Identifying waves of COVID-19 mortality using skew normal curves

Abstract

We propose a model for multiple waves of an epidemic that decomposes the health outcome of interest into the sum of scaled skew normal curves. When applied to daily COVID-19 mortality in six regions (Japan, Italy, Belgium, Ontario, Texas, and Peru), this model provides three notable results. First, when fit to data from early 2020 to May 31, 2022, the estimated skew normal curves substantially overlap with the dates of COVID-19 waves in Ontario and Belgium, as determined by their respective health authorities. Second, the asymmetry of the skew normal curves changes over time – they progress from increasing more quickly to decreasing more quickly, indicating changes in the relative speed that daily COVID-19 mortality rises and falls over time. Third, most regions have day-of-the-week effects, which suggests that day-of-the-week effects should be included when modeling daily COVID-19 mortality. We conclude by discussing limitations and possible extensions of this model and its results, including commenting on its applicability to potential future COVID-19 waves.

Keywords:

• Daily mortality
• day-of-the-week effect
• skew normal distribution
• Bayesian inference
• COVID-19

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data sources are detailed in Section 2.1 and the code used to produce this paper is available at: https://github.com/Kamal-R/SoSN-Model.

Additional information

Funding

This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).