A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales.
J R Stat Soc Ser A Stat Soc
; 185(1): 202-218, 2022 Jan.
Article
in English
| MEDLINE | ID: covidwho-1575364
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
As the COVID-19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID-19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in China, we compared counts of COVID-19 from near-real-time spatially disaggregated data (city level) with fine-spatial scale predictions from a Bayesian downscaling regression model applied to a reference province-level data set. The results highlight discrepancies in the counts of coronavirus-infected cases at the district level and identify districts that may require further investigation.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Language:
English
Journal:
J R Stat Soc Ser A Stat Soc
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS