Game study shows: local measures result in less severe lockdown   

PDPC-scientists and others worked on a model that maps corona dispersion. Their model shows that taking local measures could work. Important, according to Luc Coffeng, as a regional approach can ease the burden of heavy measures in other parts of the country 

Is Brabant going on lockdown? This question was buzzing around at the end of March 2020, when the first Dutch corona outbreak happened in Brabant. We now all know that the entire country went on lockdown – not only Brabant. But scientists kept wondering: What if? What if we had taken local measures? Would that have had the same effect on the spread of the coronavirus?   

To answer that question, assistant professor of infectious disease control and mathematical modeller Luc Coffeng worked on an extensive model with CBS and scientists from Utrecht University.  


Coffeng: ‘Our model is very similar to a game. We simulate all kinds of people living and travelling in the Netherlands. We then throw a dice repeatedly. Sometimes the simulation goes left and sometimes right. But the general picture is: if you throw the affected municipalities under lock and key, other regions do not immediately have to take the same heavy measures. Local measures could therefore reduce the social burden in other regions, compared to a national lockdown.’   

Better safe than sorry   

Good to know in case another corona wave hits. According to Coffeng, that does not mean that the Netherlands handled things poorly in 2020. ‘We did not know anything about the virus then and had to stay cautious. But our study does provide insight into the possibilities. It shows that we can consider a regional approach with good preparation.’   

Two years   

When preparing for pandemics, it can help to have similar models more or less at the ready. ‘It took us two years to build this model,’ says Coffeng. ‘In the future, with new viruses or corona variants, we should be able to assess our options more quickly.’   

The researchers combined mobility and geographic data to create a realistic simulation. For example, they looked at people’s ages, work, and how much and where they travelled. ‘It was an awful lot of work to analyze all that data. You must find the right balance: too much data makes the model slow and unworkable, and too little makes it unrealistic.’   

The researchers self-published this article