Evidence map›Paper›PMID 34094532›Full record

ArticleAnnals of medicine and surgery (2012)2021

Understanding the cycles of COVID-19 incidence: Principal Component Analysis and interaction of biological and socio-economic factors.

Pablo Duarte, Efrain Riveros-Perez

Abstract read
In one paragraph

Article in Annals of medicine and surgery (2012), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

2 authors.

Pablo DuarteFlossbach von Storch Research Institute, Germany.
Efrain Riveros-PerezMedical College of Georgia, Department of Anesthesiology and Perioperative Medicine, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incidence curve of coronavirus disease 19 (COVID-19) shows cyclical patterns over time. We examine the cyclical properties of the incidence curves in various countries and use principal components analysis to shed light on the underlying dynamics that are common to all countries. We find that the cyclical series of 37 countries can be summarized in four principal components which explain over 90% of the variation. We also discuss the influence of complex interactions between biological viral natural history and socio-political reactions and measures adopted by different countries on the cyclical patterns exhibited by COVID-19 around the globe.

Indexed as

COVID-19Epidemiological dataPredictive modelPrincipal component analysisViral spread

Identifiers

PMID34094532
PMCPMC8168336

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.