Carried out as a joint project involving Finnish, Swedish and Norwegian researchers, the model also takes into account network structures and human mobility.
The NordicMathCovid project aims to model corona and future epidemics more extensively than has been previously attempted. It also builds towards long-term cooperation in mathematical modelling and extensive collection of health data.
“’One of the purposes of the project is to compare different corona models and scenarios in different countries. For example, we can apply Swedish figures to conditions in Finland and Norway or see what would have happened if Sweden had acted differently,’ says Professor Lasse Leskelä from Aalto University.
Traditional epidemic modelling does not take into account the network structure, geographical location or human mobility. Modern network theory provides computational methods for modelling population contact structures, which is needed in order to assess, for example, the contribution of school closures towards slowing down the epidemic.
‘We are studying large populations. We do not assume that individuals are associated to each other on an entirely random basis; instead, we apply knowledge about how social networks are usually shaped: some people, such as superspreaders, have more contacts than others. In addition, social networks are clustered, which means that the connections are interlaced,’ explains Professor Mikko Kivelä.
The large variations in contacts, mobility and social activity in different population groups have a significant impact on the spread of the epidemic and the formation of immunity. In order to understand these phenomena, the project will develop new stochastic models.
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