Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework

Randolph Hall (), Andrew Moore () and Mingdong Lyu ()
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Randolph Hall: University of Southern California
Andrew Moore: University of Southern California
Mingdong Lyu: University of Southern California

Health Care Management Science, 2023, vol. 26, issue 1, No 5, 79-92

Abstract: Abstract We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana.

Keywords: operations research; COVID-19; Cases rates; Death rates; Queueing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10729-022-09619-y

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