Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing.
Nat Commun
; 12(1): 1501, 2021 03 08.
Article
in English
| MEDLINE | ID: covidwho-1123130
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Basic Reproduction Number
/
Epidemiological Monitoring
/
COVID-19
/
Models, Theoretical
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Nat Commun
Journal subject:
Biology
/
Science
Year:
2021
Document Type:
Article
Affiliation country:
S41467-021-21776-2
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