Evidence map›Paper›PMID 36968662›Full record

ArticleProcedia computer science2023

Unsupervised analysis of COVID-19 pandemic evolution in brazilian states: Vaccination Scenario.

Victor Cassão, Domingos Alves, Ana Clara de Andrade Mioto, Mariana Tavares Mozini, Renan Barbieri Segamarchi, Newton Shydeo Brandão Miyoshi

Abstract read
In one paragraph

Article in Procedia computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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.

Victor CassãoSão Carlos School of Engineering, University of São Paulo.
Domingos AlvesRibeirao Preto Medical School, University of São Paulo.
Ana Clara de Andrade MiotoSão Carlos School of Engineering, University of São Paulo.
Mariana Tavares MoziniRibeirao Preto Medical School, University of São Paulo.
Renan Barbieri SegamarchiRibeirao Preto Medical School, University of São Paulo.
Newton Shydeo Brandão MiyoshiRibeirao Preto Medical School, University of São Paulo.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brazil is one of the countries with the worst response against the pandemic scenario of coronavírus. At the beginning we were on average with 4000 deaths in a 24 hours period. In the course of this situation, large amounts of health and medicine datasets were being generated in real time, requiring effective ways to extract information and discover patterns that can help in the fight against this disease. And even more important is to monitor the progress of prophylactic measures and whether they are being effective in reducing the spread of the virus. Thus, the aim of this study is to analyze how the coronavirus has different ways to evolve in each Brazilian state with the influences of the vaccination process. To achieve this goal, the time series Clustering Technique based on a K-Means variation was applied, with the similarity metric Dynamic Time Warping (DTW). We produced this study using the data reported by the Ministry of Health in Brazil, referring to deaths per 100k inhabitants and all vaccination data available. Our results indicate an unevenly occurring vaccination and the need to identify other associated patterns with human development indices and other socio-economic indicators, being this the first analysis developed in the country, under the goals above.

Indexed as

Covid-19, VaccinationDynamic Time WarpingTime Series ClusteringUnsupervised Analysis

Identifiers

PMID36968662
PMCPMC10030184

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.