Evidence map›Paper›PMID 36874079›Full record

ArticleApplied soft computing2023

ModInterv COVID-19: An online platform to monitor the evolution of epidemic curves.

Arthur A Brum, Giovani L Vasconcelos, Gerson C Duarte-Filho, Raydonal Ospina, Francisco A G Almeida, Antônio M S Macêdo

Abstract read
In one paragraph

Article in Applied soft computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Arthur A BrumDepartamento de Física, Universidade Federal de Pernambuco, 50670-901 Recife, Pernambuco, Brazil.
Giovani L VasconcelosDepartamento de Física, Universidade Federal do Paraná, 81531-990 Curitiba, Paraná, Brazil.
Gerson C Duarte-FilhoDepartamento de Física - Universidade Federal de Sergipe, 49100-000, São Cristóvão, Sergipe, Brazil.
Raydonal OspinaDepartamento de Estatística, CASTLab, Universidade Federal de Pernambuco, 50740-540, Recife, Pernambuco, Brazil.
Francisco A G AlmeidaDepartamento de Física - Universidade Federal de Sergipe, 49100-000, São Cristóvão, Sergipe, Brazil.
Antônio M S MacêdoDepartamento de Física, Universidade Federal de Pernambuco, 50670-901 Recife, Pernambuco, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present the software ModInterv as an informatics tool to monitor, in an automated and user-friendly manner, the evolution and trend of COVID-19 epidemic curves, both for cases and deaths. The ModInterv software uses parametric generalized growth models, together with LOWESS regression analysis, to fit epidemic curves with multiple waves of infections for countries around the world as well as for states and cities in Brazil and the USA. The software automatically accesses publicly available COVID-19 databases maintained by the Johns Hopkins University (for countries as well as states and cities in the USA) and the Federal University of Viçosa (for states and cities in Brazil). The richness of the implemented models lies in the possibility of quantitatively and reliably detecting the distinct acceleration regimes of the disease. We describe the backend structure of software as well as its practical use. The software helps the user not only to understand the current stage of the epidemic in a chosen location but also to make short term predictions as to how the curves may evolve. The app is freely available on the internet (http://fisica.ufpr.br/modinterv), thus making a sophisticated mathematical analysis of epidemic data readily accessible to any interested user.

Indexed as

COVID-19Epidemic curvesGrowth modelsIntervention strategiesLOWESS regression

Identifiers

PMID36874079
PMCPMC9969754

What OpenQuestion holds

Textmetadata
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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.