GIS-based approaches on the accessibility of referral hospital using network analysis and the spatial distribution model of the spreading case of COVID-19 in Jakarta, Indonesia

Abstract

Background The outbreak of the novel coronavirus (COVID-19) has rapidly spread, causing million confirmed cases, thousands of deaths, and economic losses. The number of cases of COVID-19 in Jakarta is the largest in Indonesia. Furthermore, Jakarta is the capital city of Indonesia which has the densest population in the country. There is need for geospatial analysis to evaluate the demand in contrast to the capacity of Referral Hospitals and to model the spreading case of Covid-19 in order to support and organize an effective health service. Methods We used the data from local government publicity for COVID-19 as trusted available sources. By using the verifiable data by observation from the local government, we estimated the spatial pattern of distribution of cases to estimate the growing cases. We performed service area and Origin-Destination (OD) Cost Matrix in support to existing referral hospital, and to create Standard Deviational Ellipse (SDE) model to determine the spatial distribution of COVID-19. Results We identified more than 12.4 million people (86.7%) based on distance-based service area, live in the well served area of the referral hospital. A total 2637 positive-infected cases were identified and highly concentrated in West Jakarta (1096 cases). The results of OD cost matrix in a range of 10 km show a total 908 unassigned cases from 24 patient’s centroid which was highly concentrated in West Jakarta. Conclusions Our results indicate the needs for additional referral hospitals specializing in the treatment of COVID-19 and spatial illustration map of the growth of COVID-19′ case in support to the implementation of social distancing in Jakarta.

Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model

“…There is no universally accepted superior relation between medical care facilities and the range of roads to reach it. According to Silalahi et al [ 48 ], more attention should be paid to the transport network for easy access to PVCs. In this study, distances of 100 m (very desirable), 150 m (desirable) and 200 m (somewhat desirable) from the main roads were defined as one of the sets of criteria for selecting PVCs when applying buffer analysis in ArcGIS.…”

Measuring COVID-19 Vaccination Coverage to Support Healthcare Equity Decision-Making in Urban Areas

“…Then, we computed the vaccination capacity-to-weighted population ratio R j within the CAs using Eq. 2 below following previous studies (11,24,41,42).…”

“…There is no universally accepted superior relation between medical care facilities and the range of roads to reach it. According to Silalahi et al (41), more attention should be paid to the transport network for easy access to PVCs. In this study, distances of 100 m (very desirable), 150 m (desirable), and 200 m (somewhat desirable) from the main roads were defined as one of the sets of criteria for selecting PVCs when applying buffer analysis in QGIS.…”

ANALISIS PENENTUAN TITIK LOKASI VAKSINASI COVID-19 MENGGUNAKAN SIG (Studi Kasus di SWK Arcamanik, Kota Bandung)

“…Setelah menentukan Titik Kandidat Vaksinasi, selanjutnya adalah menentukan demand point wilayah yang didasari oleh peta kepadatan (Li et al;2018, Silalahi et al, 2020, dan titik pemerintahan yang ada, yang menjadi pertimbangan dalam penentuannya. Untuk lebih jelas dapat dilihat pada Gambar 3.…”

“…Cakupan ini didasarkan pada waktu tempuh yang diperlukan untuk mencapai suatu obyek melalui jaringan transportasi (Anonim, 2018;Yura et al, 2019;Kamel and Geraghty, 2020;Silalahi et al, 2020).…”