Time Series Analysis of COVID-19 Data to Study the Effect of Lockdown and Unlock in India

Singh, Saswat; Chowdhury, Chandreyee; Panja, Ayan Kumar; Neogy, Sarmistha

doi:10.1007/s40031-021-00585-7

Cited by 14 publications

(3 citation statements)

References 16 publications

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“…The estimated prediction interval (-18504.64, 29380.65) makes it clear that the likelihood of hospitalization due to COVID will be lower than what we have predicted, but there is still a lot of uncertainty. (1) The model proved to be the most effective model for short-term forecasting in the time series and the outcome in the very near months, in contrast to other models, which call for a significant number of previously calculated timestamp samples. Future improvements to the models' predictive accuracy will include the development of ensembles of the ones that have been presented, which combine the best aspects of each model to lower overall error, as well as the use of multivariate time series modeling to take other factors into account that may be directly or indirectly related to the pandemic's spread.…”

Section: Discussionmentioning

confidence: 94%

Time Series Analysis of COVID-19 Data- A study from Northern India

Semwal

Bahuguna

Sharma

et al. 2022

Indian J Community Health

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The continuing new Coronavirus (COVID-19) pandemic has caused millions of infections and thousands of fatalities globally. Identification of potential infection cases and the rate of virus propagation is crucial for early healthcare service planning to prevent fatalities. The research community is faced with the analytical and difficult real-world task of accurately predicting the spread of COVID-19. We obtained COVID-19 temporal data from District Surveillance Officer IDSP, Dehradun cum District Nodal Officer- Covid-19 under CMO, Department of Medical Health and Family Welfare, Government of Uttarakhand State, India, for the period, March 17, 2020, to May 6, 2022, and applied single exponential method forecasting model to estimate the COVID-19 outbreak's future course. The root relative squared error, root mean square error, mean absolute percentage error, and mean absolute error were used to assess the model's effectiveness. According to our prediction, 5438 people are subjected to hospitalization by September 2022, assuming that COVID cases will increase in the future and take on a lethal variety, as was the case with the second wave. The outcomes of the forecasting can be utilized by the government to devise strategies to stop the virus's spread.

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Section: Discussionmentioning

confidence: 94%

Time Series Analysis of COVID-19 Data- A study from Northern India

Semwal

Bahuguna

Sharma

et al. 2022

Indian J Community Health

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“…Another prediction model used in India is ARIMA, which has value in predicting cases and shows the effect of unlocking after lockdown. The ARIMA model relies on the number of positive cases, the number of performed tests per day and the average positive percentage[ 41 ]. In the United Kingdom, weighted interval scoring was used for the prediction model, which used the data from the linear progression of 7-day cases[ 42 ].…”

Section: Radiology Departments During Sars-cov-2 Pandemic and The Imp...mentioning

confidence: 99%

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Tomov,

Chervenkov,

Miteva

et al. 2023

World J Clin Cases

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Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

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“…-Singh, Chowdhury, Panja, and Neogy [11] -Ladiray, Palate, Mazzi, and Proietti [14] examined the application of 'the X11 family,' i.e., methods based on moving average like the X11, X11-ARIMA, X12-ARIMA, and X-13, to daily data and showed an actual example of electricity usage. -Ollech [3] examined the procedure for adjusting changes due to various periodicity and moving holidays and showed an actual example of adjusting the fluctuations in German currency in circulation by STL.…”

Section: What Is Already Known On This Topicmentioning

confidence: 99%

Assessment of the spread of COVID-19 in seven countries using a seasonal adjustment method

Arita¹

2022

SJI

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Early identification of the trends in the number of newly confirmed COVID-19 cases is essential. Weekly periodicity and irregular fluctuations affect the time series of the number of newly confirmed COVID-19 cases. However, a 7-day moving average and a rolling 7-day total delay identifying the trend changes because of assigning a low weight to the most recent day. Additionally, they cannot adjust for the fluctuations due to moving holidays. For the first time, this study shows that X-13ARIMA-SEATS (X-13), one of the seasonal adjustment methods, can apply to the analysis of the changes in the number of newly confirmed COVID-19 cases with examples of seven countries: Germany, Indonesia, Iran, Russia, the United Kingdom, the United States, and Japan. This study successfully extracts trend components, calendar-induced components (weekly periodicity and fluctuations due to moving holidays), and irregular components from the time series of seven countries by X-13. Thus, compared to a 7-day moving average and a rolling 7-day total, the method in this study can facilitate more rapid and accurate assessment and strategic responses to the spread of COVID-19. Furthermore, the method could be effective for analyzing other daily data with weekly periodicity.

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Time Series Analysis of COVID-19 Data to Study the Effect of Lockdown and Unlock in India

Cited by 14 publications

References 16 publications

Time Series Analysis of COVID-19 Data- A study from Northern India

Time Series Analysis of COVID-19 Data- A study from Northern India

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Assessment of the spread of COVID-19 in seven countries using a seasonal adjustment method

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