Time Series Analysis of COVID-19 Data- A study from Northern India
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https://doi.org/10.47203/IJCH.2022.v34i02.012Keywords:
COVID-19, Forecasting, Hospitalization, Single exponential smoothingDimensions Badge
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Copyright (c) 2022 Indian Journal of Community Health
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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.Abstract
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Singh S, Chowdhury C, Panja AK, Neogy S. Time Series Analysis of COVID-19 Data to Study the Effect of Lockdown and Unlock in India. J Inst Eng Ser B. 2021;102(6):1275–81. Available from: https://link.springer.com/article/10.1007/s40031-021-00585-7 The impact of COVID-19 on global health goals. [Accessed on 25.06.2022]. Available from: https://www.who.int/news-room/spotlight/the-impact-of-covid-19-on-global-health-goals Countries where Coronavirus has spread - Worldometer. [Accessed on 25.06.2022]. Available from: https://www.worldometers.info/ coronavirus/countries-where-coronavirus-has-spread/ Bodapati S, Bandarupally H, Trupthi M. COVID-19 Time Series Forecasting of Daily Cases, Deaths Caused and Recovered Cases using Long Short Term Memory Networks. 2020 IEEE 5th Int Conf Comput Commun Autom ICCCA 2020. 2020 Oct 30;525–30. [Accessed on 25.06.2022]. Available from: doi: 10.1109/ICCCA49541.2020.9250863. Satrio C.B.A, Darmawan W, Nadia BU, Hanafiah N. Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science. 2021;179:524–32. Murray CJ. Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months. medRxiv. 2020;2020.03.27.20043752.: doi.org/10.1101/2020.03.27 .20043752 Kumar N, Susan S. COVID-19 Pandemic Prediction using Time Series Forecasting Models. 2020 11th Int Conf Comput Commun Netw Technol ICCCNT 2020. 2020 Jul 22 [Accessed on 25.06.2022]; Available from: doi: 10.1109/ICCCNT49239.2020.9225319. Abotaleb M, Makarovskikh T, Rojas F, Herrera LJ, Pomare H. System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models. Eng Proc 2021;5(1):46. [Accessed on 25.06.2022]. Available from: https: https://doi.org/10.3390/engproc2021005046 . Chyon FA, Suman MN, Fahim MR, Ahmmed MS. Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning. Journal of Virological Methods. 2022;301:114433.
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