Geo‐clusters and socio‐demographic profiles at village‐level associated with COVID‐19 incidence in the metropolitan city of Jakarta: An ecological study

Abstract

The Special Capital Region of Jakarta is the epicentre of the transmission of COVID‐19 in Indonesia. However, much remains unknown about the spatial and temporal patterns of COVID‐19 incidence and related socio‐demographic factors explaining the variations of COVID‐19 incidence at local level. COVID‐19 cases at the village level of Jakarta from March 2020 to June 2021 were analyzed from the local public COVID‐19 dashboard. Global and local spatial clustering of COVID‐19 incidence was examined using the Moran's I and local Moran analysis. Socio‐demographic profiles of identified hotspots were elaborated. The association between village characteristics and COVID‐19 incidence was evaluated. The COVID‐19 incidence was significantly clustered based on the geographical village level (Moran's I = 0.174; p = .002). Seventeen COVID‐19 high‐risk clusters were found and dynamically shifted over the study period. The proportion of people aged 20–49 (incidence rate ratio [IRR] = 1.016; 95% confidence interval [CI]: 1.012–1.019), proportion of elderly (≥50 years) (IRR = 1.045; 95% CI = 1.041–1.050), number of households (IRR = 1.196; 95% CI = 1.193–1.200), access to metered water for washing, and the main occupation of the residents were village level socio‐demographic factors associated with the risk of COVID‐19. Targeted public health responses such as restriction, improved testing and contact tracing, and improved access to health services for those vulnerable populations are essential in areas with high‐risk COVID‐19.

Spatial Analysis to Identify Hotspots of Covid-19 Incidence in Jambi Municipality Indonesia

“…Many studies on the spatial aspects of the Covid-19 case have been carried out around the world (Ballesteros et al, 2021;Edriani et al, 2021;Hu et al, 2021;Lak et al, 2021;Mohammad Ebrahimi et al, 2021;Pang et al, 2021;Purwanto et al, 2021;Raymundo et al, 2021;Saavedra et al, 2021). Not much has been done to study the spatial aspects of the Indonesian Covid-19 case (Dhewantara et al, 2022;Heldayani et al, 2021;Iriany et al, 2021;Ratnasari & Dewi, 2021). Spatial distribution analysis using the Getis Ord G* method is a technique that is often and widely used, one of which is the detection of hotspots or disease clusters (ESRI, 2021;Hohl et al, 2020).…”

Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling

“…In Germany, most research on the spatial distribution of COVID-19 is restricted to the relatively coarse level of counties [16], masking important variation at the small-area, municipality, or even neighbourhood level, hampering productive outbreak detection and management, despite numerous studies' having shown the value of microgeographic data on COVID-19 [17][18][19].…”