Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism

Area of Science:

• Epidemiology and Statistical Physics
• Modeling infectious disease dynamics
• Application of thermodynamic principles to biological systems

Background:

• Understanding COVID-19 transmission is crucial for public health interventions.
• Existing models often lack detailed consideration of thermodynamic analogies.
• The need for models incorporating population density and policy impacts.

Purpose of the Study:

• To propose a novel evolution law for COVID-19 transmission.
• To integrate principles from thermodynamics of irreversible processes.
• To analyze the impact of various factors on epidemic spread.

Main Methods:

• Representing population on an infinite ordered lattice.
• Defining virus free energy as the natural logarithm of active infected cases.
• Postulating free energy using irreversible thermodynamics and wave propagation analogies.
• Introducing a parameter for government restriction measures and defining entropy rate.
• Developing an iterative law for infected case evolution over time.

Main Results:

• The model predicts effects of population size, density, and restriction measures on epidemic peaks and duration.
• A parameter analogous to absolute temperature influences epidemic dynamics.
• Application to Tunisia demonstrates the model's predictive capability.
• Low epidemic size in Tunisia attributed to low population density and strict measures.

Conclusions:

• The proposed thermodynamic model offers a new perspective on infectious disease evolution.
• Government interventions and population density are key determinants of epidemic outcomes.
• The model accurately reflects real-world epidemic data, as shown by the Tunisia case study.