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

Philip Gerlee,Thomas Brezicka,Anna Jöud,Torbjörn Lundh,Ingrid Fritzell,Toomas Timpka,Armin Spreco,Julia Karlsson

doi:10.1038/s41598-021-03499-y

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 regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.

Highlights

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors
We set out to investigate whether variations in data reflecting local social mixing through weekly commuting rates were associated with later COVID-19 hospitalisation rates
We found that a SEIR-model can be fitted using two free parameters to regional data from Sweden and that COVID-19 hospital admissions can be predicted 3 weeks in advance using time-dependent mobility data

Summary

Introduction

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Obtaining an understanding of the effect of mobility-related social mixing on the transmission of COVID-19 requires an ability to measure and quantify said changes in individual mobility. This was achieved during the early phases of the COVID-19 pandemic by geographically tracking cell phone usage, either directly by mobile phone operators[4] or via usage of Google s ervices[5] that are readily available for many geographic regions. Mobility was measured by considering the utilisation of public transport[6] This type of information has been used in a number of studies in order to model and understand the pandemic. Mobility data has been used in order to draw conclusions about how markets and governments respond to surges in COVID-19 c ases[10], and to obtain a quantification of the impact on reduced mobility on COVID-19 cases and deaths[11], and on the risk of transmission[12]

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