Open AccessSpatial Area Model for Covid-19 in Java Based on R-Shiny Web Framework
Author(s) -
Rokhana Dwi Bekti,
Yudi Setyawan,
Enik Laksminiasih
Publication year - 2021
Publication title -
jurnal matematika, statistika dan komputasi/jurnal matematika statistik dan komputasi
Language(s) - English
Resource type - Journals
eISSN - 2614-8811
pISSN - 1858-1382
DOI - 10.20956/j.v17i3.11743
Subject(s) - java , covid-19 , inflation (cosmology) , lag , population , computer science , variable (mathematics) , pandemic , econometrics , spatial analysis , statistics , demography , economics , mathematics , medicine , disease , sociology , computer network , mathematical analysis , physics , pathology , theoretical physics , infectious disease (medical specialty) , programming language
The Covid-19 in Indonesia has had an impact on almost all lives, especially at economic, social, education, and health.. Efforts to prevent and reduce the number of cases are still ongoing. Likewise, research on the causes of the emergence of the Covid-19 pandemic outbreak, drugs, vaccines, and the factors that influence it are still being carried out. This study analyzes the effect of Covid-19 on inflation and the effect of population density on Covid-19 in Java. The method used is area spatial modeling. To make it easier for researchers to analyze data, this study also developed a web application based on the R shiny framework. This application has displayed valid output from the results of its use and is in accordance with existing theories, and is able to make it easier for users to carry out Covid-19 analysis in Java using the area spatial model method. The estimation results of the Spatial Durbin Model (SDM) show that the variable that has a significant effect on inflation is the inflation lag in the model with cumulative positive cases (α = 10%). This shows that the inflation of a province tends to be influenced by other neighboring provinces. Meanwhile, population density is also significant for Covid-19 positive cases (α = 5%).