Use @RISK 8 to understand the spread of disease like Coronavirus

As the current COVID-19 pandemic evolves over time, people keep asking "When will it be over?". It is a question we probably all want to know the answer to and nobody knows for sure as it depends of many factors. In general, estimates are particularly difficult because any modelling or future predictions for coronavirus and the scale of it is unprecedented in living memory. This is why the use of innovative techniques such as Monte Carlo simulation provide a useful framework to be prepared for worst and best 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 modelling.

As the current COVID-19 pandemic evolves over time, people keep asking "When will it be over?". It is a question we probably all want to know the answer to and nobody knows for sure as it depends of many factors. In general, estimates are particularly difficult because any modelling or future predictions for coronavirus and the scale of it is unprecedented in living memory. This is why the use of innovative techniques such as Monte Carlo simulation provide a useful framework to be prepared for worst and best 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 modelling.