Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Türkiye'de COVID-19 Vaka Sayılarının Poisson Regresyon ile Tahmini ve Alınan Önlemlerin İnsidans Hızı Tahminlerine Etkisi
Predicting COVID-19 Cases in Turkey with Poisson Regression and the Effect of Preventions on Incidence Rate Ratio Estimation
Bahar TAŞDELENa, Didem DERİCİ YILDIRIMa
aMersin Üniversitesi Tıp Fakültesi, Biyoistatistik ve Tıbbi Bilişim ABD, Mersin, TÜRKİYE
Turkiye Klinikleri J Biostat. 2020;12(3):293-302
doi: 10.5336/biostatic.2020-77595
Article Language: TR
Full Text
ÖZET
 Amaç: Bu çalışmanın amacı, COVID-19 pandemisinin Türkiye'de yayılım sürecinin ilk 60 gününü sık aralıklarla Poisson regresyon ile modelleyerek, kestirim zamanının tahminler üzerine etkisini incelemek, alınan önlemler ve yapılan test sayısının vaka sayısı üzerine etkisini değerlendirmektir. Gereç ve Yöntemler: Bu çalışmada, Türkiye'ye ait 10 Mart-8 Mayıs 2020 tarih aralığındaki günlük vaka sayılarından yararlanılmıştır. Günlük vaka sayıları, Poisson regresyon yöntemi kullanılarak modellenmiştir. Modelde, günlük vaka sayısı bağımlı değişken, gün zaman serisi değişkeni ve günlük test sayısı kovaryant değişken olarak alınmıştır. Salgının yayılımını önlemeye yönelik alınan önlemlerin günlük vaka sayısı üzerine etkileri, insidans hızı oranı (IRR) ve %95 güven aralığı (GA) ile değerlendirilmiştir. Bulgular: Salgının ilk 10 gününde vaka sayılarındaki varyasyon düşük olduğundan, gerçek verilere oldukça yakın tahminler elde edilmiştir. Ancak 10 günden sonra verilerdeki varyasyon giderek arttığından, tahminlerin gerçek değerlerden uzaklaştığı görülmüştür. Günlük vaka sayısının 30-40 gün periyodunda pik yaptığı ve pik noktasından sonra günlük vaka sayılarının başlangıçtaki artış hızıyla simetrik olarak azalmaya başladığı gözlenmiştir. Bu periyottan itibaren verilerdeki tutarlılık tahminlere de yansıdığından, 40-50 günlük verilerle yapılan geleceğe yönelik tahminlerin önceki periyotlara oranla gerçeğe daha yakın olduğu belirlenmiştir. Önlemler ve alındıkları günler genel olarak değerlendirildiğinde, vaka sayısındaki en önemli azalmanın, okullarda yüz yüze eğitime ara verilmesi, kamu kurumlarında kronik hastalığı olan ve 65 yaş üstü bireylere idari izin [IRR=0,783, %95 GA (0,635-0,966)] ile kuaför ve güzellik salonlarının kapatılması ile sağlandığı tespit edilmiştir [IRR=0,618, %95 GA (0,612-0,623)]. Sonuç: Bu çalışmada yapılan ülke geneline ait istatistiksel modellemelerin, bölge bölge ve hatta il il yapılmasının, bu tip salgınların kontrol altına alınmasında etkin rol oynayacağı düşünülmektedir.

Anahtar Kelimeler: COVID-19; Poisson regresyon modeli; insidans; tahmin
ABSTRACT
 Objective: The aim of this study is to model the spread process of COVID-19 in Turkey within the first 60 days via frequent poisson regression, investigate prediction time effect on estimations and evaluate effects of preventions and number of tests on the number of cases. Material and Methods: In this study, daily case numbers between March 10 and May 8 of Turkey were used to analyze. Daily case numbers were modelt with poisson regression. In the model, dependent variable was daily event number, day was the time-series variable and daily test number was determined as covariate variable. Incidence rate ratio (IRR) and 95% confidence intervals (CIs) were used to evaluate the effects of the preventions on the number of cases per day. Results: The estimations were very close to real data because of low variation in first 10 days of pandemy. However, it was seen that the estimates diverged from the real values, since the variation in the data gradually increased after 10 days. It was observed that the number of daily cases peaked in the 30-40 day period and after the peak point, the number of daily cases started to decrease symmetrically with the initial increase rate. It has been determined that the estimates of 40-50 days data were closer to real than the previous periods because of the consistency in the data of this period. The preventations and the days they are taken were evaluated in general. The interruption of face-to-face education in schools, administrative leave for individuals over 65 years old with chronic disease in public institutions [IRR=0.783, 95% CI (0.635-0.966)] and the closure of hairdressers and beauty salons [IRR=0.618, 95% CI (0.612-0.623)] provided the most important decrease of case numbers in all preventions. Conclusion: It is thought that the statistical modeling of country-wide, regionally and even provincially will play an effective role in controlling such epidemics.

Keywords: COVID-19; Poisson regression model; incidence; prediction
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