Predicting regional COVID-19 hospital admissions in Sweden using mobility data.

ABSTRACT: The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations 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 social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regio

SUBMITTER: Gerlee P 

PROVIDER: S-EPMC8683437 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature