The Performance of Some Outbreak Detection Algorithms: Using the Reported COVID-۱۹ cases in Iran

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.

Keywords:

Outbreak , Epidemic , Mathematics Concept , COVID-۱۹ , Iran