Evidence map›Paper›PMID 36588986›Full record

ArticleNonlinear dynamics2023

Multiple waves of COVID-19: a pathway model approach.

Giovani L Vasconcelos, Nathan L Pessoa, Natan B Silva, Antônio M S Macêdo, Arthur A Brum, Raydonal Ospina, Ugur Tirnakli

Abstract read
In one paragraph

Article in Nonlinear dynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Giovani L VasconcelosDepartamento de Física, Universidade Federal do Paraná, Curitiba, Paraná 81531-980 Brazil.ORCID https://orcid.org/0000-0001-6609-5960
Nathan L PessoaDepartamento de Física, Universidade Federal de Pernambuco, Recife, Pernambuco 50670-901 Brazil.
Natan B SilvaDepartamento de Física, Universidade Federal do Paraná, Curitiba, Paraná 81531-980 Brazil.
Antônio M S MacêdoDepartamento de Física, Universidade Federal de Pernambuco, Recife, Pernambuco 50670-901 Brazil.
Arthur A BrumDepartamento de Física, Universidade Federal de Pernambuco, Recife, Pernambuco 50670-901 Brazil.
Raydonal OspinaDepartamento de Estatística, Universidade Federal de Pernambuco, Recife, Pernambuco 50740-540 Brazil.
Ugur TirnakliDepartment of Physics, Faculty of Arts and Sciences, Izmir University of Economics, 35330 Izmir, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A generalized pathway model, with time-dependent parameters, is applied to describe the mortality curves of the COVID-19 disease for several countries that exhibit multiple waves of infections. The pathway approach adopted here is formulated explicitly in time, in the sense that the model's growth rate for the number of deaths or infections is written as an explicit function of time, rather than in terms of the cumulative quantity itself. This allows for a direct fit of the model to daily data (new deaths or new cases) without the need of any integration. The model is applied to COVID-19 mortality curves for ten selected countries and found to be in very good agreement with the data for all cases considered. From the fitted theoretical curves for a given location, relevant epidemiological information can be extracted, such as the starting and peak dates for each successive wave. It is argued that obtaining reliable estimates for such characteristic points is important for studying the effectiveness of interventions and the possible negative impact of their relaxation, as it allows for a direct comparison of the time of adoption/relaxation of control measures with the peaks and troughs of the epidemic curve.

Indexed as

COVID-19Epidemic waveGrowth modelPublic health

Identifiers

PMID36588986
PMCPMC9786534

What OpenQuestion holds

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Registered trials

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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.