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Afrina Andriani br Sebayang
Department of Mathematics, Institut Teknologi Bandung, Bandung, 40132, West Java, Indonesia
Social Sciences

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Journal : Communication in Biomathematical Sciences

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Defining Causality in Covid-19 and Google Search Trends in Java, Indonesia Cases: A Retrospective Analysis Afrina Andriani br Sebayang; Enrico Antonius; Elisabeth Victoria Pravitama; Jonathan Irianto; Shannen Widijanto; Muhammad Syamsuddin
Communication in Biomathematical Sciences Vol. 4 No. 2 (2021)
Publisher : Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2021.4.2.1

Abstract

The Coronavirus disease 2019 (Covid-19) has led all countries around the world to the unpredicted situation. It is such a crucial to investigate novel approaches in predicting the future behaviour of the outbreak. In this paper, Google trend analysis will be employed to analyse the seek pattern of Covid-19 cases. The first method to investigate the seek information behaviour related to Covid-19 outbreak is using lag-correlation between two time series data per regional data. The second method is used to encounter the cause-effect relation between time series data. We apply statistical methods for causal inference in epidemics. Our focus is on predicting the causal-effect relationship between information-seeking patterns and Google search in the Covid-19 pandemic. We propose the using of Granger Causality method to analyse the causal relation between incidence data and Google Trend Data.
Co-Authors Elisabeth Victoria Pravitama Enrico Antonius Jonathan Irianto Muhammad Syamsuddin Shannen Widijanto