ModelsCoronavirus: There’s No One Perfect Model of the Disease
The world is gripped by the COVID-19 pandemic, caused by the spread of a virus called SARS-CoV-2. Since the emergence of this new virus, mathematical modelling has been at the forefront of policy decision-making around the disease. Different models depict different scenarios. Do these seemingly differing findings mean that one model is more accurate than the other? And if so, which one is correct? In truth, credible models developed by respectable research teams are mathematically sound and elegantly answer their posed questions using appropriate data. So more importantly than answering the question “which one is correct?” — we need to understand the differences between the different models and discuss why they come to seemingly different conclusions.
The world is gripped by the COVID-19 pandemic, caused by the spread of a virus called SARS-CoV-2. Since the emergence of this new virus, mathematical modelling has been at the forefront of policy decision-making around the disease.
Mathematical modelling has already been used widely to help make decisions around the control of the COVID-19 spread. For example, the imposed social-distancing measures in the U.K. have been widely attributed to the projected outcomes of the COVID-19 epidemic based on a mathematical model led by Neil Ferguson’s research group at Imperial College London.
Models represent the spread of the virus as a graph with a curved line indicating how many infections there will probably be at any point in time. As the virus spreads, the line curves up until it peaks, and then falls down again as the virus is expected to eventually decline. The Imperial model suggests that the virus is still in the initial, fast-growing part of the epidemic curve.
More recently, another model, developed by the research group of Sunetra Gupta at Oxford University, suggested that “ongoing epidemics in the U.K.… started at least a month before the first reported death” and that the virus has already spread widely across the U.K. population.
Imperial v Oxford: Which Model Is Correct?
Do these seemingly differing findings mean that one model is more accurate than the other? And if so, which one is correct?
In truth, both models are mathematically sound and elegantly answer their posed questions using appropriate data. So more importantly than “which one is correct?”, we need to understand the differences between them and discuss why they come to seemingly different conclusions.
There are three main differences between the Imperial and the Oxford models. One, they use different modelling approaches. Two, they use different key parameters. And three, they pose and answer different questions.
First, the Imperial model is a more complicated mathematical model that tracks the behavior of each individual in the population and is hence called an “individual-based model”. It considers the infectiousness of each person in the population.
In contrast, the Oxford model is a mathematically simpler model that tracks the behavior of different groups of people or “populations” and is therefore called a “population-based model”.
The populations tracked in time are those susceptible (S) to the virus, those infected by the virus (I) and those that recovered from the infection with the virus (R). This SIR model averages the infectiousness across the population.
