Evidence map›Paper›PMID 37529563›Full record

ArticleJournal of applied statistics2023

Identifying the cycles in COVID-19 infection: the case of Turkey.

Yılmaz Akdi, Yunus Emre Karamanoğlu, Kamil Demirberk Ünlü, Cem Baş

Open access · greenAbstract read
In one paragraph

Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 23% of its field
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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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

4 authors at 3 institutions in 1 country.

Yılmaz AkdiDepartment of Statistics, Faculty of Science, Ankara University, Ankara, Turkey.ORCID https://orcid.org/0000-0003-0188-0970
Yunus Emre KaramanoğluGendarmerie and Coast Guard Academy, Ankara, Turkey.ORCID https://orcid.org/0000-0001-9711-6867
Kamil Demirberk ÜnlüDepartment of Mathematics Ankara, Atilim University, Ankara, Turkey.ORCID https://orcid.org/0000-0002-2393-6691
Cem BaşPrice Statistics Directorate, Turkish Statistical Institute (TURKSTAT), Ankara, Turkey.ORCID https://orcid.org/0000-0003-4488-2562
Ankara University · TRAtilim University · TRPolice Academy · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The new coronavirus disease, called COVID-19, has spread extremely quickly to more than 200 countries since its detection in December 2019 in China. COVID-19 marks the return of a very old and familiar enemy. Throughout human history, disasters such as earthquakes, volcanic eruptions and even wars have not caused more human losses than lethal diseases, which are caused by viruses, bacteria and parasites. The first COVID-19 case was detected in Turkey on 12 March 2020 and researchers have since then attempted to examine periodicity in the number of daily new cases. One of the most curious questions in the pandemic process that affects the whole world is whether there will be a second wave. Such questions can be answered by examining any periodicities in the series of daily cases. Periodic series are frequently seen in many disciplines. An important method based on harmonic regression is the focus of the study. The main aim of this study is to identify the hidden periodic structure of the daily infected cases. Infected case of Turkey is analyzed by using periodogram-based methodology. Our results revealed that there are 4, 5 and 62 days cycles in the daily new cases of Turkey.

Indexed as

C22Corona virusCovid-19harmonic regressionperiodicityTurkey

Identifiers

PMID37529563
PMCPMC10388807
OpenAlexW4210524489

What OpenQuestion holds

Textmetadata
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.