Reaction-diffusion spatial modeling of COVID-19: Greece and Andalusia as case examples

Area of Science:

• Epidemiology
• Mathematical Modeling
• Computational Science

Background:

• COVID-19 pandemic presented significant public health challenges globally.
• Understanding disease transmission dynamics is crucial for effective control strategies.
• Spatial and temporal factors significantly influence infectious disease outbreaks.

Purpose of the Study:

• To develop and apply spatial epidemiological models for COVID-19.
• To analyze the impact of asymptomatic transmission and containment measures.
• To compare model predictions with real-world data in Andalusia, Spain, and Greece.

Main Methods:

• Utilized a zero-dimensional (0D) compartmental epidemiological model (SEAIHR).
• Optimized model parameters by minimizing prediction errors against infection and death data.
• Developed a spatially distributed reaction-diffusion model solved using finite-element software (COMSOL Multiphysics®).

Main Results:

• The SEAIHR model effectively captured pre-quarantine and post-quarantine epidemic phases.
• Spatial modeling demonstrated the spread dynamics influenced by asymptomatic and infected populations.
• Model sensitivity analyses addressed parameter identifiability and initial conditions.

Conclusions:

• Integrated 0D and spatial models offer robust tools for understanding pandemic spread.
• The models successfully captured both well-mixed and spatially expanding aspects of the COVID-19 outbreak.
• Future refinements can enhance model accuracy and predictive capabilities.