This report details efforts to use mathematical models that predict the spread of the SARS-CoV-2 virus.
Governments across the world are relying on mathematical projections to help guide decisions in this pandemic. Computer simulations account for only a fraction of the data analyses that modelling teams have performed in the crisis, but they are an increasingly important part of policymaking. But much information about how SARS-CoV-2 spreads is still unknown and must be estimated or assumed, which limits the precision of forecasts.
In order to understand the value of COVID-19 models, it’s crucial to know how they are made and the assumptions on which they are built.
Although many of the models simulating how diseases spread are unique to individual academic groups, the mathematical principles underlying most models are similar. Both equation based SIR models and agent-based models have been used in this pandemic:
SIR models are based around trying to understand how people move between three main states, and how quickly: individuals are either susceptible (S) to the virus; have become infected (I); and then either recover (R) or die. The R group is presumed to be immune to the virus, so can no longer pass on the infection. Sometimes these models include an ‘E’ group of people who have been exposed but are not yet infectious. People with natural immunity would also belong to this group. SIR models have a wide range of complexity.
Agent-based models are similar to equation-based SIR models, but each person can behave differently on a given day or in an identical situation. These models require a lot of data.
The article discusses various strengths and weaknesses of the models.