Evidence map›Paper›PMID 38702453›Full record

ArticleScientific reports2024

Statistical analysis of three data sources for Covid-19 monitoring in Rhineland-Palatinate, Germany.

Maximilian Pilz, Karl-Heinz Küfer, Jan Mohring, Johanna Münch, Jarosław Wlazło, Neele Leithäuser

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Maximilian PilzFraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany. maximilian.pilz@itwm.fraunhofer.de.
Karl-Heinz KüferFraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany.
Jan MohringFraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany.
Johanna MünchFraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany.
Jarosław WlazłoFraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany.
Neele LeithäuserFraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In Rhineland-Palatinate, Germany, a system of three data sources has been established to track the Covid-19 pandemic. These sources are the number of Covid-19-related hospitalizations, the Covid-19 genecopies in wastewater, and the prevalence derived from a cohort study. This paper presents an extensive comparison of these parameters. It is investigated whether wastewater data and a cohort study can be valid surrogate parameters for the number of hospitalizations and thus serve as predictors for coming Covid-19 waves. We observe that this is possible in general for the cohort study prevalence, while the wastewater data suffer from a too large variability to make quantitative predictions by a purely data-driven approach. However, the wastewater data and the cohort study prevalence are able to detect hospitalizations waves in a qualitative manner. Furthermore, a detailed comparison of different normalization techniques of wastewater data is provided.

Indexed as

COVID-19HospitalizationSARS-CoV-2WastewaterCohort StudiesGermanyHumansInformation SourcesPandemicsPrevalenceWastewater

Identifiers

PMID38702453
PMCPMC11068884

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.