Evidence map›Paper›PMID 34869819›Full record

ArticleOne health (Amsterdam, Netherlands)2021

COVID-19 severity determinants inferred through ecological and epidemiological modeling.

Sofija Markovic, Andjela Rodic, Igor Salom, Ognjen Milicevic, Magdalena Djordjevic, Marko Djordjevic

Abstract read
In one paragraph

Article in One health (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Systems Biology Approaches to Understanding COVID-19 Spread in the Population.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  3. Article
  4. Article
  5. 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.

Sofija MarkovicQuantitative Biology Group, Faculty of Biology, University of Belgrade, Serbia.
Andjela RodicQuantitative Biology Group, Faculty of Biology, University of Belgrade, Serbia.
Igor SalomInstitute of Physics Belgrade, National Institute of the Republic of Serbia, University of Belgrade, Serbia.
Ognjen MilicevicDepartment for Medical Statistics and Informatics, School of Medicine, University of Belgrade, Serbia.
Magdalena DjordjevicInstitute of Physics Belgrade, National Institute of the Republic of Serbia, University of Belgrade, Serbia.
Marko DjordjevicQuantitative Biology Group, Faculty of Biology, University of Belgrade, Serbia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (

Indexed as

COVID-19Disease severityEcological regression analysisEnvironmental factorsEpidemiological modelMachine learning

Identifiers

PMID34869819
PMCPMC8626896

What Socratic 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 Socratic graph.