A Detecting System for Abrupt Changes in Temporal Incidence Rate of COVID-19 and Other Pandemics

Jiecheng Song, Guanchao Tong () and Wei Zhu
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Jiecheng Song: Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
Guanchao Tong: Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
Wei Zhu: Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA

Stats, 2023, vol. 6, issue 3, 1-11

Abstract: COVID-19 spread dramatically across the world in the beginning of 2020. This paper presents a novel alert system that will detect abrupt changes in the COVID-19 or other pandemic incidence rate through the estimated time-varying reproduction number (Rt). We applied the system to detect abrupt changes in the COVID-19 pandemic incidence rates in thirteen world regions with eight in the US and five across the world. Subsequently, we also evaluated the system with the 2009 H1N1 pandemic in Hong Kong. Our system performs well in detecting both the abrupt increases and decreases. Users of the system can obtain accurate information on the changing trend of the pandemic to avoid being misled by low incidence numbers. The world may face other threatening pandemics in the future; therefore, it is crucial to have a reliable alert system to detect impending abrupt changes in the daily incidence rates. An added benefit of the system is its ability to detect the emergence of viral mutations, as different virus strains are likely to have different infection rates.

Keywords: abrupt change detection; COVID-19; daily case prediction; pandemic incidence rate; reproduction number (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2023
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