The transmission of covid-19 is dependent on the number of physical encounters between people, the rate of which has varied during the course of the pandemic due to mandated and voluntary social distancing. One way to measure and predict this transmission is to study our mobility, assuming that the more we move, the more people we encounter.
Philip Gerlee and Torbjörn Lundh, Chalmers University of Technology and the University of Gothenburg, have together with several other researchers at universities and university hospitals in Gothenburg, Linköping and Lund compared the number of hospitalised covid-19 patients with mobility data in terms of public transport utilisation and mobile phone usage. This model has been shown to capture the timing of both the first and the beginning of the second wave of the pandemic.
Travel data from regional public transport companies
The comparison with mobile phone data was made for all regions in Sweden and the model turned out to perform somewhat better for larger regions than for smaller, where random effects may have a greater effect. The researchers also received travel data from the regional public transport companies Västtrafik and Skånetrafiken and were able to show that this data provided an even better agreement between model and data.
Since there is a time lag between an increased number of infections and hospital admissions, this model can predict the need for hospital care at a regional level three weeks in advance through the access to local traffic data. The preprint “Predicting regional COVID-19 hospital admissions in Sweden using mobility data” can be accessed via the web site arXiv.
Interview with Philip Gerlee in August about predicting the care need for covid-19 patients >>
Contact information for Philip Gerlee and Torbjörn Lundh >>
Text: Setta Aspström