A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time

Area of Science:

• Epidemiology
• Computational Biology
• Mathematical Modeling

Background:

• The global COVID-19 pandemic caused significant mortality and socio-economic disruption.
• Accurate modeling of disease spread is crucial for effective public health interventions.

Purpose of the Study:

• To develop and validate a stochastic point process Monte Carlo simulation model for COVID-19.
• To estimate key epidemiological parameters and predict disease trajectories for Australia and the UK.

Main Methods:

• A stochastic point process modeling approach was employed.
• Monte Carlo simulations were utilized to represent COVID-19 transmission dynamics.
• The model was calibrated and tested using real-world COVID-19 data from Australia and the UK (March 1 - May 1).

Main Results:

• The model estimated COVID-19 peaks around March 29 in Australia (≈1,700 cases) and April 22 in the UK (≈22,860 cases).
• Total confirmed cases were projected to reach 6,790 in Australia (75 days) and 206,480 in the UK (105 days).
• Estimated COVID-19 reproduction numbers aligned with existing literature.

Conclusions:

• The developed simulation model is an effective and adaptable tool for decision-making and "what-if" analyses concerning COVID-19.
• This modeling approach holds potential for application to other infectious diseases in the future.