  The transmission of COVID-19 is dependent on social contacts, the rate of
which have varied during the pandemic due to mandated and voluntary social
distancing. Changes in transmission dynamics eventually affect hospital
admissions and we have used this connection in order to model and predict
regional hospital admissions in Sweden during the COVID-19 pandemic. We use an
SEIR-model for each region in Sweden in which the infectivity is assumed to
depend on mobility data in terms of public transport utilisation and mobile
phone usage. The results show that the model can capture the timing of the
first and beginning of the second wave of the pandemic. Further, we show that
for two major regions of Sweden models with public transport data outperform
models using mobile phone usage. The model assumes a three week delay from
disease transmission to hospitalisation which makes it possible to use current
mobility data to predict future admissions.
