Development of forecast models for COVID-19 hospital admissions using anonymized and aggregated mobile network data.
Sci Rep
; 12(1): 17726, 2022 Oct 22.
Article
in English
| MEDLINE | ID: covidwho-2087292
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, working closely with the experts engaged in the hospital resource planning. Importantly, the results of the forecast models were used in year 2021 by logisticians at the hospitals as one of the main inputs for their decisions regarding resource management.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
/
Models, Theoretical
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Sci Rep
Year:
2022
Document Type:
Article
Affiliation country:
S41598-022-22350-6
Similar
MEDLINE
...
LILACS
LIS