Evidence map›Paper›PMID 34216635›Full record

ArticleMathematical biosciences2021

Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution.

Martin J Kühn, Daniel Abele, Tanmay Mitra, Wadim Koslow, Majid Abedi, Kathrin Rack, Martin Siggel, Sahamoddin Khailaie, Margrit Klitz, Sebastian Binder and 10 more

Abstract read
In one paragraph

Article in Mathematical biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Mass testing to end the COVID-19 public health threat.The Lancet regional health. Europe · 2023
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. [The contribution of epidemiological models to the description of the outbreak of the COVID-19 pandemic].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2021
    Review
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

20 authors.

Martin J KühnInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany. Electronic address: Martin.Kuehn@DLR.de.
Daniel AbeleInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Tanmay MitraDepartment of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
Wadim KoslowInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Majid AbediDepartment of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
Kathrin RackInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Martin SiggelInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Sahamoddin KhailaieDepartment of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
Margrit KlitzInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Sebastian BinderDepartment of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
Luca SpataroInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Jonas GilgInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Jan KleinertInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Matthias HäberleEarth Observation Center, Department EO Data Science, German Aerospace Center, Weßling, Germany.
Lena PlötzkeInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Christoph D SpinnerTechnical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, Munich, Germany.
Melanie StecherUniversity Hospital of Cologne, Department I for Internal Medicine, University of Cologne; German Center for Infection Research (DZIF), Cologne, Germany.
Xiao Xiang ZhuEarth Observation Center, Department EO Data Science, German Aerospace Center, Weßling, Germany.
Achim BasermannInstitute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany. Electronic address: Achim.Basermann@DLR.de.
Michael Meyer-HermannDepartment of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany. Electronic address: MMH@Theoretical-Biology.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.

Indexed as

Communicable Disease ControlCOVID-19Models, StatisticalSocial Network AnalysisSpatial AnalysisAge FactorsGermanyHumansCoronavirus diseaseCovid-19ForecastMitigationNon-pharmaceutical interventionsSARS-CoV-2

Identifiers

PMID34216635
PMCPMC8243656

What OpenQuestion holds

Textmetadata
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.