Pandæsim: An Epidemic Spreading Stochastic Simulator.

Area of Science:

• Epidemiology
• Computational Biology
• Mathematical Modeling

Background:

• Traditional epidemic models often simplify environments or ignore inherent randomness.
• Accurate spatial and stochastic modeling is crucial for understanding disease dynamics.

Purpose of the Study:

• To develop and apply a novel stochastic algorithm combined with spatial methods for epidemic modeling.
• To simulate and analyze the COVID-19 epidemic in France, comparing discrete and continuous approaches.

Main Methods:

• A variant of the Gillespie stochastic algorithm was employed.
• The sub-volumes method was integrated for spatial localization.
• The model allows seamless switching between discrete stochastic and continuous deterministic simulations.

Main Results:

• The discrete stochastic simulation (Pandæsim) demonstrated strong correlation with French hospital data for COVID-19, both daily and overall.
• The model successfully incorporated the impact of lockdown measures on epidemic spread.
• Significant behavioral differences between continuous and discrete methods were identified under specific conditions.

Conclusions:

• The developed stochastic discrete simulation approach provides a robust tool for modeling epidemic spreading with high accuracy.
• The findings highlight the importance of considering stochasticity and spatial heterogeneity in epidemic modeling.
• The study offers insights into COVID-19 dynamics in France and the utility of different modeling paradigms.