ArticleJournal of the Royal Statistical Society. Series A, (Statistics in Society)2022
A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales.
Article in Journal of the Royal Statistical Society. Series A, (Statistics in Society), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales.Communications medicine · 2024Article
- Mapping ex ante risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide.One health (Amsterdam, Netherlands) · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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.
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Registered trials
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