Investigating the dynamics of COVID-19 pandemic in India under lockdown

Pai, Chintamani; Bhaskar, Ankush; Rawoot, Vaibhav

doi:10.1016/j.chaos.2020.109988

Cited by 82 publications

(61 citation statements)

References 10 publications

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“…Researchers are involved to create theories and forecasting models [ 99 , 100 ]. It is certain that during the first phase of lockdown COVID-19 infected cases were under controlled [ 101 ]. Because of the population and population density lockdown was needed [ 102 ].…”

Section: Discussionmentioning

confidence: 99%

How India is dealing with COVID-19 pandemic

Ghosh

Nundy

Mallick

2020

Sensors International

181

115

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India, which has the second-largest population in the world is suffering severely from COVID-19 disease. By May 18th, India investigated ∼1 lakh (0.1million) infected cases from COVID-19, and as of 11th July the cases equalled 8 lakhs. Social distancing and lockdown rules were employed in India, which however had an additional impact on the economy, human living, and environment. Where a negative impact was observed for the economy and human life, the environment got a positive one. How India dealt and can potentially deal with these three factors during and post COVID-19 situation has been discussed here.

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Section: Discussionmentioning

confidence: 99%

How India is dealing with COVID-19 pandemic

Ghosh

Nundy

Mallick

2020

Sensors International

181

115

View full text Add to dashboard Cite

show abstract

“…Then, to counter the fast contagion of COVID-19, a 3-week nationwide lockdown was imposed in India on March 24, which was expended till June 30. The lockdown measures mitigate the impact of COVID-19 on Indian health infrastructure and it also helped in curbing the rate of the spread of this infectious disease among people (Pai et al, 2020;Anderson et al, 2020). Because of the prohibition of industrial activities and mass transportation, anthropogenic emissions showed a tremendous reduction.…”

Section: Introductionmentioning

confidence: 99%

Impact of reduced anthropogenic emissions during COVID-19 on air quality in India

Zhang¹,

Katiyar²,

Zhu³

et al. 2020

Preprint

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Abstract. To mitigate the impacts of the pandemic of coronavirus disease 2019 (COVID-19), the Indian government implemented lockdown measures on March 24, 2020, which prohibit unnecessary anthropogenic activities and thus leading to a significant reduction in emissions. To investigate the impacts of this lockdown measures on air quality in India, we used the Community Multi-Scale Air Quality (CMAQ) model to estimate the changes of key air pollutants. From pre-lockdown to lockdown periods, improved air quality is observed in India, indicated by the lower key pollutant levels such as PM2.5 (−26 %), maximum daily 8-h average ozone (MDA8 O3) (−11 %), NO2 (−50 %), and SO2 (−14 %). In addition, changes in these pollutants show distinct spatial variations with the more important decrease in northern and western India. During the lockdown, our results illustrate that such emission reductions play a positive role in the improvement of air quality. Significant reductions of PM2.5 and its major components are observed especially for secondary inorganic aerosols with the decreasing rates up to 92 %, 57 %, and 79 % for nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), respectively. On average, the MDA8 O3 also decreases 15 % during the lockdown period although it increases sparsely in some urban locations, which is mainly due to the lower NOx and VOCs emissions. More aggressive and localized emissions control strategies should be implemented in India to mitigate air pollutions in the future.

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“…The model predicts peak of cumulative cases around 43,000 in mid-May. However, 7-21% increase in peak value of cases was predicted for post lockdown period, re ecting relaxation in control strategies [20]. The evolution of COVID-19 in topmost affected states of India was done using SISO model [23].…”

Section: Discussionmentioning

confidence: 99%

Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

Bhandari

Tak

Gupta

et al. 2020

Preprint

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Abstract The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centerpiece in evidence based disease management. Numerous approaches that use mathematical modeling have been used to predict the outcome of the pandemic, including data driven models, empirical and hybrid models. This study was aimed at prediction COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Retrieving real time data from the Johns Hopkins dashboard from 11 Mar 2020 to 25 Jun 2020 (N = 107 time points) to fit the model. The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths respectively with minimum Akaike Informaton Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 Jun 2020 to 05 Jul 2020 showed a trend toward continuous increment. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) of ARIMA(1,3,2) model was 21137 and 166330 respectively. Similarly, PredRMSE and BaseRMSE of ARIMA(3,3,1) model was 668.7 and 5431 respectively. We propose that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimization and evidence based decision making for a subsequent state of affairs.

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Investigating the dynamics of COVID-19 pandemic in India under lockdown

Cited by 82 publications

References 10 publications

How India is dealing with COVID-19 pandemic

How India is dealing with COVID-19 pandemic

Impact of reduced anthropogenic emissions during COVID-19 on air quality in India

Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

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