A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support

Submitted: 13 August 2021
Accepted: 8 November 2021
Published: 14 January 2022
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With 25% confirmed cases of the country's total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran's I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.

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Abdrakhmanov SK, Mukhanbetkaliyev YY, Korennoy FI, Karatayev BS, Mukhanbetkaliyeva AA, Abdrakhmanova AS, 2017. Spatio-temporal analysis and visualisation of the anthrax epidemic situation in livestock in Kazakhstan over the period 1933-2016. Geospat Health 12:316-24. DOI: https://doi.org/10.4081/gh.2017.589
Alkuzweny M, Raj A, Mehta S, 2020. Preparing for a COVID-19 surge: ICUs. Eclinical Medicine 25. doi.org/10.1016/j.eclinm.2020.100502. DOI: https://doi.org/10.1016/j.eclinm.2020.100502
Anselin L, 1995. Local Indicators of Spatial Association - LISA. Geogr Anal 27:93-115. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin L, Syabri I, Kho Y, 2006. GeoDa: an introduction to spatial data analysis. Geogr Anal 38:5-22. DOI: https://doi.org/10.1111/j.0016-7363.2005.00671.x
Barker A, Souisa H, 2020. Coronavirus COVID-19 death rate in Indonesia is the highest in the world. News posted Mon 23 March 2020 with update the same day. Available from: https://www.abc.net.au/news/2020-03-23/why-is-indonesia-coronavirus-death-rate-highest-in-world/12079040 Accessed: 12 May 2020.
Bergquist R, Kiani B, Manda S. 2020. First year with COVID-19: assessment and prospects. Geospat Health 15:187-90. DOI: https://doi.org/10.4081/gh.2020.953
BNPB, 2012. General Guidelines for Assessing Disaster Risk. Pub. L. No. 02.
BNPB, 2020. Hub InaCOVID-19: GIS portal task force for the acceleration of handling COVID-19 Republic of Indonesia. Available from: https://covid19.go.id Accessed: 12 May 2020.
Boulos MNK, Geraghty EM, 2020. Geographical tracking and mapping of coronavirus disease COVID‑19/SARS‑CoV‑2 epidemic and associated events around the world: how 21 century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 19(8). DOI: https://doi.org/10.1186/s12942-020-00202-8
Bourdin S, Jeanne L, Nadou F, Noiret G, 2020. Does lockdown work? A spatial analysis of the spread and concentration of Covid-19 in Italy. Reg Stud 55:1182-93. DOI: https://doi.org/10.1080/00343404.2021.1887471
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L, 2020. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Chin J Epid 395:507-13. DOI: https://doi.org/10.1016/S0140-6736(20)30211-7
Cho CJ, 1998. An equity-efficiency trade-off model for the optimum location of medical care facilities. Socio-Econ Plann Sci 32:99-112. DOI: https://doi.org/10.1016/S0038-0121(97)00007-4
Cui Z, Lin D, Chongsuvivatwong V, Zhao J, Lin M, Ou J, Zhao J, 2019. Spatiotemporal patterns and ecological factors of tuberculosis notification: a spatial panel data analysis in Guangxi, China. PLoS One 14(5). DOI: https://doi.org/10.1371/journal.pone.0212051
Desjardins MR, Hohl A, Delmelle EM, 2020. Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: detecting and evaluating emerging clusters. Appl Geogr 118:102202. DOI: https://doi.org/10.1016/j.apgeog.2020.102202
Djalante R, Lassa J, Setiamarga D, Sudjatma A, Indrawan M, Haryanto B, Mahfud C, Sinapoy MS, Djalante S, Rafliana I, Gunawan LA, Surtiari GAK, Warsilah H, 2020. Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020. Prog Disaster Sci 6:100091. DOI: https://doi.org/10.1016/j.pdisas.2020.100091
Dwyer A, Zoppou C, Nielsen O, Day S, Roberts S, 2004. Quantifying Social Vulnerability: A methodology for identifying those at risk to natural hazards. Geosci Austral 14:1-92.
Flies EJ, Mavoa S, Zosky GR, Mantzioris E, Williams C, Eri R, Brook BW, Buettel JC, 2019. Urban-associated diseases: Candidate diseases, environmental risk factors, and a path forward. Environment Int 133:105187. DOI: https://doi.org/10.1016/j.envint.2019.105187
Halim D, 2021. UPDATE 31 January: Distribution of 12,001 New Cases, Highest in Jakarta, West Java, and Central Java. Available from: https://nasional.kompas.com/read/2021/01/31/16461151/update-31-januari-sebaran-12001-kasus-baru-tertinggi-di-jakarta-jabar-dan?page=all Accessed: 12 March 2021.
Hashtarkhani S, Tabatabaei-Jafari H, Kiani B, Furst M, Salvador-Carulla L, Bagheri N, 2021. Use of geographical information systems in multiple sclerosis research: a systematic scoping review. Mult Scler Relat Disord 51:102909. DOI: https://doi.org/10.1016/j.msard.2021.102909
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B, 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395:497-506. DOI: https://doi.org/10.1016/S0140-6736(20)30183-5
Kazory A, Ronco C, McCullough PA, 2020. SARS-CoV-2 (COVID-19) and intravascular volume management strategies in the critically ill. Proc (Bayl Univ Med Cent) 33:370-5. DOI: https://doi.org/10.1080/08998280.2020.1754700
Kiani B, Raouf Rahmati A, Bergquist R, Hashtarkhani S, Firouraghi, et al., 2021. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health 21:1093. DOI: https://doi.org/10.1186/s12889-021-11157-1
Kironji AG, Hodkinson P, de Ramirez SS, Anest T, Wallis L, Razzak J, Jenson A, Hansoti B, 2018. Identifying barriers for out of hospital emergency care in low and low-middle income countries: a systematic review. BMC Health Serv Res 18:291. DOI: https://doi.org/10.1186/s12913-018-3091-0
Koch T, 2005. Cartographies of disease: maps, mapping, and medicine. ESRI Press redlands CA, USA, 403 pp.
Kumpulainen S, 2006. Vulnerability concepts in hazard and risk assessment. Geol Survey Finland Special Paper 42:65-74.
Lawson AB, 2018. Bayesian disease mapping: hierarchical modeling in spatial epidemiology - 3rd edn. 486 pp. Interdiciplinary Statistics Series. Chapman & Hall/CRC, Boca Raton, FL, USA.
Lee D, Lawson A, 2014. Cluster detection and risk estimation for spatio-temporal health data. arXiv:1408.1191 [stat.ME]. Available from: https://arxiv.org/abs/1408.1191 Accessed: 6 Nov. 2021.
Levesque JF, Harris MF, Russella G, 2013. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health 12:18. DOI: https://doi.org/10.1186/1475-9276-12-18
Li Q, Guan X, Wu P, Wang X, Zhou L, Wt L, 2020. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 382:1199-207.
Liu Y, Watson SC, Gettings JR, Lund RB, Nordone SK, Yabsley MJ, McMahan CS, 2017. A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States. PLoS One 12(7). DOI: https://doi.org/10.1371/journal.pone.0182028
Mahmoud MS, Al-Nasser FA, Al-Sunni FM, 2013. Network-based strategies for signalised traffic intersections. Indersci Publi 5(1). DOI: https://doi.org/10.1504/IJSCC.2013.054139
Mo C, Tan D, Mai T, Bei C, Qin J, Pang W, Zhang Z, 2020. An analysis of spatiotemporal pattern for COIVD-19 in China based on space-time cube. J Med Virol 92:1587-95. DOI: https://doi.org/10.1002/jmv.25834
MohammadEbrahimi S, Mohammadi A, Bergquist R, Dolatkhah F, Olia M, Tavakolian A, Pishgar E, Kiani B, 2021. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health 21:1373. DOI: https://doi.org/10.1186/s12889-021-11326-2
Okba NMA, Müller MA, Li W, Wang C, GeurtsvanKessel CH, Corman VM, Lamers MM, Sikkema RS, de Bruin E, Chandler FD, Yazdanpanah Y, Le Hingrat Q, Descamps D, Houhou-Fidouh N, Reusken CBEM, Bosch BJ, Drosten C, Koopmans MPG, Haagmans BL, 2020. SARS-CoV-2 specific antibody responses in COVID-19 patients. Emerg Infect Dis 26:1478-88. DOI: https://doi.org/10.3201/eid2607.200841
Pou SA, Tumas N, Soria DS, Ortiz P, del Pilar Díaz M, 2017. Large-scale societal factors and non-communicable diseases: Urbanization, poverty, and mortality spatial patterns in Argentina. Appl Geogr 86:32-40. DOI: https://doi.org/10.1016/j.apgeog.2017.06.022
Ristiantri YRA, Syetiawan A, Tambunan MP, Tambunan RP, 2021. Strategic planning for alternative determination of COVID-19 referral Hospital. Majalah Ilmiah Globë 23:1-12. DOI: https://doi.org/10.24895/MIG.2021.23-1.1225
Robertson C, Nelson TA, 2014. An overview of spatial analysis of emerging infectious diseases. Prof Geogr 66:579-88. DOI: https://doi.org/10.1080/00330124.2014.907702
Roser M, Ritchie H, Ortiz-Ospina E, Hasell J, 2020. Coronavirus pandemic (COVID-19). Available from: https://ourworldindata.org/coronavirus Accessed: 8 May 2020.
Shahid R, Bertazzon S, Knudtson ML, Ghali WA, 2009. Comparison of distance measures in spatial analytical modeling for health service planning. BMC Health Serv Res 9(200). DOI: https://doi.org/10.1186/1472-6963-9-200
Shekhar S, Jiang Z, Ali RY, Eftelioglu E, Tang X, Gunturi VMV, Zhou X, 2015). Spatiotemporal Data Mining: A Computational Perspective. ISPRS Int J Geoinf 4:2306-38. DOI: https://doi.org/10.3390/ijgi4042306
Silalahi FES, Hidayat F, Dewi RS, Purwono N, Oktaviani N, 2020. GIS-based approaches on the accessibility of referral hospital using network analysis and the spatial distribution model of the spreading case of COVID-19 in Jakarta, Indonesia. BMC Health Serv Res 20:1053. DOI: https://doi.org/10.1186/s12913-020-05896-x
Thomas JW, Penchansky R, 1984. Relating satisfaction with access to utilization of services. Med Care 22:553-68. DOI: https://doi.org/10.1097/00005650-198406000-00006
Vaneckova P, Beggs PJ, Jacobson CR, 2010. Spatial analysis of heat-related mortality among the elderly between 1993 and 2004 in Sydney, Australia. Soc Sci Med 70:293-304. DOI: https://doi.org/10.1016/j.socscimed.2009.09.058
Wan N, Zou B, Sternberg T, 2012. A three-step floating catchment area method for analyzing spatial access to health services. Int J Geogr Inf Sci 26:1073-89. DOI: https://doi.org/10.1080/13658816.2011.624987
Wang E, 2011. Understanding the ‘retail revolution’ in urban China: a survey of retail formats in Beijing. Service Ind J 31:169-94. DOI: https://doi.org/10.1080/02642060802706964
Wang Z, Lam NSN, 2020. Extending Getis-Ord statistics to account for local space-time autocorrelation in spatial panel data. Prof Geogr 72:411-20. DOI: https://doi.org/10.1080/00330124.2019.1709215
Weimann A, Dai D, Oni T, 2016. A cross-sectional and spatial analysis of the prevalence of multimorbidity and its association with socioeconomic disadvantage in South Africa: A comparison between 2008 and 2012. Soc Sci Med 163:144-56. DOI: https://doi.org/10.1016/j.socscimed.2016.06.055
WHO, 2020. Coronavirus disease 2019 (COVID-19): situation report, 72. 13 pp. Available from: https://apps.who.int/iris/handle/10665/331685 Accessed: 6 Nov. 2021.
Worldometer, 2020. Coronavirus Update Worldwide. Available from: https://www.worldometers.info/coronavirus/#countries Accessed: 12 May 2020.
Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W; China Novel Coronavirus Investigating and Research Team, 2020. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med 382:727-33. DOI: https://doi.org/10.1056/NEJMoa2001017

How to Cite

Syetiawan, A., Harimurti, M., & Prihanto, Y. . (2022). A spatiotemporal analysis of COVID-19 transmission in Jakarta, Indonesia for pandemic decision support. Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1042

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