Time series analysis using different forecast methods and case fatality rate for COVID-19 pandemic
Regional Science Policy & Practice
; n/a(n/a), 2022.
Article
in English
| Wiley | ID: covidwho-1868692
ABSTRACT
This study presented 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 like 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.
Full text:
Available
Collection:
Databases of international organizations
Database:
Wiley
Type of study:
Experimental Studies
Language:
English
Journal:
Regional Science Policy & Practice
Year:
2022
Document Type:
Article
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