MODELING CASE FATALITY RATE OF COVID-19 IN INDONESIA USING TIME SERIES SEMIPARAMETRIC REGRESSION BASED ON LOCAL POLYNOMIAL ESTIMATOR

Vita Fibriyani, Nur Chamidah, Toha Saifudin

Research output: Contribution to journalArticlepeer-review

Abstract

At the beginning of 2020, the world was shocked by the COVID-19 which causes acute and contagious respiratory problems. Several countries in the world have declared the COVID-19 outbreak a pandemic, including Indonesia. The number of patients who have infected by the COVID-19 and died due to this virus continues to increase every day. In this study, we model the Case Fatality Rate (CFR) of COVID-19 in one city in Indonesia by using semiparametric regression model approach based on time series local polynomial estimator where the local polynomial estimator is used to accommodate fluctuations of daily COVID-19 death rate data. The daily data on CFR of COVID-19 during a certain period in one city were used as the basis for the analysis. In this approach, we combine parametric regression and nonparametric regression components to identify factors that influence on the CFR. The parametric component represents factors that influence on the CFR with a known regression function, while the nonparametric component represents factors that influence on the COVID-19 death rate with an unknown regression function. In this study, it is known that the parametric component is the COVID-19 death rate one day earlier, while the nonparametric component is a time series. Result shows that the time series semiparametric regression approach based on local polynomial estimator can model the CFR of COVID-19 well, and in the future the obtained model can be used to predict the CFR of COVID-19 in Indonesia, especially in Pasuruan city, for supporting one of the (Sustainable Development Goals) SDGs namely control the pandemic. Also, the obtained model can be used to predict the death rate caused by COVID-19 or similar disease outbreaks.

Original languageEnglish
Article number9
JournalCommunications in Mathematical Biology and Neuroscience
Volume2024
DOIs
Publication statusPublished - 2024

Keywords

  • COVID-19, time series
  • fatality rate
  • local polynomial estimator
  • semiparametric regression

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