Multiple waves of COVID-19: a pathway model approach.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The COVID-19 pandemic has demonstrated complex, multi-wave infection patterns globally.
• Accurate modeling of epidemic dynamics is crucial for understanding disease spread and intervention effectiveness.

Purpose of the Study:

• To introduce and validate a generalized, time-dependent pathway model for describing COVID-19 mortality curves.
• To enable direct fitting to daily epidemiological data (new cases/deaths) without integration.
• To extract key epidemiological parameters like wave start and peak dates.

Main Methods:

• Application of a generalized pathway model with time-dependent parameters.
• Formulation of the model's growth rate as an explicit function of time.
• Direct fitting of the model to daily COVID-19 mortality data from ten countries.

Main Results:

• The model demonstrated very good agreement with COVID-19 mortality data across all ten selected countries.
• The model successfully captured multiple infection waves observed in the data.
• Relevant epidemiological information, including wave timing, was extracted from fitted curves.

Conclusions:

• The time-dependent pathway model provides a robust framework for analyzing epidemic curves, particularly for diseases with multiple waves like COVID-19.
• Reliable estimation of wave characteristics is vital for evaluating the impact of public health interventions and policy changes.
• The model facilitates a direct comparison between intervention timelines and epidemic peaks/troughs.