Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Erken Uyarı ve İzleme Yöntemleri ile Artçı COVID-19 Dalgalarının Tespit Edilmesi: Retrospektif Çalışma
Detecting the Next Waves of COVID-19 Using Early Detection and Surveillance Methods: Retrospective Research
Zeynep Filiz ERENa
aMuğla Sıtkı Koçman Üniversitesi Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Muğla, TÜRKİYE
Turkiye Klinikleri J Biostat. 2021;13(3):301-10
doi: 10.5336/biostatic.2021-85512
Article Language: TR
Full Text
ÖZET
 Amaç: Dünya Sağlık Örgütü tarafından pandemi olarak ilan edilen, 15 Mayıs 2021 tarihi itibarıyla dünyada yaklaşık 3 milyon ölen; 160 milyon doğrulanan; Türkiye'de ise yaklaşık 45.000 ölen; 5 milyon doğrulanan vakası bulunan koronavirüs hastalığı-2019 [coronavirus disease-2019 (COVID-19)] salgını, artçı dalgalar şeklinde devam etmektedir. Amaç, bu salgınları erken uyarı sistemleri kullanarak zamanında tespit edebilmek; ilgili sonuçları 'Covid19Takip' adlı arayüz ile anlaşılır şekilde sunmaktır. Gereç ve Yöntemler: Açık kaynaklı R yazılımı ve 'Duyarlı-Enfekteİyileşmiş' modeli kullanılmış, değişik senaryolar için 'enfekte kişi sayısı'nı içeren zaman serileri simüle edilmiştir. Sonrasında: (1) Johns Hopkins Üniversitesi resmî web sitesinde gerçek zamanlı derlenen Türkiye verilerine; (2) elde edilen sentetik verilere, istatistiksel erken uyarı sistemlerinden Erken Sapma Raporlama Sistemi [Early Aberration Reporting System (EARS)] C2 algoritması uygulanmıştır. Erken uyarı ve takip sisteminin sonuçları Covid19Takip arayüzüne entegre edilmiştir. Bulgular: EARS C2 yöntemi, hem sentetik verilerde hem de gerçek verilerde, salgının artış göstermeye başladığı günlerde, zamanında alarm üreterek erken uyarı vermiş, kısıtlamalar geldikten sonraki dönemlerde alarm üretmeyi kesmiştir. Sonuç: Erken uyarı ve takip sistemi ile Türkiye'deki COVID-19 salgınının şimdiye kadar ki durumu ve gelecekteki olası dalgalarına ait uygulamalar yapılmıştır. Arayüzle gerçek zamanlı sunulan sonuçlar, salgına ilişkin erken tedbirler almak konusunda sağlık otoritelerine ve topluma destek olacak niteliktedir.

Anahtar Kelimeler: COVID-19; koronavirüs; pandemik; erken uyarı sistemleri; Early Aberration Reporting System
ABSTRACT
 Objective: Declared as a pandemic by the World Health Organization, as of May 15; around 3 million people, out of 5 million confirmed cases out of the world have died because of coronavirus disease-2019 (COVID-19). In Turkey around 45,000 people have died out of 5 million confirmed cases. In this study, we aim to detect the next possible waves of pandemic using early detection systems and share the results in the 'Covid19Takip' interface. Material and Methods: We simulated 'number of infected people' time series for different scenarios using the open-source software R and Susceptible-InfectedRecovered model. We then apply a statistical early detection system using Early Aberration Reporting System (EARS) C2 algorithm, for (1) simulated data and (2) Turkey data, which is acquired from the website of the Johns Hopkins University in real time. The outputs of the early detection and surveillance system is shared through 'Covid19Takip' interface. Results: EARS C2 could successfully detect the waves on time; and stopped giving signals after new restrictions were taken. Conclusion: An early detection and surveillance algorithm is applied to previous Turkish case counts and possible future waves of pandemic. The early detection and surveillance results which are shared through the interface will be beneficial to health authorities and the society in taking early precautions regarding the epidemic.

Keywords: COVID-19; coronavirus; pandemic; early detection systems; Early Aberration Reporting System
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