Predicting regional COVID-19 hospital admissions in Sweden using mobility data.
Sci Rep
; 11(1): 24171, 2021 12 17.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1593554
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.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Admisión del Paciente
/
Algoritmos
/
Teléfono Celular
/
COVID-19
/
Hospitalización
/
Modelos Teóricos
Tipo de estudio:
Estudio observacional
/
Estudio pronóstico
Límite:
Humanos
País/Región como asunto:
Europa
Idioma:
Inglés
Revista:
Sci Rep
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
S41598-021-03499-y
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