Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden.
PLoS Comput Biol
; 18(12): e1010767, 2022 12.
Article
in English
| MEDLINE | ID: covidwho-2154217
ABSTRACT
The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Europa
Language:
English
Journal:
PLoS Comput Biol
Journal subject:
Biology
/
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
Journal.pcbi.1010767
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