Exploring The Spatial Relationship Between Covid-19 and Poverty in Indonesia: A Moran’s I Approach

Alex Bagas Koro; Uzma Eliyanti; Lianawati

doi:10.30595/jhepr.v3i1.245

Authors

Alex Bagas Koro Faculty of Public Health Khon Kaen University, Thailand
Uzma Eliyanti Faculty of Pharmacy, Universitas Muhammadiyah Purwokerto
Lianawati Faculty of Public Health Khon Kaen University, Thailand

DOI:

https://doi.org/10.30595/jhepr.v3i1.245

Keywords:

COVID-19, Moran’s I, Poverty, Spatial Analysis

Abstract

Background: COVID-19 has emerged as a pervasive infectious disease globally. Between 2020 and 2022, Indonesia experienced over 6.4 million COVID-19 cases and more than 157,000 confirmed deaths. Aside from vaccination efforts, the spread of COVID-19 is influenced by socioeconomic conditions, particularly poverty. Impoverished communities are especially vulnerable to COVID-19 due to limited access to healthcare services and high population density in living areas. This research aims to explore the spatial relationship between poverty and COVID-19 in Indonesia during 2021-2022.

Methods: This study employs an ecological design, focusing on the spatial relationship between COVID-19 incidence rates and poverty levels across Indonesian provinces. The analysis is conducted using Moran’s I, a spatial autocorrelation statistic, to determine the extent of clustering of COVID-19 cases about poverty.

Results: In 2021, East Nusa Tenggara had the lowest COVID-19 incidence rate at 517.56 per 100,000 population, while DKI Jakarta recorded the highest. During 2022, Bali remained the province with the highest incidence rate at 318.99 per 100,000 population, whereas Aceh had the lowest at only 1.43 per 100,000 population in 2022. The average poverty rate in Indonesia in 2021 was 10.76%, which slightly decreased to 10.24% in 2022. The difference between the minimum and maximum poverty percentages in both years was insignificant. Bivariate analysis shows that Moran's I value was -0.101 in 2021 (significant) and 0.042 in 2022 (not significant).

Conclusion: The spatial distribution of COVID-19 cases across Indonesian provinces in 2021 and 2022 exhibited significant variation influenced by multiple factors, such as vaccination programs and the percentage of poverty. However, this study has limitations due to secondary data that may not capture the real condition of COVID-19 and poverty dynamics.

Keywords: COVID-19, Moran’s I, Poverty, Spatial Analysis.

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