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The Performance of Some Outbreak Detection Algorithms: Using the Reported COVID-19 cases in Iran

Publish Year: 1402
Type: Journal paper
Language: English
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JR_HPR-8-1_006

Index date: 16 June 2024

The Performance of Some Outbreak Detection Algorithms: Using the Reported COVID-19 cases in Iran abstract

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-19 data for outbreak detection.Methods: Three outbreak detection algorithms were applied to the data of COVID-19 daily new cases in Iran between 19/02/2020 and 20/06/2022, and 344 simulated outbreak days were injected into the data sequences. The Area Under the Receiver Operating Characteristics (ROC) Curve (AUC) and its 95% confidence intervals (95% CI) were also computed.Results: EWMA9 had the lowest AUC (51%). Among the different algorithms, EWMA9 with λ = 0.9 and CUSUM 1 had the highest sensitivity with 100 and 87% (95% CI: 84%-91%), respectively.Conclusion: According to the results, CUSUM, EWMA, and poison regression showed appropriate performance in detecting the COVID-19 outbreaks. These algorithms can be extremely helpful for health practitioners and policymakers in the detection of infectious disease outbreaks.

The Performance of Some Outbreak Detection Algorithms: Using the Reported COVID-19 cases in Iran Keywords:

The Performance of Some Outbreak Detection Algorithms: Using the Reported COVID-19 cases in Iran authors

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