SPATIAL AND TEMPORAL ANALYSIS OF COVID-19 CASES DISTRIBUTION IN SUKOHARJO REGENCY
Analisis Spasial dan Temporal Distribusi Kasus COVID-19 di Kabupaten Sukoharjo
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Background: COVID-19 has become a public health challenge in Sukoharjo Regency, as its cumulative cases reached 15,258 confirmed cases with 1,380 deaths (CFR 9.04%). Spatial and temporal analysis can provide an overview of the spatial and temporal factors associated with the disease and explain the analysis of the disease distribution in a population to clarify the transmission mechanism. Purpose: This study aimed to provide an overview of the spatial and temporal distribution of COVID-19 cases in the Sukoharjo Regency and determine the spatial autocorrelation of the distribution of COVID-19 cases in the Sukoharjo Regency. Methods: This was an observational study with an ecological design. The data used was Secondary data collected from the Health Office of Sukoharjo, with the population of all COVID-19 confirmed cases recorded by the Health Office of Sukoharjo from 2020 to 2021. The sample was 15,528 patients. Results: The highest number of COVID-19 cases in Sukoharjo Regency was found in the Baki Sub-district (242.23/10,000 people). In comparison, the lowest number of cases was found in the Polokarto Sub-district (114.60/10,000 people). The Sukoharjo Regency experienced two waves of COVID-19, and its peak occurred in July 2021. The results showed spatial dependence in the COVID-19 case distribution with a Moran'sI value of 0.36, z-score of 7.50, and p-value <0.01. Conclusion: The highest number of COVID-19 findings occurred in July 2021, and there was spatial autocorrelation in the distribution of COVID-19 cases in the Sukoharjo Regency with a clustered transmission pattern.
World Health Organization. Corona Virus Disease (COVID-19). World Health Organization. 2021.
Satuan Tugas Penanganan COVID-19. Situsasi Virus COVID-19 di Indonesia. Satuan Tugas Penanganan COVID-19. 2021.
Dinas Kesehatan Provinsi Jawa Tengah. Tanggap COVID-19 Provinsi Jawa Tengah. Dinas Kesehatan Provinsi Jawa Tengah. 2021.
Dinas Kesehatan Kabupaten Sukoharjo. Sukoharjo Tanggap COVID-19. Dinas Kesehatan Kabupaten Sukoharjo. 2021.
Barbieri DM, Lou B, Passavanti M, Hui C, Hoff I, Lessa DA, et al. Impact of COVID-19 pandemic on mobility in ten countries and associated perceived risk for all transport modes. PLoS One. 2021;16(2 February):1–18.
Nicola M, Alsafi Z, Sohrabi C, Kerwan A, Al-Jabir A, Iosfidis C, et al. The socio-economic implications of the coronavirus pandemic (COVID-19): A review. Int J Surg. 2020;78:185–93.
United Nations. UN Response to COVID-19. United Nations. 2021.
Ansah JP, Matchar DB, Wei SLS, Low JG, Pourghaderi AR, Siddiqui FJ, et al. The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience. PLoS One. 2021;16(3 March):1–16.
Boulos MNK, Geraghty EM. Geographical tracking and mapping of coronavirus disease COVID "‘ 19 / severe acute respiratory syndrome coronavirus 2 ( SARS "‘ CoV "‘ 2 ) epidemic and associated events around the world : how 21st century GIS technologies are supporting the global fight ag. Int J Health Geogr. 2020;19(8):1–12.
Wang L, Xu C, Wang J, Qiao J, Yan M, Zhu Q. Spatiotemporal heterogeneity and its determinants of COVID-19 transmission in typical labor export provinces of China. BMC Infect Dis. 2021;21(1):1–12.
Cavalcante JR, de Abreu A de JL. COVID-19 in the city of Rio de Janeiro : spatial analysis of first confirmed cases and deaths. Epidemiol e Serv saude Rev do Sist Unico Saude do Bras. 2020;29(3):1–9.
Fatima M, O'keefe KJ, Wei W, Arshad S, Gruebner O. Geospatial analysis of covid-19: A scoping review. Int J Environ Res Public Health. 2021;18(5):1–14.
Zhou C, Su F, Pei T, Zhang A, Du Y, Luo B. Geography and Sustainability COVID-19 : Challenges to GIS with Big Data. 2020;1:77–87.
Wang Q, Dong W, Yang K, Ren Z, Huang D, Zhang P, et al. Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors. Int J Infect Dis. 2021;105:675–85.
Susilo A, Rumende CM, Pitoyo CW, Santoso WD, Yulianti M, Sinto R, et al. Coronavirus Disease 2019 : tinjauan literatur terkini Coronavirus Disease 2019 : review of current literatures. J Penyakit Dalam Indones. 2020;7(1):45–67.
Bhunia GS, Roy S, Shit PK. Spatio-temporal analysis of COVID-19 in India – a geostatistical approach. Spat Inf Res. 2021;
Lolita DA, Nugraha AL, Awaludin M. Penilaian kapasitas COVID-19 di Kabupaten Sukoharjo menggunakan sistem informasi geografis. J Geod Undip. 2022;11(2):1–7.
Rex FE, Borges S. Spatial analysis of the COVID-19 distribution pattern in Sí£o Paulo State , Brazil. Cien Saude Colet. 2020;25(9):3377–84.
Ren H, Zhao L, Zhang A, Song L, Liao Y, Lu W, et al. Early forecasting of the potential risk zones of COVID-19 in China's megacities. Sci Total Environ. 2020;729(January):1–8.
Chen Z, Zhang Q, Lu Y, Guo Z, Zhang X, Zhang W, et al. Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan , China. Chin Med J (Engl). 2020;133(9):1044–50.
Triana D, Ambarsarie R, Suryani UH, Massardi NA, Sariyanti M, Nugraheni E, et al. Analysis of vulnerability and spatio-temporal distribution toward the severity level of COVID-19 in Bengkulu, Indonesia. Eur J Mol Clin Med. 2021;8(3):657–63.
Puspitaningrum WA, Zaen NA, Wahyuni CU. Epidemiology of COVID-19 cases in the Klaten district in 2020. J Berk Epidemiol. 2022;10(2):210–8.
Pertiwi KD, Widyaningsih T, Sucipto PT, Masyarakat SK, Waluyo UN, Masyarakat SK, et al. Analisis spasio-temporal covid-19 di Kabupaten Semarang pada bulan September. Pro Heal J Ilm Kesehat hingga Novemb Tahun 2021. 2022;4(November 2021):213–25.
Ghiffari RA. Dampak populasi dan mobilitas perkotaan terhadap penyebaran pandemi COVID-19 di Jakarta. J Tunas Geogr. 2020;09(01):81–8.
Wahyudiyono W, Eko BR, Trisnani T. Persepsi masyarakat terhadap COVID-19 pasca PPKM (Pemberlakuan Pembatasan Kegiatan Masyarakat). J Komunika J Komunikasi, Media dan Inform. 2021;10(2):102–12.
Liu Y, Rocklov J. The reproductive number of the Delta variant of SARS-CoV-2 is far higher compared to the ancestral SARS-CoV-2 virus. J Travel Med. 2021;Agustus(2):1–3.
Yu H, Li J, Bardin S, Gu H, Fan C. Spatiotemporal dynamic of covid-19 diffusion in china: A dynamic spatial autoregressive model analysis. ISPRS Int J Geo-Information. 2021;10(8):1–13.
Cavalcante JR, Abreu A de JL de. COVID-19 no município do Rio de Janeiro: análise espacial da ocorríªncia dos primeiros casos e óbitos confirmados. Epidemiol e Serv saude Rev do Sist Unico Saude do Bras. 2020;29(3):1–9.
Ghosh P, Cartone A. A Spatio-temporal analysis of COVID-19 outbreak in Italy. Reg Sci Policy Pract. 2020;12(6):1047–62.
Xie Z, Qin Y, Li Y, Shen W, Zheng Z, Liu S. Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors. Sci Total Environ. 2020;744(January).
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