The Correlation of Socioeconomic and Mobility Factors on Covid-19 Morbidity in Semarang City

Abstract

Semarang City as the capital of Central Java Province contributes a fairly high number of cases of the COVID-19 virus. This study used a sample of the number of COVID-19 case data in 16 sub-districts in Semarang City. The sampling technique used is purposive sampling with a period of 3 months, namely from October until December 2020. The research aims to find out the impact of social economy and community mobility on the growth of COVID-19 in Semarang City. There is a linkage of spatial factors between sub-districts in the growth of COVID-19 numbers. Spatial influence can show the relationship of influence between sub-districts with each other. In addition, appropriate quantitative approaches are used to obtain a complete view of the expected results through mapping with Geoda software through regression testing analyzed through spatial econometric methods. The process of data collection is carried out by a literature review. This study showed that the model correlated with a high R2 value of 0.7780. All variables show significant numbers in the model. Through policies at the Semarang City level through coordination between sub-districts to achieve prevention goals in the growth rate of COVID-19 numbers of each sub-district. The findings from this study will inform that the effort to a policy of handling COVID-19 must also be carried out continuously and the regulation of large-scale social restrictions (PSBB) in Semarang city is needed.

Strategi Penguatan Usaha Mikro, Kecil, dan Menengah (UMKM) di DKI Jakarta Pasca Pandemi Covid-19

“…Another way is through the Spatial Error Model (SEM) where it is assumed that in the error model of a region with another region there is a spatial correlation. (Purwoko et al, 2022) developed a model that assumes that the dependent variables of a region are also related to other region-independent variables. Model specifications and estimation procedures for spatial econometric models of panel data have been widely developed by experts including (Anselin et al, 2006), (Oliver & Gujarati, 1993).…”