The Performance of Some Outbreak Detection Algorithms: Using the Reported COVID-۱۹ cases in Iran
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 69
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شناسه ملی سند علمی:
JR_HPR-8-1_006
تاریخ نمایه سازی: 27 خرداد 1403
چکیده مقاله:
Background: Outbreak detection algorithms could play a key role in public health surveillance.Objectives: This study aimed to compare the performance of three algorithms (EWMA, Cumulative Sum (CUSUM), and Poisson Regression) using the reported COVID-۱۹ data for outbreak detection.Methods: Three outbreak detection algorithms were applied to the data of COVID-۱۹ daily new cases in Iran between ۱۹/۰۲/۲۰۲۰ and ۲۰/۰۶/۲۰۲۲, and ۳۴۴ simulated outbreak days were injected into the data sequences. The Area Under the Receiver Operating Characteristics (ROC) Curve (AUC) and its ۹۵% confidence intervals (۹۵% CI) were also computed.Results: EWMA۹ had the lowest AUC (۵۱%). Among the different algorithms, EWMA۹ with λ = ۰.۹ and CUSUM ۱ had the highest sensitivity with ۱۰۰ and ۸۷% (۹۵% CI: ۸۴%-۹۱%), respectively.Conclusion: According to the results, CUSUM, EWMA, and poison regression showed appropriate performance in detecting the COVID-۱۹ outbreaks. These algorithms can be extremely helpful for health practitioners and policymakers in the detection of infectious disease outbreaks.
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نویسندگان
Mojtaba Sepandi
Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
Yousef Alimohamadi
Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
Mousa Imani
Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran