A new logistic growth model applied to COVID-19 fatality data

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• COVID-19 temporal growth exhibits sub-exponential power-law scaling with interventions.
• Existing models may not fully capture this power-law behavior during epidemic control.

Purpose of the Study:

• To develop a new phenomenological logistic model for power-law epidemic growth.
• To accurately predict COVID-19 epidemic dynamics, including peak timing and height.

Main Methods:

• Empirical development of a logistic growth model using scaling arguments and boundary conditions.
• Model validation against COVID-19 data from Belgium, China, Denmark, and Germany.
• Non-linear least-squares minimization for parameter mapping and prediction.

Main Results:

• The proposed model accurately predicts peak heights, locations, and cumulative saturation for incomplete epidemic curves.
• The model demonstrates effectiveness even with less stringent containment strategies.
• Forecasts for COVID-19 fatalities in South Africa's third wave were generated.

Conclusions:

• The developed logistic model offers accurate predictions for COVID-19 dynamics.
• The model's utility extends to forecasting infections and deaths in other regions and infectious diseases exhibiting power-law scaling.