Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Algorithms for Outbreak Detection in the Syndromic Surveillance of Influenza‐Like Illness: A Methodological Research
Grip Benzeri Hastalıkların Sendromik Sürveyansında Salgın Tespiti için İstatistiksel ve Denetimli Öğrenme Algoritmalarının Karşılaştırmalı Analizi: Metodolojik Bir Araştırma
Hasan Ali ÖZKANa, Mehmet Kadri GOFRALILARa, Zeynep Filiz ERENa
aDepartment of Computer Engineering, Muğla Sıtkı Koçman University, Muğla, Türkiye
Turkiye Klinikleri J Biostat. 2022;14(2):129-36
doi: 10.5336/biostatic.2022-91708
Article Language: EN
Full Text
ABSTRACT
 Objective: Public health authorities monitor epidemiological syndromes to provide early alerts of anomalies. A variety of approaches are applied for effective surveillance systems for influenza like illness (ILI). The present study systematically scores the accuracy of algorithms used for automated and prospective infectious-disease-outbreak detection. Another objective is to improve the performance of machine-learning (ML) approaches through statistical learning. Material and Methods: In order to reflect various situations, the volume and the size of the outbreak is chosen different for each simulation. We simulate 20 yearly sets of "daily ILI visit" to emergency department (ED), which includes seasonal outbreaks as well as unusual outbreaks of varying duration and magnitude. We search which biosurveillance algorithms work best across hidden "unusual outbreaks". Results: In terms of timeliness, both settings of kNN (res-raw), RF (resraw), and LR-raw have the best performance. All ML algorithms have sensitivity results greater than 0.90, where SVM-res (0.97), EWMA (0.96), CUSUM-moderate (0.95) are the best algorithms in terms of specificity. ML algorithms all give better performance with an integrated fitted regression model. The methods which have high sensitivity and specificity together is SVM-res (0.90 and 0.97), and LR-res (0.92 and 0.83). Conclusion: The results verified that ML algorithms integrated with statistical methods can be applied to daily ED data and can be used as a real-time surveillance method for prospective monitoring of ILI cases in the emergency setting. This study can contribute to the early detection of hidden unusual outbreaks for epidemiological studies.

Keywords: Public health surveillance; outbreak detection; CUSUM; EWMA; machine learning algorithms
ÖZET
 Amaç: Halk sağlığı yetkilileri, sıradışı gözlemler oluşması durumunda, epidemiyolojik sürveyans ile erken uyarı elde etmeyi hedefler. İnfluenza benzeri hastalıklara (influenza-like illness, ILI) ait etkin sürveyans sistemleri için çeşitli yaklaşımlar bulunmaktadır. Bu çalışmada amaç, bulaşıcı hastalık ve salgınların otomatik ve ileriye dönük tespitinde kullanılan algoritmaların gücünü sistematik bir şekilde incelemektir. Bir diğer amaç ise, istatistiksel öğrenme yoluyla makine öğrenimi (machine-learning, ML) yaklaşımlarının performansını iyileştirmektir. Gereç ve Yöntemler: Yirmi adet bir yıl uzunluğunda "ILI''ye bağlı günlük acil servis ziyaretleri" türetilmiştir. Türetilen veriler, mevsimsel salgınların yanı sıra, değişik hacimde ve boyutta olağandışı salgınları da içermektedir. Gizli "olağandışı salgınların" tespitinde hangi biyo-gözetim algoritmalarının en iyi sonucu verdiği araştırılmıştır. Bulgular: Zamanlılık açısından, Knn (res-raw), RF (res-raw) ve LR-raw uygulamaları en iyi performansa sahiptir. Tüm ML algoritmaları 0,90'dan büyük duyarlılığa sahiptir. SVM-res (0.97), EWMA (0.96), CUSUM-moderate (0.95) özgüllük açısından en iyi algoritmalardır. ML algoritmalarının tümü, regresyon modeliyle entegre şekilde kullanıldığında daha iyi performans vermektedir. Duyarlılığı ve özgüllüğü aynı anda yüksek olan yöntemler SVM-res (0.90 ve 0.97) ve LR-res (0.92 ve 0.83)'tir. Sonuç: ML algoritmaları, istatistiksel yöntemlerle entegre edilerek günlük hasta verilerine uygulandığında yüksek performans göstermektedir. Gerçek zamanlı sürveyans sistemi geliştirirken kullanılacak olan algoritmalar, araştırmada hangi performans ölçüsünün önemli olduğuna göre seçilebilir. Çalışma, epidemiyolojik çalışmalarda, gizli olağandışı salgınların erken tespitine katkıda bulunacak niteliktedir.

Anahtar Kelimeler: Halk sağlığı sürveyansı; salgın tespiti; CUSUM; EWMA; makine öğrenmesi yöntemler
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