Görünmeyeni Ölçmek: COVID-19 için Epidemiyolojik Eksik Tahminleme Sorunu

Year 2025, Volume: 14 Issue: 1, 35 - 67, 12.03.2025

M. Aykut Attar , Ayça Tekin Koru

Abstract

Güvenilir epidemiyolojik veriler, pandemiyle ilişkili politikaların kamu sağlığı önlemleri ve ekonomik etkiler açısından anlamlı bir şekilde analiz edilebilmesi için zorunlu bir ön koşuldur. Türkiye’de hükümet, ilk hareket kısıtlamalarının Haziran 2020’de gevşetilmesinden sonraki aylar boyunca, teyit edilen tüm COVID-19 vakalarının sayısını açıklamamış ve bu durum, bu politikaların ekonomik ve sağlık ödünleşmelerinin değerlendirilmesini ciddi şekilde zorlaştırmıştır. Bu makale, aşırı veri sınırlılıkları altında bile epidemiyolojik eksik tahminlemeyi tespit edebilen ve nicel olarak değerlendirebilen bir sistem dinamiği yaklaşımı geliştirmektedir. Simülasyon algoritmamız, virüse maruz kalan fakat henüz bulaştırıcı olmayan bireyleri açıkça dikkate alan bir doğrusal olmayan dinamik model üzerinde inşa edilmekte ve sadece birkaç güvenilir veri noktasına sahip olunmasını gerektirmektedir. Bulgular, resmî ve tahmin ettiğimiz sayılar arasında büyük farklılaşmalara işaret etmekte, karşıolgusal deneyler sosyal mesafe politikalarının yeterince iyi biçimde ve yeterince uzun süre uygulandığında COVID-19’un kontrol altında tutulması için oldukça etkili olabileceğini göstermektedir.

Keywords

doğrusal olmayan sistemler, SEIRD modeli, eksik raporlama, sosyal mesafe

Quantifying the Unseen: Epidemiological Underestimation Problem for COVID-19

Abstract

Reliable epidemiological data is a prerequisite for meaningful economic analysis of pandemic-related policies, as it provides the foundation for evaluating public health measures and their economic impacts. In Türkiye, the government did not disclose the number of all confirmed COVID-19 cases for several months after the relaxation of initial mobility restrictions in June 2020, creating significant challenges for assessing the economic and health tradeoffs of these policies. This paper addresses this issue by developing a system dynamics approach that can identify and quantify epidemiological underestimation under extreme data limitations. Our simulation algorithm builds on a nonlinear dynamical model that explicitly accounts for individuals that are exposed but not yet infectious and requires only a few reliable data points. Results imply large deviations between official and estimated figures, and counterfactual experiments show that social distancing, if practiced well and long enough, would have been highly effective for the containment of COVID-19.

Keywords

nonlinear systems, SEIRD model, underreporting, social distancing

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