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

The coronavirus disease 2019 (COVID-19) has now spread throughout most countries in the world causing heavy life losses and damaging social-economic impacts. Following a stochastic point process modelling approach, a Monte Carlo simulation model was developed to represent the COVID-19 spread dynamics. First the simulation study was to examine various expected properties of the simulation model performance based on a number of arbitrarily defined scenarios. Then the simulation studies were performed in analysis of the real COVID-19 data reported for Australia and United Kingdom (UK). Given the initial number of active cases before 1 March were around 10 for both countries, the model estimated that the number of active COVID-19 cases was to peak around 30 March in Australia (≈ 1630 cases) and around 11 April in UK (≈ 24600 cases); ultimately the total confirmed cases could sum to 6610 for Australia in about 70 days and 136000 for UK in about 90 days. The analysis results also confirmed the reproduction number ranges as reported in the literature. This simulation model was considered as an effective and adaptable decision making/what-if analysis tool in battling COVID-19 in the immediate need, and in battling any other infectious diseases in the future.