. Studies have also shown that music can act as a social surrogate, helping to significantly reduce loneliness by acting as an empathetic friend. We explored these phenomena through a novel study of online music sharing during the Covid-19 pandemic in India. We collected tweets from the popular social media platform Twitter during India’s first and second wave of the pandemic (n = 1,364). We examined the different ways in which music was able to accomplish the role of a social surrogate via analyzing tweet text using Natural Language Processing techniques. Additionally, we analyzed the emotional connotations of the music shared through the acoustic features and lyrical content and compared the results between pandemic and pre-pandemic times. It was observed that the role of music shifted to a more community focused function rather than tending to a more self-serving utility. Results demonstrated that people shared music during the Covid-19 pandemic which had lower valence and shared songs with topics that reflected turbulent times such as Hardship and Exclusion when compared to songs shared during pre-Covid times. The results are further discussed in the context of individualistic versus collectivistic cultures.KeywordsMusical emotionsOnline music sharingCovid pandemicSocial surrogacyLyrics
Understanding first and second wave of covid19 Indian data along with its few selective states, we have realized a transition between two Sigmoid pattern with twice larger growth parameter and maximum values of cumulative data. As a result of those transition, time duration of second wave shrink to half of that first wave with four times larger peak values. Realizing first and second wave Sigmoid pattern due to covid19 virus and its mutated variant—\(\delta \) virus respectively, third wave was mapped by another Sigmoid pattern with three times larger growth parameter than that of first wave. After understanding the crossing zone among first, second and third wave curves due to covid19, \(\delta \) and omicron respectively, a hidden Sigmoid pattern due to mutated \(\delta +\) virus is identified in between \(\delta \) and omicron. It is really interesting that entire covid19 data of India can be easily (offcourse grossly) understood by simple algebraic expressions of Sigmoid function and we can identify 4 Sigmoid patterns due to covid19 virus and its 3 dominant variants.KeywordsCovid19OmicronSigmoid function\(3\textrm{rd}\) Wave
One of the groups of pollutants in the atmosphere is Polycyclic Aromatic Hydrocarbon (PAHs). PAHs compounds are currently extensively studied due to their harm to human and ecosystem health. Biomonitoring using plants was currently introduced as an alternative to monitoring pollutants through active air sampling to minimize the bias effect of the short-term active air sampling. Biomonitoring is also considered an effective technique to be applied in developing countries for the reason that it can avoid the high cost of instrumental monitoring. Previous studies from the last decade were analyzed to gain insight into current practices, progress, and challenges. In addition, content analysis was employed to systematically characterize and classify the existing biomonitoring application. Therefore, the emphasis in this review will be placed on the use of bioaccumulation and biomarker responses in the plants as monitoring tools for PAH concentrations for the ecosystem and its limitations.KeywordsPolycyclic aromatic hydrocarbons (PAHs)BiomonitoringAir pollutionPersistent organic pollutants (POPs)
Criteria pollutants such as CO, SO2, NO2, PM10, and PM2.5 were monitored in the Kullu valley, a famous tourist destination in Himachal Pradesh, during the lockdown period of the 2nd wave of COVID-19 pandemic. The Pre-Lockdown Period (PLD) from 1 January to 6 May 2021 is taken here as a reference period, while the period from 7 May to 30 June 2021 is the Lockdown Period (LD). The present study was carried out to assess the anthropogenic impact on gaseous and particulate pollutants. Sulfur dioxide (1 h time average) and carbon monoxide (8 h average) were monitored using Thermo Fisher Scientific Gas Analyzers which uses pulse fluorescence technology and gas filter technology, respectively. While NO2 (24 h average) was analysed by Jacob and Hochessier method, and PM10 (24 h average) and PM2.5 (24 h average) were monitored using gravimetric method. The concentrations of pollutants were further analyzed incorporating mean ± standard error. HYSPLIT model was used to determine back trajectory of long-range transport of air pollutants. The results revealed average concentration of CO 0.59 ± 0.03 ppm in the PLD period, while it decreased to 0.35 ± 0.01 ppm in the LD period indicating how CO is largely influenced by vehicular emissions in the valley. Also, particulate matter such as PM10 and PM2.5 showed a decrease in lockdown period by 65.31% and 52.5%, respectively. The overall air quality was improved in the Kullu valley during lockdown period of the second wave of the COVID-19 pandemic as tourist-related activity and anthropogenic activities were restricted. The MERRA-2 data during the pre-lockdown and the lockdown period was analyzed in ArcGIS 10.8, which indicates almost similar result.
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The deadly transmission of the coronavirus forced all countries to implement lockdowns to restrict the transmission of this highly infectious disease. As a result of these lockdowns and restrictions, many urban centers have seen a positive impact on air quality with a significant reduction in air pollution. Therefore, in this study, the impact of COVID-19 lockdown vis-a-vis meteorological parameters on the ambient air quality of Srinagar city was examined. In this regard, we have evaluated the temporal variation of six different key air pollutants (PM10, PM2.5, SO2, NO2, O3, and NH3) along with meteorological parameters (relative humidity, rainfall, temperature, wind speed, and wind direction). The duration of the study was divided into three periods: Before Lockdown(BLD), Lockdown (LD), and Partial Lockdown(PLD). Daily average data for all the parameters was accessed from one of the real-time continuous monitoring stations of the central pollution control board (CPCB) at Rajbagh Srinagar. Some air pollutants have decreased, according to the results, while others have increased. The air quality index (AQI) decreases overall by 6.15 percent compared to before lockdown, and it never exceeds the "moderate" category. The AQI was in the following order for both lockdown and pre-lockdown periods: satisfactory > moderate > good. However, for partial lockdown, it was moderate > satisfactory > good. It was observed that the maximum decrease was seen in the concentration of NO2, NH3 with 75.11% and 69.18%. A modest decrease was observed in PM10 at 3.8%. While SO2 and O3 had an upward trend of 85.82% and 48.74%, The NO2 to SO2 ratio reveals that the emissions of NO2 have substantially decreased due to the complete restriction of transport systems. From principal component analysis for all three study periods, PM10 and PM2.5 were combined into a single component, inferring their shared behavior and source of origin. SO2 and O3 demonstrated identical behavior during the lockdown and partial lockdown periods of study. According to the findings of the study, it is beneficial for the government, environmentalists, and policymakers to impose rigorous lockdown measures, particularly during extreme air pollution events, in order to reduce the damage caused by automotive and industrial emissions.
Even after a year since COVID-19 pandemic originated, there are no concrete signs of slowing down of the virus any day now. The pandemic had an adverse effect on the healthcare system across the globe. It was assumed earlier that the hot summer weather will bring boon by decreasing the caseloads and easing down the stress on health care system worldwide. The purpose of this research is to assess the implications of this pandemic on the healthcare system in India and forecast overall active cases of COVID-19 in India using time series analysis. The data set used in the study spans to the 3 hottest months, from May, 2021 to July, 2021, thereby narrowing down the analysis of the pandemic to extreme summers of India. The popular time series Auto-Regressive Integrated Moving Average (ARIMA) model was then extensively manoeuvred to observe the trend and predict results. It was observed that ARIMA (0, 2, 0) model was pretty pertinent in forecasting the active cases during summers in India. KeywordsARIMATime series analysisMachine learningCOVID 19Health CareIndia
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The SARS-CoV-2 lineage B.1.1.7, designated a Variant of Concern 202012/01 (VOC) by Public Health England1, originated in the UK in late Summer to early Autumn 20202. Whole genome SARS-CoV-2 sequence data collected from community-based diagnostic testing shows an unprecedentedly rapid expansion of the B.1.1.7 lineage during Autumn 2020, suggesting a selective advantage. We find that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S-gene target failures (SGTF) in community-based diagnostic PCR testing. Analysis of trends in SGTF and non-SGTF case numbers in local areas across England shows that the VOC has higher transmissibility than non-VOC lineages, even if the VOC has a different latent period or generation time. The SGTF data indicate a transient shift in the age composition of reported cases, with a larger share of under 20 year olds among reported VOC than non-VOC cases. Time-varying reproduction numbers for the VOC and cocirculating lineages were estimated using SGTF and genomic data. The best supported models did not indicate a substantial difference in VOC transmissibility among different age groups. There is a consensus among all analyses that the VOC has a substantial transmission advantage with a 50% to 100% higher reproduction number.
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UK variant transmission Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has the capacity to generate variants with major genomic changes. The UK variant B.1.1.7 (also known as VOC 202012/01) has many mutations that alter virus attachment and entry into human cells. Using a variety of statistical and dynamic modeling approaches, Davies et al. characterized the spread of the B.1.1.7 variant in the United Kingdom. The authors found that the variant is 43 to 90% more transmissible than the predecessor lineage but saw no clear evidence for a change in disease severity, although enhanced transmission will lead to higher incidence and more hospital admissions. Large resurgences of the virus are likely to occur after the easing of control measures, and it may be necessary to greatly accelerate vaccine roll-out to control the epidemic. Science , this issue p. eabg3055
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We construct a universal epidemic curve for COVID-19 using the epidemic curves of eight nations that have reached saturation for the first phase and then fit an eight-degree polynomial that passes through the universal curve. We take India’s epidemic curve up to January 1, 2021 and match it with the universal curve by minimizing square-root error between the model prediction and actual value. The constructed curve has been used to forecast epidemic evolution up to February 25, 2021. The predictions of our model and those of supermodel for India (Agrawal et al. in Indian J Med Res, 2020; Vidyasagar et al. in, 2020) are reasonably close to each other considering the uncertainties in data fitting.
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Two new SARS-CoV-2 lineages with the N501Y mutation in the receptor-binding domain of the spike protein spread rapidly in the United Kingdom. We estimated that the earlier 501Y lineage without amino acid deletion Δ69/Δ70, circulating mainly between early September and mid-November, was 10% (6-13%) more transmissible than the 501N lineage, and the 501Y lineage with amino acid deletion Δ69/Δ70, circulating since late September, was 75% (70-80%) more transmissible than the 501N lineage. Two new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineages carrying the amino acid substitution N501Y in the receptor-binding domain (RBD) of the spike protein have spread rapidly in the United Kingdom (UK) during late autumn 2020. Assessing the public health threat of these lineages (e.g. the potential for them to increase herd immunity thresholds if they displace other circulating SARS-CoV-2 strains) requires quantification of their comparative transmissibility. Here we adopted our previous epidemiological framework for relative fitness inference of co-circulating pathogen strains, which has been applied on influenza viruses [1] and SARS-CoV-2 D614G strains [2], to characterise the comparative transmissibility of the 501Y lineages.
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Background: The effective reproduction number R e (t) is a critical measure of epidemic potential. R e (t) can be calculated in near real time using an incidence time series and the generation time distribution: the time between infection events in an infector-infectee pair. In calculating R e (t), the generation time distribution is often approximated by the serial interval distribution: the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid-19, rendering such an approximation improper in some contexts. Methods: We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of R e (t) for covid-19 in the Greater Toronto Area of Canada using: negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution. Results: We estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of R e (t) due to larger mean, while negative-permitting serial interval distribution caused underestimation of R e (t) due to larger variance. Implications: Approximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating R e (t) may result in over or underestimation of transmission potential, respectively.
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Background On 11th March 2020, the World Health Organization declared COVID-19 as Pandemic. The estimation of transmission dynamics in the initial days of the outbreak of disease is crucial to control its spread in a new area. The serial interval is one of the significant epidemiological measures that determine the spread of infectious disease. It is the time interval between the onset of symptoms in the primary and secondary case. Objective The present study aimed at the qualitative and quantitative synthesis of the currently available evidence for the serial interval of COVID-19. Methodology Data on serial intervals were used from 11 studies by implementing inclusion and exclusion criteria after initial screening. A meta-analysis was performed to estimate the pooled estimate of the serial interval. The heterogeneity and bias in the included studies were tested by various statistical measures and tests, including I2 statistic, Cochran's Q test, Egger's test, and Beggs's test. Result The pooled estimate for the serial interval was 5.40 (5.19, 5.61) and 5.19 (4.37, 6.02) by the fixed and random effects model, respectively. The heterogeneity between the studies was found to be 89.9% by I2 statistic. There is no potential bias introduced in the meta-analysis due to small study effects. Conclusion The present review provides sufficient evidence for the estimate of serial interval of COVID-19, which can help in understanding the epidemiology and transmission of the disease. The serial interval can be useful for policy makers including contact tracing and monitoring community transmission of COVID-19. Keywords COVID-19, Serial interval, Systematic review, Meta-analysis, Epidemiology
Background: The much-heralded second wave of coronavirus disease (COVID-19) has arrived in Italy. Right now, one of the main questions about COVID-19 is whether the second wave is less severe and deadly than the first wave. In order to answer this challenging question, we decided to evaluate the chest X-ray (CXR) severity of COVID-19 pneumonia, the mechanical ventilation (MV) use, the patient outcome, and certain clinical/laboratory data during the second wave and compare them with those of the first wave. Methods: During the two COVID-19 waves two independent groups of hospitalised patients were selected. The first group consisted of the first 100 COVID-19 patients admitted to our hospital during the first wave. The second group consisted of another 100 consecutive COVID-19 patients admitted to our hospital during the second wave. We enlisted only Caucasian male patients over the age of fifty for whom the final outcome was available. For each patient, the CXR severity of COVID-19 pneumonia, the MV use, the patient outcome, comorbidities, corticosteroid use, and C-reactive protein (CRP) levels were considered. Nonparametric statistical tests were used to compare the data obtained from the two waves. Results: The CXR severity of COVID-19 pneumonia, the in-hospital mortality, and CRP levels were significantly higher in the first wave than in the second wave (p ≤ .041). Although not statistically significant, the frequency of MV use was higher in the first wave. Conclusions: This preliminary investigation seems to confirm that the COVID-19 second wave is less severe and deadly than the first wave.
A mathematical analysis of patterns for the evolution of COVID-19 cases is key to the development of reliable and robust predictive models potentially leading to efficient and effective governance against COVID-19. Towards this objective, we study and analyze the temporal growth pattern of COVID-19 infection and death counts in various states of India. Our analysis up to August 4, 2020, shows that several states (namely Maharashtra, Tamil Nadu, West Bengal) have reached t2 power-law growth, while Gujarat and Madhya Pradesh exhibit linear growth. Delhi has reached t phase and may flatten in the coming days. However, some states have deviated from the universal pattern of the epidemic curve. Uttar Pradesh and Rajasthan show a gradual rise in the power-law regime, which is not the usual trend. Also, Bihar, Karnataka, and Kerala are exhibiting a second wave. In addition, we report that initially, the death counts show similar behavior as the infection counts. Later, however, the death growth rate declines as compared to the infection growth due to better handling of critical cases and increased immunity of the population. These observations indicate that except Delhi, most of the Indian states are far from flattening their epidemic curves.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is a major concern in coronavirus disease 2019 (COVID-19) prevention and economic reopening. However, rigorous determination of SARS-COV-2 infectivity is very difficult owing to its continuous evolution with over ten thousand single nucleotide polymorphisms (SNP) variants in many subtypes. We employ an algebraic topology-based machine learning model to quantitatively evaluate the binding free energy changes of SARS-CoV-2 spike glycoprotein (S protein) and host angiotensin-converting enzyme 2 (ACE2) receptor following mutations. We reveal that the SARS-CoV-2 virus becomes more infectious. Three out of six SARS-CoV-2 sub- types have become slightly more infectious, while other three subtypes have significantly strengthened their infectivity. We also find that SARS-CoV-2 is slightly more infectious than SARS-CoV according to computed S protein-ACE2 binding free energy changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain (RBD), we show that most likely future mutations will make SARS-CoV-2 more infectious. Combining sequence alignment, probability analysis, and binding free energy calculation, we predict that a few residues on the receptor-binding motif (RBM), i.e., 452, 489, 500, 501, and 505, have high chances to mutate into significantly more infectious COVID-19 strains.
Korber et al. (2020) found that a SARS-CoV-2 variant in the spike protein, D614G, rapidly became dominant around the world. While clinical and in vitro data suggest that D614G changes the virus phenotype, the impact of the mutation on transmission, disease, and vaccine and therapeutic development are largely unknown.