COVID-19 Tanısında Semptomların ve Demografik Özelliklerin Lojistik Regresyon Modeli ile Analizi

Abstract

Yeni koronavirüs COVID-19, Aralık 2019'da küresel olarak yayılmaya başlayan bulaşıcı bir hastalıktır. Bazı semptomların COVID-19 virüsünün enfekte olup olmadığına dair ipuçları verdiği bilinmektedir. Bu nedenle, bu makalenin temel amacı COVID-19 vakalarının hızlı teşhisi için COVID-19 ile ilgili spesifik semptomları belirlemektir. PCR sonuçları, 2 demografik özellik (yaş, cinsiyet) ve baş ağrısı, nefes darlığı, boğaz ağrısı, ateş ve öksürük gibi 5 semptomu içeren 25985 kişiden oluşan veri seti bu çalışmada dikkate alınmıştır. Bu ortak değişkenler ile PCR sonuçları arasındaki ilişki ikili lojistik regresyon modeli ile analiz edilmiştir. PCR sonucu pozitif olan toplam 16405 (%63,1) birey bu çalışmaya dahil edilmiştir. Araştırma popülasyonu iki yaş grubuna ayrılmıştır (<60 ve ≥60). COVID-19 hastalarında gözlenen semptomlara ilişkin bulgular şu şekilde sıralanabilir: Baş ağrısı (%25,8), nefes darlığı (%2,2), boğaz ağrısı (%11,2), ateş (%16,3) ve öksürük (%26,2). İkili lojistik regresyon analizi bulgularına göre, yaşlı gruptaki herhangi bir bireyin pozitif PCR sonucu alma olasılığı yaklaşık 1,6 kat daha fazladır (odds oranı [OR]: 1,681), %95 güven aralığı [CI]: 1.535-1.840). Ayrıca, baş ağrısı semptomları olan bir bireyin pozitif PCR sonucuna sahip olma olasılığı olmayanlara göre yaklaşık %7 daha fazladır (OR: 1.068, CI: 1.006-1.135).

Keywords

• İkili lojistik regresyon
• COVID-19
• Bulaşıcı hastalık
• Semptomlar
• Odds oranı

Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model

Abstract

The new coronavirus COVID-19 is an infectious disease that started spreading globally in December 2019. Some symptoms are known to give clues as to whether the COVID-19 virus is infected. Therefore, the main purpose of this paper was to determine specific symptoms related to COVID-19 for the rapid diagnosis of COVID-19 cases. The data set consists of 25985 individuals including PCR results, 2 demographic properties (age, gender), and 5 symptoms such as headache, shortness of breath, sore throat, fever, and cough is considered in this study. We analyzed the relationship between these covariates and PCR results by binary logistic regression model. A total of 16405 (63.1%) individuals having to positive PCR results were included in this study. The research population was divided into two age groups (<60 and ≥60). The findings regarding the symptoms observed in COVID-19 patients can be listed as follows: Headache (25.8%), shortness of breath (2.2%), sore throat (11.2%), fever (16.3%), and cough (26.2%). The findings of binary logistic regression analysis show that any individual in the elder group has more probability of a positive PCR result approximately 1.6 times (odds ratio [OR]: 1.681), 95% confidence interval [CI]: 1.535-1.840). Also, an individual with symptoms of headache is approximately %7 more likely to have a positive PCR result than a nonexistent one (OR: 1.068, CI: 1.006-1.135).

Keywords

• Binary logistic regression
• COVID-19
• Infectious disease
• Symptoms
• Odds ratio

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