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

TL;DR

This study models and predicts regional COVID-19 hospital admissions in Sweden by linking mobility data to transmission dynamics, successfully capturing pandemic waves and outperforming other models in key regions.

Contribution

Introduces a region-specific SEIR model incorporating mobility data to forecast hospital admissions, demonstrating improved prediction accuracy during COVID-19 waves in Sweden.

Findings

Model captures timing of pandemic waves

Public transport data improves predictions in major regions

Mobility data enables future hospital admission forecasts

Abstract

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.