Evidence map›Paper›PMID 36396693›Full record

ArticleScientific data2022

COVID-19 in Switzerland real-time epidemiological analyses powered by EpiGraphHub.

Flávio Codeço Coelho, Eduardo Corrêa Araújo, Olivia Keiser

Abstract read
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Flávio Codeço CoelhoFundação Getulio Vargas, Escola de Matemática Aplicada, Rio de Janeiro, 22250-900, Brazil. flavio.codeco.coelho@fgv.br.ORCID 0000-0003-3868-4391
Eduardo Corrêa AraújoThe Global Research and Analysis for Public Health (GRAPH) Network, Geneva, Switzerland.ORCID 0000-0002-4032-6123
Olivia KeiserThe Global Research and Analysis for Public Health (GRAPH) Network, Geneva, Switzerland.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) 31CA30_196270
6 · The paper itself

Abstract

Here we present the design and results of an analytical pipeline for COVID-19 data for Switzerland. It is applied to openly available data from the beginning of the epidemic in 2020 to the present day (august 2022). We analyzed the spatio-temporal patterns of the spread of SARS-CoV2 throughout the country, applying Bayesian inference to estimate population prevalence and hospitalization ratio. We also developed forecasting models to characterize the transmission dynamics for all the country's cantons taking into account their spatial correlations in COVID incidence. The two-week forecasts of new daily hospitalizations showed good accuracy, as reported herein. These analyses' raw data and live results are available on the open-source EpiGraphHub platform to support further studies.

Indexed as

COVID-19Bayes TheoremHumansRNA, ViralSARS-CoV-2SwitzerlandRNA, Viral

Identifiers

PMID36396693
PMCPMC9669531

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.