Spatiotemporal relationship analysis of the 2019-nCoV patients hospitalized in Istanbul: A retrospective database analysis

Year 2020, Volume: 4 Issue: 11, 1046 - 1051, 01.11.2020

Bengü Şaylan , Doğus Özkan

https://doi.org/10.28982/josam.793759

Abstract

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.

Keywords

Spatio-Temporal Relationship Analysis, 2019-nCoV, Counties

İstanbul’da hastanelere başvuran 2019-nCov hastalarının yer-zaman ilişkisinin incelemesi: Geçmişe dönük bir veri tabanı incelemesi

Abstract

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.

Keywords

Yer-zamansal ilişki incelemesi, 2019-nCoV, İlçeler

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