Year 2020, Volume 4 , Issue 11, Pages 1046 - 1051 2020-11-01

İstanbul’da hastanelere başvuran 2019-nCov hastalarının yer-zaman ilişkisinin incelemesi: Geçmişe dönük bir veri tabanı incelemesi
Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis

Bengü ŞAYLAN [1] , Doğus ÖZKAN [2]


Amaç: COVID-19 virüsünü salgını dünyanın neredeyse her yerine yayılmış durumdadır. Kontrol yöntemleri ise etkili takip ve izolasyon yöntemleri gerektirmektedir. Elektronik haritalama teknikleri COVID-19 virüsünün yayılma özelliklerini görselleştirmede kullanılmaktadır. Konum Bazlı Bilgi Sistemi yetkilileri ve halkı bilgilendirmek için çoğunlukla tercih edilmektedir. Bu sistemler halk sağlığı yetkililerine hastalığın yayılma özelliklerini göstererek, daha etkili kontrol planlaması yapılmasını sağlamaktadır. Bu çalışmanın amacı İstanbul’un en büyük iki ilçelerinden ikisinde (Kadıköy ve Üsküdar) yaşayan ve COVID-19 nedeniyle hastaneye kabul edilen hastaların yer-zaman ilişkilerinin belirlenmesidir. Yöntemler: COVID-19 enfeksiyonu tanısı onaylanmış/olası 672 yetişkin hasta bu çalışmaya dahil edilmiştir. COVID-19 teshisleri; ya pozitif sonuçlanan RT-PCT testleri ile ya da hastalığın belirtilerini görülüyorsa radyolojik göğüs tetkikleri ile doğrulanmıştır. Veriler Pearson korelasyon analizi ve Moran’s korelasyon analizi ile değerlendirilmiştir İlçeler için küçük bölge parçaları [100.000 x 100.000] ayarlandı ve her olgunun kaynağı kartezyen yer belirleme kullanılarak kurulan küçük bölge parçalarına oturtuldu. Zaman içerisinde yoğunluğun artması ile enfeksiyonu tam olarak belirlemek için Getis-Ord sıcak nokta analizi kullanılmıştır. Bulgular: Mesafe ve hastaneye kabul edilme süresi arasında Pearson korelasyonuna göre ilişki bulunmazken, Moran korelasyonun da anlamlı bir ilişki bulmuştur [I:0.64]. 3 bölgede en az 10 sıcak nokta tespit edilmiştir. Sonuç: İndeks hastane vakaları arasındaki yer-zaman ilişkisinin belirlenmesi, yerel yetkililere hastalığın yayılma örüntüsü hakkında bilgi verebilir ve salgınlara karşı kontrol önlemleri geliştirilmesine yardımcı olabilir.
Aim: The COVID-19 epidemic has reached every country in the world. Control strategies require effective tracing and isolation activities. Electronic mapping techniques are used in the visualization of spreading characteristics of COVID-19. The Geospatial Information System became an exceedingly popular open web tool to inform professionals and the public. These systems allow public health authorities to monitor the spreading characteristics and plan effective control strategies. The objective of this study was to identify the spatiotemporal mutual relationship of COVID19 patients living in two of the biggest districts of Istanbul (Kadikoy and Uskudar) who were admitted to the hospital. Methods: A total of 672 adult patients who were diagnosed with possible or confirmed COVID19 infection were included in the analysis. COVID19 diagnosis was confirmed either with positive RT-PCR test or radiographic chest imaging plus the presence of symptoms of the infection. Pearson correlation analysis and Moran’s correlation analysis were applied to the data set. Small pieces of regions [100,000 x 100,000] were set for the districts, and each event origin was fitted into the proper region using cartesian coordinate information. Getis-Ord hot spot analysis was performed to pinpoint the infections with higher concentration over time. Results: Pearson’s correlation revealed no significant results, while Moran’s analysis showed a significant correlation between distance and admission date [I: 0.64]. We identified at least 10 relevant hot spots in 3 districts. Conclusion: Determining the spatiotemporal relationship among cases of a central hospital may inform local authorities about dissemination patterns and help improve control measures against epidemics.
  • 1. WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/ Access 07.05.2020.
  • 2. Koch T. 1831: The map that launched the idea of global health. Int J Epidemiol. 2014; 43:1014-20.
  • 3. Zhou C, Su F, Pei T, Zhang A, Du Y, Luo B, et al. Covid-19: challenges to GIS with big data. Geography and Sustainability. 2020 Mar;1:77-87.
  • 4. Bull SE, Briddon RW, Sserubombwe WS, Ngugi K, Markham PG, Stanley J. Genetic diversity and phylogeography of cassava mosaic viruses in Kenya. J Gen Virol. 2006 Oct; 87:3053-3065.
  • 5. Kauhl B, Heil J, Hoebe CJ, Schweikart J, Krafft T, Dukers-Muijrers NH. The spatial distribution of hepatitis C virus infections and associated determinants -an application of a geographically weighted poisson regression for evidence-based screening interventions in hotspots. Plos One. 2005 Sep;10(9).
  • 6. Wang L, Xing J, Chen F, Yan R, Ge L, Qin Q, et al. Spatial analysis on hepatitis C virus infection in mainland China: from 2005 to 2011. Plos One. 2014;9(10).
  • 7. Daw MA, Buktir Ali LA, Daw AM, Sifennasr NEM, Dau AA, Agnan MM, et al. The geographic variation and spatiotemporal distribution of hepatitis C virus infection in Libya: 2007-2016. BMC Infect Dis. 2018 Nov;18(594).
  • 8. Venna SR, Tavanaei A, Gottumukkala RN, Raghavan VV, Maida AS, Nichols S. A novel data-driven model for real-time influenza forecasting. IEEE Access. 2019;7:7691-701.
  • 9. Kamel Boulos MN, 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 against outbreaks and epidemics. Int J Health Geogr. 2020 Mar;19(8).
  • 10. Guner R, Hasanoglu I, Aktas F. COVID-19: Prevention and control measures in community. Turk J Med Sci. 2020 Apr; 50(3):571-577.
  • 11. Kass RE, Caffo BS, Davidian M, Meng XL, Yu B, Reid N. Ten simple rules for effective statistical practice. Plos Comput Biol. 2016;12(6).
  • 12. Moran PAP. Notes on continuous stochastic phenomena. Biometrika. 1950;37:17-23.
  • 13. Getis A, Ord JK. The analysis of spatial sssociation by use of distance statistics. Geographical Analysis. 1992;24:189-206.
  • 14. Florian C. A history of mathematical notations. New York: Cosimo Inc.; 1929.
  • 15. Gravetter FJ, Wallnau LB. Statistics for the behavioral sciences. Belmont: Wadsworth – Thomson Learning; 2000.
  • 16. Lloyd C. Spatial data analysis: an introduction for GIS users. Oxford: Oxford university press; 2010.
  • 17. McGraw-Hill Concise Dictionary of Modern Medicine. https://medical-dictionary.thefreedictionary.com/hot+spot. Access 07.08.2020.
  • 18. Chakravorty S. Identifying crime clusters: the spatial principles. Middle States Geographer. 2018;28:53-8.
  • 19. Sánchez Martín JM, Rengifo Gallego JI, Blas Morato R. Hot spot analysis versus cluster and outlier analysis: An enquiry into the grouping of rural accommodation in extremadura (Spain). Int J Geo-Inf. 2019;8(176).
  • 20. Protests over responses to the COVID-19 pandemic. https://en.wikipedia.org/w/index.php?title=Protests_over_responses_to_the_COVID-19_pandemic&oldid=958106480. Access 23.05.2020.
  • 21. Yang W, Deng M, Li C, Huang J. Spatio-temporal patterns of the 2019-nCoV epidemic at the county level in Hubei province, China. Int J Environ Res Public Health. 2020;17(7):2563.
  • 22. Zhifeng J, Feng A, Li T. Consistency analysis of COVID-19 nucleic acid tests and the changes of lung CT. J Clin Virol. 2020 Jun;127.
Primary Language en
Subjects Infectious Diseases
Journal Section Research article
Authors

Orcid: 0000-0002-5922-0847
Author: Bengü ŞAYLAN (Primary Author)
Institution: İSTANBUL SULTAN ABDÜLHAMİD HAN SAĞLIK UYGULAMA VE ARAŞTIRMA MERKEZİ
Country: Turkey


Orcid: 0000-0002-5922-0847
Author: Doğus ÖZKAN
Institution: BAHÇEŞEHİR ÜNİVERSİTESİ
Country: Turkey


Dates

Publication Date : November 1, 2020

Bibtex @research article { josam793759, journal = {Journal of Surgery and Medicine}, issn = {}, eissn = {2602-2079}, address = {jsurgmed@gmail.com}, publisher = {Fatih BAŞAK}, year = {2020}, volume = {4}, pages = {1046 - 1051}, doi = {10.28982/josam.793759}, title = {Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis}, key = {cite}, author = {Şaylan, Bengü and Özkan, Doğus} }
APA Şaylan, B , Özkan, D . (2020). Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis . Journal of Surgery and Medicine , 4 (11) , 1046-1051 . DOI: 10.28982/josam.793759
MLA Şaylan, B , Özkan, D . "Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis" . Journal of Surgery and Medicine 4 (2020 ): 1046-1051 <http://jsurgmed.com/en/pub/issue/57748/793759>
Chicago Şaylan, B , Özkan, D . "Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis". Journal of Surgery and Medicine 4 (2020 ): 1046-1051
RIS TY - JOUR T1 - Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis AU - Bengü Şaylan , Doğus Özkan Y1 - 2020 PY - 2020 N1 - doi: 10.28982/josam.793759 DO - 10.28982/josam.793759 T2 - Journal of Surgery and Medicine JF - Journal JO - JOR SP - 1046 EP - 1051 VL - 4 IS - 11 SN - -2602-2079 M3 - doi: 10.28982/josam.793759 UR - https://doi.org/10.28982/josam.793759 Y2 - 2020 ER -
EndNote %0 Journal of Surgery and Medicine Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis %A Bengü Şaylan , Doğus Özkan %T Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis %D 2020 %J Journal of Surgery and Medicine %P -2602-2079 %V 4 %N 11 %R doi: 10.28982/josam.793759 %U 10.28982/josam.793759
ISNAD Şaylan, Bengü , Özkan, Doğus . "Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis". Journal of Surgery and Medicine 4 / 11 (November 2020): 1046-1051 . https://doi.org/10.28982/josam.793759
AMA Şaylan B , Özkan D . Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis. J Surg Med. 2020; 4(11): 1046-1051.
Vancouver Şaylan B , Özkan D . Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis. Journal of Surgery and Medicine. 2020; 4(11): 1046-1051.
IEEE B. Şaylan and D. Özkan , "Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis", Journal of Surgery and Medicine, vol. 4, no. 11, pp. 1046-1051, Nov. 2020, doi:10.28982/josam.793759