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Mathematical models can predict the spread of disease

People were advised not to gather in crowded places when the COVID-19 pandemic reached the Nordic countries in early 2020. This included workplaces, eateries, supermarkets, public transport and shopping centres.

Of these five places, where was the risk of infection greatest? A Nordic research project has investigated this. We'll come back to the answer.

Leading the study is Tom Britton, a professor at Stockholm University. He has led a project in which researchers from Norway, Sweden and Finland have investigated how data flows and mathematical modelling can support decision-making during pandemics.

Portrait of Tom Britton
Tom Britton, professor at Stockholm University. Photo: Stockholm University.

"The mathematical modelling is used for scenario descriptions, even though there is a lot of uncertainty associated with it. The scenario descriptions can guide policy makers on what should be done, for example, during a pandemic. This is important to research because the cost of the recent COVID-19 pandemic was astronomical – both financially and in terms of suffering and lost lives. Therefore, any knowledge that can reduce this in a future pandemic is important," says Tom Britton.

The number 1 is crucial when it comes to mathematical contagion modelling. This is because an infectious virus depends on the spread of the disease. If the disease spreads to more than one person on average, the infection rate increases. If it spreads to fewer than one person, it decreases.

The reproduction rate indicates how many people each infected person infects on average. It was important to know the current reproduction rate at all times. If the number was 2, it meant that you had to reduce the infection rate by 50 per cent. The goal is to get the number to be less than 1.

"There are mathematical models that we call microscopic models. They describe how individuals behave on an individual level. Simple mathematical models say that each individual has a certain number of contacts. We observe what happens at the individual, microscopic level and then we analyse it to say something at the macroscopic, population level. This is common to all mathematical modelling of infectious diseases."

Where the risk of infection was highest

Back to the risk of infection at the five locations: workplaces, eateries, supermarkets, public transport and shopping centres. Using Google Mobility Trends, the research project mapped where the risk of infection was highest. The data used by the researchers was mainly from Sweden and Norway. Google Mobility Trends records where people are at a given time using signals from their mobile phones.

Photo: Ricky John Molloy/

The data shows how long people were in certain locations during the pandemic. It turned out that the time people spent in supermarkets decreased when the pandemic hit. The same was the case with cafes and restaurants. When they compared the data with the contagion reports, they found that the greatest contagion took place on public transport and in workplaces.

"The infection was less prevalent in supermarkets, and this may be because people spent less time there than in public transport and workplaces. This is good news because it would be more difficult to prevent people from going to the supermarket to buy food than from using public transport. These results give an indication of where policy makers can take action to reduce the spread of infection. What we have learnt is that if there is a new pandemic with a similar virus, we should make sure that public transport is not overcrowded. Our analysis suggests that people are crowded in public transport," he says.

Photo: Ricky John Molloy/

Nordic collaboration on mathematical modelling is important

In the Nordic countries, there are between 20 and 25 researchers with expertise in mathematical modelling. This compares to around 200 in the UK before the pandemic.

"It's valuable that the Nordic countries are working together on mathematical modelling, because each of us is small in this field. It's essential that we learn from each other. In some areas we are similar and in others we are different, and we saw this during the pandemic when Sweden had fewer restrictions than Norway and Denmark," says Tom Britton.

Are we prepared for a new pandemic?

NordForsk has funded several research projects that shed light on different aspects of the handling of the COVID-19 pandemic in the Nordic region. Here are examples of some of the other projects:


Maria Nilsson

Maria Nilsson

Special Adviser
Marianne Knudsen. Photo: NordForsk

Marianne Knudsen

Senior Communications Adviser