Evidence map›Paper›PMID 34898863›Full record

ArticleChaos, solitons, and fractals2022

Gompertz model in COVID-19 spreading simulation.

E Pelinovsky, M Kokoulina, A Epifanova, A Kurkin, O Kurkina, M Tang, E Macau, M Kirillin

Abstract read
In one paragraph

Article in Chaos, solitons, and fractals, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Improving outbreak forecasts through model augmentation.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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

8 authors.

E PelinovskyNational Research University - Higher School of Economics, Myasnitskaya st., 20, Moscow 101000, Russian Federation.
M KokoulinaNizhny Novgorod State Technical University n.a. R.E. Alekseev, Minin st., 24, Nizhny Novgorod 603950, Russian Federation.
A EpifanovaNizhny Novgorod State Technical University n.a. R.E. Alekseev, Minin st., 24, Nizhny Novgorod 603950, Russian Federation.
A KurkinNizhny Novgorod State Technical University n.a. R.E. Alekseev, Minin st., 24, Nizhny Novgorod 603950, Russian Federation.
O KurkinaNizhny Novgorod State Technical University n.a. R.E. Alekseev, Minin st., 24, Nizhny Novgorod 603950, Russian Federation.
M TangEast China Normal University, Shanghai 200241, China.
E MacauUniversidade Federal de São Paulo, São Paulo 04021-001, Brazil.
M KirillinInstitute of Applied Physics RAS, Ul'yanov st., 46, Nizhny Novgorod 603950, Russian Federation.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The paper reports on application of the Gompertz model to describe the growth dynamics of COVID-19 cases during the first wave of the pandemic in different countries. Modeling has been performed for 23 countries: Australia, Austria, Belgium, Brazil, Great Britain, Germany, Denmark, Ireland, Spain, Italy, Canada, China, the Netherlands, Norway, Serbia, Turkey, France, Czech Republic, Switzerland, South Korea, USA, Mexico, and Japan. The model parameters are determined by regression analysis based on official World Health Organization data available for these countries. The comparison of the predictions given by the Gompertz model and the simple logistic model (i.e., Verhulst model) is performed allowing to conclude on the higher accuracy of the Gompertz model.

Indexed as

COVID-19Gompertz modelLogistic equationMathematical modeling

Identifiers

PMID34898863
PMCPMC8642157

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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.