Predictive models of COVID-19 in India: A rapid review

Kotwal, Atul; Yadav, Arun Kumar; Yadav, Jyoti; Kotwal, Jyoti; Khune, Sudhir

doi:10.1016/j.mjafi.2020.06.001

Cited by 49 publications

(44 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include leptospirosis and its relationship with rainfall and temperature [ 5 ] and the relationship of suicide cases with changes in national alcohol policies [ 30 ] among others. Time series modelling of infectious disease specially COVID-19 has been reported by several researchers [ 4 , 7 , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this paper, we have employed Auto Regressive Integrated Moving Average (ARIMA) model to predict the incidence and spread of the COVID-19 in India, Russia, Brazil, Spain and the US as the five most badly hit countries [ 1 ]. As compared to other econometric models ARIMA models have been used with success in the prediction of several diseases [ [2] , [3] , [4] , [5] , [6] , [7] ].…”

mentioning

confidence: 99%

See 1 more Smart Citation

ARIMA modelling & forecasting of COVID-19 in top five affected countries

Sahai

Rath

Sood

et al. 2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

126

View full text Add to dashboard Cite

Background and aims In a little over six months, the Corona virus epidemic has affected over ten million and killed over half a million people worldwide as on June 30, 2020. With no vaccine in sight, the spread of the virus is likely to continue unabated. This article aims to analyze the time series data for top five countries affected by the COVID-19 for forecasting the spread of the epidemic. Material and methods Daily time series data from 15th February to June 30, 2020 of total infected cases from the top five countries namely US, Brazil, India, Russia and Spain were collected from the online database. ARIMA model specifications were estimated using Hannan and Rissanen algorithm. Out of sample forecast for the next 77 days was computed using the ARIMA models. Results Forecast for the first 18 days of July was compared with the actual data and the forecast accuracy was using MAD and MAPE were found within acceptable agreement. The graphic plots of forecast data suggest that While Russia and Spain have reached the inflexion point in the spread of epidemic, the US, Brazil and India are still experiencing an exponential curve. Conclusion Our analysis shows that India and Brazil will hit 1.38 million and 2.47 million mark while the US will reach the 4.29 million mark by 31st July. With no effective cure available at the moment, this forecast will help the governments to be better prepared to combat the epidemic by ramping up their healthcare facilities.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

ARIMA modelling & forecasting of COVID-19 in top five affected countries

Sahai

Rath

Sood

et al. 2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

126

View full text Add to dashboard Cite

show abstract

“…Agent based models have provided useful insights, at the level of full cities, into mitigation methods and the effectiveness of non-pharmaceutical interventions [12]. Related references which model COVID-19 in India are [10,[13][14][15][16][17][18][19][20][21][22][23][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. These models are very largely compartmental models of varying degrees of complexity [37].…”

Section: Introductionmentioning

confidence: 99%

The INDSCI-SIM model for COVID-19 in India

Hazra

Pujari

Shekatkar

et al. 2021

Preprint

View full text Add to dashboard Cite

Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. The INDSCI-SIM model is a 9-component, age-stratified, contact-structured compartmental model for COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions, an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient across the country. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05 - 0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 40% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

show abstract

“…A review by Kotwal et al ( 2020 ) revealed that due to the multiple evolving factors and related uncertainties, the short term projection of Covid-19 in India could be the preferred option as long term projections would involve possible higher inaccuracies. Consequently, for the purpose of monitoring the trend (India) and modifying the government and health care policies according to the current trend of Covid-19, it becomes imperative to employ a trend test, which would have good detection power for short length data.…”

Section: Introductionmentioning

confidence: 99%

Sen's Innovative Method for Trend Analysis of Epidemic: A Case Study of Covid-19 Pandemic in India

Dauji

2021

Trans Indian Natl. Acad. Eng.

View full text Add to dashboard Cite

Analysis of trend of epidemiological data helps to appreciate the progression of an epidemic and to develop monitoring and control strategies by the government agencies. Sen's Innovative Method suggests a graphical analysis, which can overcome many limitations of data such as short length, non-Gaussian nature, skewness or serial correlation. In this article, this method is applied for the first time on epidemiological data. For the case study, Covid-19 or SARS-CoV-2 data from India were employed. The results show that Sen's Innovative Method is capable of indicating the shift in epidemiological trend quite efficiently, before it is reflected in the time series or moving average plots. The graphical analysis worked particularly well in comparing the trends of monthly data. It is concluded that this method would be especially suitable for monitoring the epidemiological trend by breaking up the data into smaller segments, as was illustrated in the study.

show abstract

Predictive models of COVID-19 in India: A rapid review

Cited by 49 publications

References 17 publications

ARIMA modelling & forecasting of COVID-19 in top five affected countries

ARIMA modelling & forecasting of COVID-19 in top five affected countries

The INDSCI-SIM model for COVID-19 in India

Sen's Innovative Method for Trend Analysis of Epidemic: A Case Study of Covid-19 Pandemic in India

Contact Info

Product

Resources

About