Generalized Spatio-Temporal Autoregressive (GSTAR) modeling on coronavirus disease (COVID-19) cases in DKI Jakarta

Coronavirus Disease (COVID-19) is a new disease that first hit the world in 2020. This disease is thought to have originated in Wuhan, China [1]. WHO has designated COVID-19 as a pandemic which has successfully infected more than 190 countries in the world. DKI Jakarta is the capital city of Indones...

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Bibliographic Details
Main Authors: Widyaningsih, Y., Ayu, N.
Format: Conference Proceeding
Language:English
Subjects:
Autocorrelation functions
Coronaviruses
COVID-19
Parameter estimation
Time dependence
Viral diseases
Weighting
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Summary:Coronavirus Disease (COVID-19) is a new disease that first hit the world in 2020. This disease is thought to have originated in Wuhan, China [1]. WHO has designated COVID-19 as a pandemic which has successfully infected more than 190 countries in the world. DKI Jakarta is the capital city of Indonesia which is also one of the provinces with the highest number of positive confirmed cases of COVID-19 until the end of July 2020. This study aims to model the rate of COVID-19 cases in 15 sub-districts of DKI Jakarta with the highest intensity. The area of 15 sub-districts are chosen with the highest intensity to be the study area where more than 63.43 % of confirmed COVID-19 cases are in this area. The sub-districts are Gambir, Menteng, Sawah Besar, Kemayoran, Taman Sari, Senen, Tanah Abang, Johar Baru, Tambora, Grogol Petamburan, Cempaka Putih, Pademangan, Setia Budi, Matraman, and Palmerah. The rate of COVID-19 cases in this area is then analyzed using a GSTAR model. This model is one of the models in a stochastic time series that considers spatial component or location and time [2]. The binary weight matrix, uniform weight matrix, and distance weight matrix in this study were formed as a spatial dependency matrix among locations or called the W weight matrix. The results of Space Time Autocorrelation Function (STACF) and Space Time Partial Autocorrelation Function (STPACF) services for all spatial weighting matrices obtained the same model, that is GSTAR (3,1). Estimation of parameters of the GSTAR model (3,1) is carried out for each weighting matrix. The best GSTAR (3,1) model is based on a distance weighted matrix, with the smallest RMSE.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0212710