As the current COVID-19 pandemic evolves over time, people keep asking "When will it be over?" Modeling or future predictions for this answer are particularly difficult because the scale of the coronavirus is unprecedented in living memory. This is why the use of innovative techniques, such as Monte Carlo simulation, provides a useful framework to be prepared for worst and best-case scenarios that could occur in practice.
In this webinar, we will review a simple model on @RISK that could be applied to understand the spread of a disease like Coronavirus in order to identify the time it takes to observe a reduction in the number of infections within a certain population. The variables that are included in this model are:
• Total population size.
• Initial number of infected individuals.
• Number of people that have contact with an infected person on a daily basis.
• Probability of contagion.
• Probability of acquiring severe conditions after contagion.
• Probability of death.
• Time of full recovery.
Discussion about the most appropriate probability distributions for each case will be held as well as an explanation of advanced features on @RISK 8.0 than can be used to simplify the complexity of this type of modeling.