Evidence map›Paper›PMID 34328524›Full record

ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2021

[The contribution of epidemiological models to the description of the outbreak of the COVID-19 pandemic].

Viola Priesemann, Michael Meyer-Hermann, Iris Pigeot, Anita Schöbel

Abstract readReview
In one paragraph

Review in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2021. 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

4 authors.

Viola PriesemannMax-Planck-Institut für Dynamik und Selbstorganisation, Am Faßberg 17, 37077, Göttingen, Deutschland. viola.priesemann@ds.mpg.de.
Michael Meyer-HermannHelmholtz-Zentrum für Infektionsforschung, Braunschweig und Technische Universität Braunschweig, Braunschweig, Deutschland.
Iris PigeotLeibniz-Institut für Präventionsforschung und Epidemiologie - BIPS, Bremen, Deutschland.
Anita SchöbelFraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM, Kaiserslautern, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After the global outbreak of the COVID-19 pandemic, an infection dynamic of immense extent developed. Since then, numerous measures have been taken to bring the infection under control. This was very successful in the spring of 2020, while the number of infections rose sharply the following autumn. To predict the occurrence of infections, epidemiological models are used. These are in principle a very valuable tool in pandemic management. However, they still partly need to be based on assumptions regarding the transmission routes and possible drivers of the infection dynamics. Despite numerous individual approaches, systematic epidemiological data are still lacking with which, for example, the effectiveness of individual measures could be quantified. Such information generated in studies is needed to enable reliable predictions regarding the further course of the pandemic. Thereby, the complexity of the models could develop hand in hand with the complexity of the available data. In this article, after delineating two basic classes of models, the contribution of epidemiological models to the assessment of various central aspects of the pandemic, such as the reproduction rate, the number of unreported cases, infection fatality rate, and the consideration of regionality, is shown. Subsequently, the use of the models to quantify the impact of measures and the effects of the "test-trace-isolate" strategy is described. In the concluding discussion, the limitations of such modelling approaches are juxtaposed with their advantages.

Indexed as

COVID-19Models, StatisticalPandemicsGermanyHumansAgent-based modelsCompartmental modelsDark figureInfection fatality rateReproductive number

Identifiers

PMID34328524
PMCPMC8322638

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.