Identifying waves of COVID-19 mortality using skew normal curves

Area of Science:

• Epidemiology
• Biostatistics
• Mathematical Modeling

Background:

• Modeling epidemic waves is crucial for understanding disease dynamics.
• Previous models may not fully capture the complexities of daily mortality fluctuations.

Purpose of the Study:

• To propose and validate a new mathematical model for analyzing multiple epidemic waves.
• To apply this model to daily COVID-19 mortality data across diverse global regions.

Main Methods:

• Decomposition of health outcomes into scaled skew normal curves.
• Fitting the model to daily COVID-19 mortality data from January 2020 to May 2022.
• Analysis of curve asymmetry and day-of-the-week effects.

Main Results:

• The skew normal curves accurately aligned with identified COVID-19 waves in Ontario and Belgium.
• Observed shifts in curve asymmetry indicate changes in the rate of mortality increase and decrease.
• Significant day-of-the-week effects were detected in most analyzed regions.

Conclusions:

• The proposed skew normal curve model effectively represents epidemic waves and mortality trends.
• The findings highlight the importance of incorporating day-of-the-week effects in epidemic modeling.
• The model shows potential for analyzing future epidemic waves.