On the early detecting of the COVID-19 outbreak

Abstract

Introduction: This paper aims to measure the performance of early detection methods, which are usually used for infectious diseases.
 Methodology: By using real data of confirmed Coronavirus cases from the Kingdom of Saudi Arabia and Italy, the moving epidemic method (MEM) and the moving average cumulative sums (Mov. Avg Cusum) methods are used in our simulation study.
 Results: Our results suggested that the CUSUM method outperforms the MEM in detecting the start of the Coronavirus outbreak.

Early Detection of SARS-CoV-2 Epidemic Waves: Lessons from the Syndromic Surveillance in Lombardy, Italy

“…Some additional issues deserve to be addressed. First, CUSUM chart models have been extensively used for syndromic surveillance systems worldwide [13][14][15]. Although we did not attempt a between-model comparison, we observed that the residual EWMA chart worked well in showing abnormal increases in daily counts of respiratory-syndrome visits.…”

“…Statistical detection algorithms are critical in any syndromic surveillance system [13]. One of the most popular methods used for the early detection of a respiratory syndrome outbreak is the residual chart model known as the cumulative sum (CUSUM) [14,15]. Similar methods received less attention from public health agencies [16], although some of them (e.g., the exponentially weighted moving average (EWMA) chart) are characterized by exciting properties and greater flexibility compared to the CUSUM residual chart [17,18].…”