A Spatial Model of the Social Vulnerability Index for Vaccine COVID-19 in Java, Indonesia

Main Article Content

Martya Rahmaniati Makful
Tris Eryando
Dian Sidik Arsyad


The COVID-19 vaccine coverage in Indonesia remains low, with uneven distribution across Java, while COVID-19 cases continue to pose a public health concern. This study seeks to develop a spatial model using the Social Vulnerability Index (SVI) approach to identify the spatial pattern of COVID-19 vaccination and the factors influencing it in Java. The study adopts an ecological design with a spatial approach, encompassing 118 districts/cities. The dataset used in this research focuses on the coverage of COVID-19 vaccination for the second dose, spanning from March 15, 2021, to January 11, 2022. Spatial statistical techniques such as spatial autocorrelation and Geographically Weighted Regression were employed to analyze the data. The findings reveal that the Human Development Index, unemployment rate, and housing conditions significantly impact the spatial distribution of COVID-19 vaccine coverage, indicating the presence of spatial interaction among regions. Socioeconomic factors emerged as key variables influencing the study outcomes. Given that enhancing the community's economy requires time, interventions tailored to the prevailing conditions are necessary. Therefore, interventions to increase COVID-19 vaccine coverage should prioritize health promotion efforts, particularly in areas with low socioeconomic conditions.

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How to Cite
Makful, M. R. ., Risma, Eryando, T., Arsyad, D. S., & Argianto. (2023). A Spatial Model of the Social Vulnerability Index for Vaccine COVID-19 in Java, Indonesia. Journal of Population and Social Studies [JPSS], 31, 745–761. Retrieved from https://so03.tci-thaijo.org/index.php/jpss/article/view/264176
Research Articles
Author Biography

Martya Rahmaniati Makful, Department of Biostatistics and Population Studies, Faculty of Public Health, Universitas Indonesia, Indonesia

Corresponding author


• Al Rifai, M., Jain, V., Khan, S. U., Nasir, K., Zhu, D., Vasudeva, R., Lavie, C. J., Dodani, S., Petersen, L. A., & Virani, S. S. (2021). Social vulnerability and COVID-19: An analysis of CDC data. Progress in Cardiovascular Diseases, 73, 91–93. https://doi.org/10.1016/j.pcad.2021.09.006

• Anselin, L., & Getis, A. (1992). Spatial statistical analysis and geographic information systems. Annals of Regional Science, 26(1), 19–33. https://doi.org/10.1007/BF01581478

• Apparicio, P., Abdelmajid, M., Riva, M., & Shearmur, R. (2008). Comparing alternative approaches to measuring the geographical accessibility of urban health services: Distance types and aggregation-error issues. International Journal of Health Geographics, 7(1), Article 7. https://doi.org/10.1186/1476-072X-7-7

• Arumsari, W., Desty, R. T., & Kusumo, W. E. G. (2021). Gambaran penerimaan vaksin COVID-19 di Kota Semarang [Overview of receiving COVID-19 vaccine in Semarang City]. Indonesian Journal of Health Community, 2(1), 35–45. https://doi.org/10.31331/ijheco.v2i1.1682

• Babu, G. R., Khetrapal, S., John, D. A., Deepa, R., & Narayan, K. M. V. (2021). Pandemic preparedness and response to COVID-19 in South Asian countries. International Journal of Infectious Diseases, 104, 169–174. https://doi.org/10.1016/j.ijid.2020.12.048

• Banerjee, D. (2020). The impact of Covid‐19 pandemic on elderly mental health. International Journal of Geriatric Psychiatry, 35(12), 1466–1467. https://doi.org/10.1002/gps.5320

• Barry, V., Dasgupta, S., Weller, D. L., Kriss, J. L., Cadwell, B. L., Rose, C., Pingali, C., Musial, T., Sharpe, J. D., Flores, S. A., Greenlund, K. J., Patel, A., Stewart, A., Qualters, J. R., Harris, L., Barbour, K. E., & Black, C. L. (2021). Patterns in COVID-19 vaccination coverage, by social vulnerability and urbanicity—United States, December 14, 2020–May 1, 2021. MMWR. Morbidity and Mortality Weekly Report, 70(22), 818–824. https://doi.org/10.15585/mmwr.mm7022e1

• Birhane, M., Bressler, S., Chang, G., Clark, T., Dorough, L., Fischer, M., Watkins, L. F., Goldstein, J. M., Kugeler, K., Langley, G., Lecy, K., Martin, S., Medalla, F., Mitruka, K., Nolen, L., Sadigh, K., Spratling, R., Thompson, G., & Trujillo, A. (2021). COVID-19 vaccine breakthrough infections reported to CDC — United States, January 1–April 30, 2021. MMWR. Morbidity and Mortality Weekly Report, 70(21), 792–793. https://doi.org/10.15585/mmwr.mm7021e3

• Buyong, T. (2007). Spatial data analysis for geographic information science. Penerbit Universiti Teknologi Malaysia.

• Cahyadi, M. N., Handayani, H. H., Warmadewanthi, I., Rokhmana, C. A., Sulistiawan, S. S., Waloedjo, C. S., Raharjo, A. B., Endroyono, Atok, M., Navisa, S. C., Wulansari, M., & Jin, S. (2022). Spatiotemporal analysis for COVID-19 Delta variant using GIS-Based Air Parameter and Spatial Modeling. International Journal of Environmental Research and Public Health, 19(3), Article 1614. https://doi.org/10.3390/ijerph19031614

• Centers for Disease Control and Prevention (CDC), & Agency for Toxic Substances and Disease Registry (ATSDR). (2022, November 16). CDC/ATSDR Social Vulnerability Index. Centers for Disease Control and Prevention. https://www.atsdr.cdc.gov/placeandhealth/svi/

• Cordes, J., & Castro, M. C. (2020). Spatial analysis of COVID-19 clusters and contextual factors in New York City. Spatial and Spatio-Temporal Epidemiology, 34, Article 100355. https://doi.org/10.1016/j.sste.2020.100355

• de Souza, C. D. F., Machado, M. F., & do Carmo, R. F. (2020). Human development, social vulnerability and COVID-19 in Brazil: A study of the social determinants of health. Infectious Diseases of Poverty, 9(1), Article 124. https://doi.org/10.1186/s40249-020-00743-x

• Dyer, O. (2021). Covid-19: Indonesia becomes Asia’s new pandemic epicentre as delta variant spreads. BMJ, 374, Article n1815. https://doi.org/10.1136/bmj.n1815

• Endrich, M. M., Blank, P. R., & Szucs, T. D. (2009). Influenza vaccination uptake and socioeconomic determinants in 11 European countries. Vaccine, 27(30), 4018–4024. https://doi.org/10.1016/j.vaccine.2009.04.029

• Eryando, T., Sipahutar, T., & Rahardiantoro, S. (2020). The risk distribution of COVID-19 in Indonesia: A spatial analysis. Asia Pacific Journal of Public Health, 32(8), 450–452. https://doi.org/10.1177/1010539520962940

• Faisal, K., Alshammari, S., Alotaibi, R., Alhothali, A., Bamasag, O., Alghanmi, N., & Bin Yamin, M. (2022). Spatial analysis of COVID-19 vaccine centers distribution: A case study of the city of Jeddah, Saudi Arabia. International Journal of Environmental Research and Public Health, 19(6), Article 3526. https://doi.org/10.3390/ijerph19063526

• Fauzi, M. A., & Paiman, N. (2020). COVID-19 pandemic in Southeast Asia: Intervention and mitigation efforts. Asian Education and Development Studies, 10(2), 176–184. https://doi.org/10.1108/AEDS-04-2020-0064

• Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. (2011). A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management, 8(1), Article 3. https://doi.org/10.2202/1547-7355.1792

• Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., Mellan, T. A., Coupland, H., Whittaker, C., Zhu, H., Berah, T., Eaton, J. W., Monod, M., Imperial College COVID-19 Response Team, Perez-Guzman, P. N., Schmit, N., Cilloni, L., Ainslie, K. E. C., Baguelin, M., Boonyasiri, A., Boyd, O., … Bhatt, S. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257–261. https://doi.org/10.1038/s41586-020-2405-7

• Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Wiley.

• Gangopadhyaya, A., & Garrett, B. (2020, March 1). Unemployment, health insurance, and the COVID-19 recession. Robert Wood Johnson Foundation. https://www.rwjf.org/en/insights/our-research/2020/03/unemployment-health-insurance-and-the-covid-19-recession.html

• Hanifa, S., Puspitasari, D., Ramadhan, C., & Herastuti, K. O. (2022). COVID-19 vaccine prioritization based on district classification in Yogyakarta Province, Indonesia. Geospatial Health, 17(s1), Article 1010. https://doi.org/10.4081/gh.2022.1010

• Harapan, H., Wagner, A. L., Yufika, A., Winardi, W., Anwar, S., Gan, A. K., Setiawan, A. M., Rajamoorthy, Y., Sofyan, H., & Mudatsir, M. (2020). Acceptance of a COVID-19 vaccine in Southeast Asia: A cross-sectional study in Indonesia. Frontiers in Public Health, 8, Article 381. https://doi.org/10.3389/fpubh.2020.00381

• Kang, D., Choi, H., Kim, J.-H., & Choi, J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases, 94, 96–102. https://doi.org/10.1016/j.ijid.2020.03.076

• Koenig, W. D. (1999). Spatial autocorrelation of ecological phenomena. Trends in Ecology and Evolution, 14(1), 22–26. https://doi.org/10.1016/S0169-5347(98)01533-X

• Lee, J., & Huang, Y. (2022). COVID-19 vaccine hesitancy: The role of socioeconomic factors and spatial effects. Vaccines, 10(3), Article 352. https://doi.org/10.3390/vaccines10030352

• Legendre, P., & Fortin, M. J. (1989). Spatial pattern and ecological analysis. Vegetatio, 80(2), 107–138. https://doi.org/10.1007/BF00048036

• Leung, K., Wu, J. T., Liu, D., & Leung, G. M. (2020). First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: A modelling impact assessment. The Lancet, 395(10233), 1382–1393. https://doi.org/10.1016/S0140-6736(20)30746-7

• MacDonald, N. E. (2015). Vaccine hesitancy: Definition, scope and determinants. Vaccine, 33(34), 4161–4164. https://doi.org/10.1016/j.vaccine.2015.04.036

• Ministry of Health. (2022). COVID-19 Vaccination. https://covid19.go.id/vaksin-covid19

• Mofleh, D., Almohamad, M., Osaghae, I., Bempah, S., Zhang, Q., Tortolero, G., Ebeidat, A., Ramphul, R., & Sharma, S. V. (2022). Spatial patterns of COVID-19 vaccination coverage by social vulnerability index and designated COVID-19 vaccine sites in Texas. Vaccines, 10(4), Article 574. https://doi.org/10.3390/vaccines10040574

• Mollalo, A., & Tatar, M. (2021). Spatial modeling of COVID-19 vaccine hesitancy in the United States. International Journal of Environmental Research and Public Health, 18(18), Article 9488. https://doi.org/10.3390/ijerph18189488

• Murthy, B. P., Sterrett, N., Weller, D., Zell, E., Reynolds, L., Toblin, R. L., Murthy, N., Kriss, J., Rose, C., Cadwell, B., Wang, A., Ritchey, M. D., Gibbs-Scharf, L., Qualters, J. R., Shaw, L., Brookmeyer, K. A., Clayton, H., Eke, P., Adams, L., … Harris, L. Q. (2021). Disparities in COVID-19 vaccination coverage between urban and rural counties—United States, December 14, 2020–April 10, 2021. MMWR. Morbidity and Mortality Weekly Report, 70(20), 759–764. https://doi.org/10.15585/mmwr.mm7020e3

• Nichter, M. (1995). Vaccinations in the third world: A consideration of community demand. Social Science & Medicine, 41(5), 617–632. https://doi.org/10.1016/0277-9536(95)00034-5

• Park, S.-Y., Kwak, J.-M., Seo, E.-W., & Lee, K.-S. (2016). Spatial analysis of the regional variation of hypertensive disease mortality and its socio-economic correlates in South Korea. Geospatial Health, 11(2), Article 420. https://doi.org/10.4081/gh.2016.420

• Putra, I. G. N. E., Rahmaniati, M., Sipahutar, T., & Eryando, T. (2022). Modeling the prevalence of tuberculosis in Java, Indonesia: An ecological study using geographically weighted regression. Journal of Population and Social Studies, 30, 741–763. https://doi.org/10.25133/JPSSv302022.041

• Rini, A. S., & Sugiharti, L. (2017). Faktor-faktor penentu kemiskinan di Indonesia: Analisis rumah tangga [Determining factors of poverty in Indonesia: Household analysis]. Jurnal Ilmu Ekonomi Terapan, 1(2), 60–95. https://doi.org/10.20473/jiet.v1i2.3252

• Rivera, K. M., & Mollalo, A. (2022). Spatial analysis and modelling of depression relative to social vulnerability index across the United States. Geospatial Health, 17(2), Article 1132. https://doi.org/10.4081/gh.2022.1132

• Roghani, A. (2021). The relationship between macro-socioeconomics determinants and COVID-19 vaccine distribution. AIMS Public Health, 8(4), 655–664. https://doi.org/10.3934/publichealth.2021052

• Rufaindah, E., Patemah, P., & Yuliyanik, Y. (2021). Pengabdian Masyarakat Stikes Widyagama Husada Malang Dalam Percepatan Vaksinasi COVID-19 Dengan Kunjungan Rumah Di Wilayah Kerja Puskesmas Karangploso Kabupaten Malang [Widyagama Husada Malang Stikes Community Service in the acceleration of COVID-19 vaccination with home visits in the work area of Karangploso Puskesmas, Malang District]. SELAPARANG Jurnal Pengabdian Masyarakat Berkemajuan, 5(1), 862–866. https://doi.org/10.31764/jpmb.v5i1.6236

• Schaffer DeRoo, S., Pudalov, N. J., & Fu, L. Y. (2020). Planning for a COVID-19 vaccination program. JAMA, 323(24), 2458–2459. https://doi.org/10.1001/jama.2020.8711

• Schnake-Mahl, A. S., & Sommers, B. D. (2017). Health care in the suburbs: An analysis of suburban poverty and health care access. Health Affairs, 36(10), 1777–1785. https://doi.org/10.1377/hlthaff.2017.0545

• Soares, P., Rocha, J. V., Moniz, M., Gama, A., Laires, P. A., Pedro, A. R., Dias, S., Leite, A., & Nunes, C. (2021). Factors associated with COVID-19 vaccine hesitancy. Vaccines, 9(3), Article 300. https://doi.org/10.3390/vaccines9030300

• Syiroj, A. T. R., Pardosi, J. F., & Heywood, A. E. (2019). Exploring parents’ reasons for incomplete childhood immunisation in Indonesia. Vaccine, 37(43), 6486–6493. https://doi.org/10.1016/j.vaccine.2019.08.081

• Thanh Le, T., Andreadakis, Z., Kumar, A., Gómez Román, R., Tollefsen, S., Saville, M., & Mayhew, S. (2020). The COVID-19 vaccine development landscape. Nature Reviews Drug Discovery, 19(5), 305–306. https://doi.org/10.1038/d41573-020-00073-5

• Triwardani, R. (2021). Indonesian officials and media fight vaccine hesitancy, misinformation. Asian Politics & Policy, 13(4), 635–639. https://doi.org/10.1111/aspp.12608

• Utami, A., Margawati, A., Pramono, D., Nugraheni, A., & Pramudo, S. (2022). Determinant factors of COVID-19 vaccine hesitancy among adult and elderly population in Central Java, Indonesia. Patient Preference and Adherence, 16, 1559–1570. https://doi.org/10.2147/PPA.S365663

• Valckx, S., Crèvecoeur, J., Verelst, F., Vranckx, M., Hendrickx, G., Hens, N., Van Damme, P., Pepermans, K., Beutels, P., & Neyens, T. (2022). Individual factors influencing COVID-19 vaccine acceptance in between and during pandemic waves (July–December 2020). Vaccine, 40(1), 151–161. https://doi.org/10.1016/j.vaccine.2021.10.073

• Wang, J., Jing, R., Lai, X., Zhang, H., Lyu, Y., Knoll, M. D., & Fang, H. (2020). Acceptance of COVID-19 vaccination during the COVID-19 pandemic in China. Vaccines, 8(3), Article 482. https://doi.org/10.3390/vaccines8030482

• Widiawaty, M. A., Lam, K. C., Dede, M., & Asnawi, N. (2022). Spatial differentiation and determinants of COVID-19 in Indonesia. BMC Public Health, 22(1), Article 1030. https://doi.org/10.1186/s12889-022-13316-4

• Wiyeh, A. B., Cooper, S., Nnaji, C. A., & Wiysonge, C. S. (2018). Vaccine hesitancy ‘outbreaks’: Using epidemiological modeling of the spread of ideas to understand the effects of vaccine related events on vaccine hesitancy. Expert Review of Vaccines, 17(12), 1063–1070. https://doi.org/10.1080/14760584.2018.1549994

• Wiysonge, C. S., Ndwandwe, D., Ryan, J., Jaca, A., Batouré, O., Anya, B.-P. M., & Cooper, S. (2022). Vaccine hesitancy in the era of COVID-19: Could lessons from the past help in divining the future? Human Vaccines & Immunotherapeutics, 18(1), 1–3. https://doi.org/10.1080/21645515.2021.1893062

• World Health Organization. (2023). Coronavirus disease (COVID-19) weekly epidemiological updates and monthly operational updates. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports

• Yufika, A., Wagner, A. L., Nawawi, Y., Wahyuniati, N., Anwar, S., Yusri, F., Haryanti, N., Wijayanti, N. P., Rizal, R., Fitriani, D., Maulida, N. F., Syahriza, M., Ikram, I., Fandoko, T. P., Syahadah, M., Asrizal, F. W., Aletta, A., Haryanto, S., Jamil, K. F., … Harapan, H. (2020). Parents’ hesitancy towards vaccination in Indonesia: A cross-sectional study in Indonesia. Vaccine, 38(11), 2592–2599. https://doi.org/10.1016/j.vaccine.2020.01.072