Development of forecast models for COVID-19 hospital admissions using anonymized and aggregated mobile network data.

ABSTRACT: Reliable forecast of COVID-19 hospital admissions in near-term horizons can help enable effective resource management which is vital in reducing pressure from healthcare services. The use of mobile network data has come to attention in response to COVID-19 pandemic leveraged on their ability in capturing people social behavior. Crucially, we show that there are latent features in irreversibly anonymized and aggregated mobile network data that carry useful information in relation to the spread of SARS-CoV-2 virus. We describe development of the forecast models using such features for prediction of COVID-19 hospital admissions in near-term horizons (21 days). In a case study, we verified the approach for two hospitals in Sweden, Sahlgrenska University Hospital and Södra Älvsborgs Hospital, w

SUBMITTER: Taghia J 

PROVIDER: S-EPMC9588002 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature