Türkiye’de Günlük COVID-19 Test Sayısı ve Yeni Vaka İlişkisinin Nedensellik Analizi

Year 2021, , 88 - 97, 30.01.2021

Sultan Tarlacı

https://doi.org/10.21673/anadoluklin.799232

Abstract

Amaç: Bu çalışmada, Türkiye’de 11 Mart–16 Temmuz 2020 tarihleri arasında, COVID-19 günlük tanısal test sayıları ve tanı konulan yeni vaka sayıları arasında kısa ve uzun süreli nedensellik analizi yapılması amaçlandı.

Gereç ve Yöntemler: Çalışmamızda klasik sıklık istatistiklerinin yanı sıra, vektör otoregresyon (VAR) modeli, Granger nedenselliği analizi ve tam düzenlenmiş en küçük kareler (FMOLS) testi kullanıldı.

Bulgular: Granger nedensellik analizine göre, VAR modelinin önerdiği beş gecikme uzunluğu ile günlük olarak gerçekleştirilen COVID-19 tanı testlerinin sayısı, günlük yeni vakaların nedeni olarak belirlendi. Buna karşın, günlük yeni vakaların sayısı COVID-19 test sayısının bir nedeni olarak tespit edilmedi.

Sonuç: Sonuçlara göre, COVID-19 günlük testinden günlük yeni tanı konulan vakaya doğru tek yönlü bir nedensellik olduğu sonucuna varıldı. Uzun vadeli nedensellik ilişkisi tahmini FMOLS tarafından analiz edildi. Bağımlı değişken yeni tanı konulan vaka sayısı alındığında, bağımsız değişken test sayısında uzun dönemde meydana gelen %1’lik bir artışın, yeni tanı alan vaka sayısında %0,71 artış sağlayacağı tespit edildi. Test sayısı ile yeni tanı konulan vaka sayısı arasında pozitif bir ilişki bulundu. Eğer denkleme uzun dönem eşbütünleme katsayıları da eklenir ise %1’lik bir COVID-19 test sayısı artışının, %0,47 yeni tanı alan vaka sayısında artışa neden olabileceği öngörüldü.

Keywords

COVID-19, nedensellik, PCR Test, VAR Modeli, Granger nedenselliği, FMOLS, tam düzenlenmiş en küçük kareler

Supporting Institution

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Thanks

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Causality Analysis of Daily COVID-19 Tests and New Case Relationship in Turkey

Abstract

Aim: In this study, we performed short and long-term causality analysis between COVID-19 daily diagnostic test numbers and daily diagnosed new cases using data March 11–July 16, 2020 in Turkey.

Materials and Methods: In this study, in addition to classical frequency statistical approach, we used advanced statistical analysis as vector autoregression (VAR) model, Granger causality analysis and fully modified ordinary least squares (FMOLS)

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Results: According to the Granger causality analysis, the number of COVID-19 diagnostic tests performed daily with five lag length recommended by the VAR model was determined as the cause of new cases per day. Despite this, the number of new cases was not appeared to be the reason for the number of COVID-19 tests.

Conclusion: According to the results, it was concluded that there is a unidirectional causality from COVID-19 daily test to daily new diagnosed case. The long-term causality relationship prediction was analyzed by FMOLS. When the number of cases newly diagnosed with the dependent variable was taken, it was determined that a 1% increase in the number of independent variable COVID-19 tests in the long term would provide a 0.71% increase in the number of newly diagnosed COVID-19 cases. A positive correlation was found between the number of tests and the number of newly diagnosed cases. If long-term cointegration coefficients are added to the equation, it was predicted that a 1% increase in the number of COVID-19 daily tests could cause an increase in the number of new cases of 0.47%.

Keywords

COVID-19, PCR Test, VAR Model, Granger causality, fully modified ordinary least squares, FMOLS

Project Number

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