The Performance of Some Outbreak Detection Algorithms: Using the Reported COVID-۱۹ cases in Iran
- سال انتشار: 1402
- محل انتشار: مجله اقدامات و تحقیقات بیمارستانی، دوره: 8، شماره: 1
- کد COI اختصاصی: JR_HPR-8-1_006
- زبان مقاله: انگلیسی
- تعداد مشاهده: 200
نویسندگان
Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
چکیده
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.کلیدواژه ها
Outbreak, Epidemic, Mathematics Concept, COVID-۱۹, Iranاطلاعات بیشتر در مورد COI
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