Hotspot Analysis of COVID-19 using Spatial Statistic in Selangor Malaysia

Abstract

The study investigates the spatial distribution of COVID-19 cases in Selangor, Malaysia, utilizing geospatial and geostatistical techniques to identify and analyze hotspots. Focusing on data from 54 districts between April and August 2021, the research employs Global Moran’s I and Getis-Ord Gi* models to detect spatial autocorrelation and clustering patterns. The study also incorporates additional parameters, including new cases, cumulative cases, deaths, clusters, and population density, to comprehensively analyze COVID-19 hotspots. The results reveal a significant clustering of COVID-19 cases, with specific districts like Petaling, Hulu Langat, and Klang identified as high-risk hotspots. The findings of this research emphasize the critical role of spatial analysis in understanding the spread of infectious diseases like COVID-19. By identifying and mapping out high-risk districts, this study provides valuable insights that can inform public health strategies and optimize resource allocation in response to the pandemic. Identifying hotspots within Selangor underscores the necessity for targeted interventions and deploying healthcare resources to areas most affected by the virus. Ultimately, this study contributes to a deeper understanding of the spatial dynamics of COVID-19 in Selangor, offering a framework for future research and public health planning in the context of epidemic management.