MAM: Flexible Monte-Carlo Agent based model for modelling COVID-19 spread.

Area of Science:

• Epidemiology
• Computational Biology
• Statistical Physics

Background:

• The COVID-19 pandemic highlighted the critical need for accurate mathematical models to predict disease spread.
• Traditional models like the susceptible-infectious-removed (SIR) model have shown limitations in capturing the complexities of COVID-19 transmission dynamics.
• The dynamic nature of the pandemic, including new variants and interventions, necessitates more adaptable predictive tools.

Purpose of the Study:

• To introduce and evaluate a novel dynamic Monte-Carlo Agent-based Model (MAM) for predicting COVID-19 transmission.
• To compare the predictive performance of MAM against the traditional SIR model using real-world outbreak data.
• To demonstrate the flexibility of MAM in analyzing the impact of interventions like vaccinations and the emergence of new variants.

Main Methods:

• Development of a dynamic Monte-Carlo Agent-based Model (MAM) grounded in statistical physics principles.
• Utilized publicly available aggregative data from three major COVID-19 outbreaks in Israel.
• Comparative analysis of prediction accuracy between the MAM and the SIR model.

Main Results:

• The MAM demonstrated superior performance compared to the SIR model across all evaluated aspects of COVID-19 outbreak prediction.
• The model successfully predicted disease spread patterns during major outbreaks in Israel.
• MAM's flexibility was confirmed through its ability to accurately simulate the effects of vaccination strategies in diverse subgroups and the introduction of new SARS-CoV-2 variants.

Conclusions:

• The dynamic Monte-Carlo Agent-based Model (MAM) offers a significant advancement over the SIR model for COVID-19 pandemic prediction.
• MAM provides a robust and flexible framework for understanding and forecasting infectious disease dynamics, including the impact of public health interventions and viral evolution.
• This model can be a valuable tool for public health decision-making during evolving pandemics.