Interpreting airborne pandemics spreading using fractal kinetics' principles.

Area of Science:

• Epidemiology
• Complex Systems Science
• Mathematical Biology

Background:

• Traditional epidemic models often assume well-mixed populations, which may not reflect real-world disease spread.
• Understanding the impact of preventive measures on population mobility is crucial for controlling infectious diseases.

Purpose of the Study:

• To analyze COVID-19 transmission dynamics using fractal kinetics principles for a not well-mixed system.
• To develop and validate a novel three-parameter fractal model for pandemic analysis.
• To assess the influence of preventive measures on disease spreading patterns.

Main Methods:

• Analysis of COVID-19 data from four countries (France, Greece, Italy, Spain) over 10 months.
• Derivation of a three-parameter fractal kinetics model incorporating a time exponent 'h'.
• Fitting model parameters to distinct pandemic stages characterized by varying control measures.

Main Results:

• The fractal exponent 'h' effectively quantifies the impact of preventive measures on population mobility and disease spread.
• A fractal exponent h=1, representing a power-of-time model, aligns with scenarios lacking preventive measures and herd immunity policies.
• The fractal kinetics model demonstrated superior accuracy in reflecting real-world pandemic progression across different stages and countries compared to traditional models.

Conclusions:

• Fractal kinetics provides a more realistic framework for analyzing contagious airborne pandemics than traditional models.
• The developed fractal model can serve as a prototype for studying and predicting the spread of future pandemics.
• Preventive measures significantly alter transmission dynamics, which can be effectively captured by fractal exponent 'h'.