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The Effect of Calendar Variation and Sun-Earth Distance on Daily Case Forecasting for COVID-19 Using the Symbolic Time Series Prognosis Model
International Journal of Sustainable Development and Planning ; 18(3):891-896, 2023.
Article in English | Scopus | ID: covidwho-2323764
ABSTRACT
The spread of COVID-19, apart from being influenced by specific pathogenic factors, is also influenced by population structure and mobility. This study was conducted to develop a forecasting model for the increase in COVID-19 cases due to variations in the holiday calendar and the sun's distance to the earth. This study uses secondary data taken from the East Java Provincial Health Office in collaboration with the East Java Province COVID-19 Task Force. This study uses two independent variables the Calendar Variation in 2020 and 2021 (X1);The Sun-Earth Distance (X2) and dependent variable (Y) is the Daily Case of COVID-19 in East Java Province from March 2020 to June 2021. The process of data analysis is carried out by analyzing time series data and building a forecasting model with a symbolic time series prognosis approach. The analysis process is carried out using Heuristic Lab 3.3 software. Based on the results of this study, it can be concluded that variations in the holiday calendar and the sun-earth distance have a strong influence on daily cases of COVID-19. The symbolic time series prognosis model obtained has an accuracy rate of up to 90.04% on training data and 82.14% on testing data. © 2023 WITPress. All rights reserved.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: International Journal of Sustainable Development and Planning Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: International Journal of Sustainable Development and Planning Year: 2023 Document Type: Article