Diffusive process under Lifshitz scaling and pandemic scenarios.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Physics

Background:

• The COVID-19 pandemic presents complex diffusion dynamics.
• Existing models may not fully capture real-world virus spread.
• Understanding transmission is crucial for effective public health interventions.

Purpose of the Study:

• To develop and validate a continuous effective model for active and cumulative COVID-19 cases.
• To utilize a modified diffusion equation with Lifshitz scaling and a dynamic diffusion coefficient.
• To analyze virus diffusion, predict pandemic evolution, and assess intervention impacts.

Main Methods:

• A continuous effective model based on a modified diffusion equation.
• Incorporation of Lifshitz scaling and a dynamic diffusion coefficient.
• Analytical and numerical solutions to model different contamination profiles.
• Fitting derived active cases curves to data from Germany and Spain.

Main Results:

• The model successfully described active COVID-19 cases in Germany and Spain.
• Predictions for the evolution of COVID-19 in Brazil were generated.
• Cumulative cases demonstrated the pandemic's spread between Brazilian cities.
• Lockdown measures were shown to flatten contamination curves.

Conclusions:

• The proposed diffusion model accurately captures COVID-19 pandemic behavior.
• The model can predict future scenarios and evaluate the effectiveness of public health policies.
• Identifying optimal diffusion coefficient profiles enhances pandemic data fitting.