ArticleRegional science policy & practice2022
Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid-19 Pandemic.
Article in Regional science policy & practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model.Scientific reports · 2025Article
- Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets.JMIR infodemiology · 2023Article
- Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid-19 Pandemic.Regional science policy & practice · 2022Article
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Authors and funding
4 authors.
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Abstract
This study presents forecasting methods using time series analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an artificial intelligence method and different statistical methodologies that can help predict the future of Covid-19 cases. Various forecasting methods in time series analysis such as ARIMA, Holt's trend, naive, simple exponential smoothing, TBATS, and MAPE are extended for the study. It also involved the case fatality rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid-19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.
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Registered trials
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