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Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia

Author

Listed:
  • Kurubaran Ganasegeran

    (Clinical Research Center, Seberang Jaya Hospital, Ministry of Health Malaysia, Seberang Perai 13700, Malaysia)

  • Mohd Fadzly Amar Jamil

    (Clinical Research Center, Seberang Jaya Hospital, Ministry of Health Malaysia, Seberang Perai 13700, Malaysia
    Institute for Clinical Research, National Institutes of Health, Ministry of Health Malaysia, Setia Alam 40170, Malaysia)

  • Maheshwara Rao Appannan

    (Crisis Preparedness and Response Center, Disease Control Division, Ministry of Health Malaysia, Putrajaya 62590, Malaysia)

  • Alan Swee Hock Ch’ng

    (Clinical Research Center, Seberang Jaya Hospital, Ministry of Health Malaysia, Seberang Perai 13700, Malaysia
    Medical Department, Seberang Jaya Hospital, Ministry of Health Malaysia, Seberang Perai 13700, Malaysia)

  • Irene Looi

    (Clinical Research Center, Seberang Jaya Hospital, Ministry of Health Malaysia, Seberang Perai 13700, Malaysia
    Medical Department, Seberang Jaya Hospital, Ministry of Health Malaysia, Seberang Perai 13700, Malaysia)

  • Kalaiarasu M. Peariasamy

    (Institute for Clinical Research, National Institutes of Health, Ministry of Health Malaysia, Setia Alam 40170, Malaysia)

Abstract

As COVID-19 dispersion occurs at different levels of gradients across geographies, the application of spatiotemporal science via computational methods can provide valuable insights to direct available resources and targeted interventions for transmission control. This ecological-correlation study evaluates the spatial dispersion of COVID-19 and its temporal relationships with crucial demographic and socioeconomic determinants in Malaysia, utilizing secondary data sources from public domains. By aggregating 51,476 real-time active COVID-19 case-data between 22 January 2021 and 4 February 2021 to district-level administrative units, the incidence, global and local Moran indexes were calculated. Spatial autoregressive models (SAR) complemented with geographical weighted regression (GWR) analyses were executed to determine potential demographic and socioeconomic indicators for COVID-19 spread in Malaysia. Highest active case counts were based in the Central, Southern and parts of East Malaysia regions of Malaysia. Countrywide global Moran index was 0.431 ( p = 0.001), indicated a positive spatial autocorrelation of high standards within districts. The local Moran index identified spatial clusters of the main high–high patterns in the Central and Southern regions, and the main low–low clusters in the East Coast and East Malaysia regions. The GWR model, the best fit model, affirmed that COVID-19 spread in Malaysia was likely to be caused by population density (β coefficient weights = 0.269), followed by average household income per capita (β coefficient weights = 0.254) and GINI coefficient (β coefficient weights = 0.207). The current study concluded that the spread of COVID-19 was concentrated mostly in the Central and Southern regions of Malaysia. Population’s average household income per capita, GINI coefficient and population density were important indicators likely to cause the spread amongst communities.

Suggested Citation

  • Kurubaran Ganasegeran & Mohd Fadzly Amar Jamil & Maheshwara Rao Appannan & Alan Swee Hock Ch’ng & Irene Looi & Kalaiarasu M. Peariasamy, 2022. "Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia," IJERPH, MDPI, vol. 19(4), pages 1-13, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2082-:d:748209
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    References listed on IDEAS

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    1. Balvinder Singh Gill & Vivek Jason Jayaraj & Sarbhan Singh & Sumarni Mohd Ghazali & Yoon Ling Cheong & Nuur Hafizah Md Iderus & Bala Murali Sundram & Tahir Bin Aris & Hishamshah Mohd Ibrahim & Boon Ha, 2020. "Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia," IJERPH, MDPI, vol. 17(15), pages 1-13, July.
    2. Pamela Herd & Amelia Karraker & Elliot Friedman, 2012. "The Social Patterns of a Biological Risk Factor for Disease: Race, Gender, Socioeconomic Position, and C-reactive Protein," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 67(4), pages 503-513.
    3. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    4. Carlos Eduardo Raymundo & Marcella Cini Oliveira & Tatiana de Araujo Eleuterio & Suzana Rosa André & Marcele Gonçalves da Silva & Eny Regina da Silva Queiroz & Roberto de Andrade Medronho, 2021. "Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
    5. Tonglin Zhang & Ge Lin, 2008. "Identification of local clusters for count data: a model-based Moran's I test," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(3), pages 293-306.
    6. Qiong Jia & Yue Guo & Guanlin Wang & Stuart J. Barnes, 2020. "Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework," IJERPH, MDPI, vol. 17(17), pages 1-21, August.
    7. Kurubaran Ganasegeran & Mohd Fadzly Amar Jamil & Alan Swee Hock Ch’ng & Irene Looi & Kalaiarasu M. Peariasamy, 2021. "Influence of Population Density for COVID-19 Spread in Malaysia: An Ecological Study," IJERPH, MDPI, vol. 18(18), pages 1-12, September.
    8. Meng Xu & Joel E Cohen, 2019. "Analyzing and interpreting spatial and temporal variability of the United States county population distributions using Taylor's law," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-25, December.
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    2. Ruonan Wang & Xiaolong Li & Zengyun Hu & Wenjun Jing & Yu Zhao, 2022. "Spatial Heterogeneity and Its Influencing Factors of Syphilis in Ningxia, Northwest China, from 2004 to 2017: A Spatial Analysis," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
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    4. Krzysztof Rząsa & Mateusz Ciski, 2022. "Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression," IJERPH, MDPI, vol. 19(19), pages 1-26, September.

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