Evidence map›Paper›PMID 39075858›Full record

ArticleThe Journal of international medical research2024

COVID-19 trends across borders: Identifying correlations among countries.

Jihan Muhaidat, Aiman Albatayneh

Abstract read
In one paragraph

Article in The Journal of international medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Jihan MuhaidatDepartment of Dermatology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
Aiman AlbataynehDepartment of Energy Engineering, School of Natural Resources Engineering and Management, German Jordanian University, Amman, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo enhance the accuracy of forecasting future coronavirus disease 2019 (COVID-19) cases and trends by identifying and analyzing correlations between the daily case counts of different countries reported between January 2020 and January 2023, to uncover significant links in COVID-19 patterns between nations, allowing for real-time, precise predictions of disease spread based on observed trends in correlated countries.

methodsDaily COVID-19 cases for each country were tracked between January 2020 and January 2023 to identify correlations between nations. Current case data were obtained from reliable sources, such as Johns Hopkins University and the World Health Organization. Data were analyzed in Microsoft Excel using Pearson's correlation coefficient to assess the strength of connections.

resultsStrong correlations (r > 0.80) were revealed between the daily reported COVID-19 case counts of numerous countries across various continents. Specifically, 62 nations showed significant correlations with at least one correlated (connected) country per nation. These correlations indicate a similarity in COVID-19 trends over the past 3 or more years.

conclusionThis study addresses the gap in country-specific correlations within COVID-19 forecasting methodologies. The proposed method offers essential real-time insights to aid effective government and organizational planning in response to the pandemic.

Indexed as

COVID-19SARS-CoV-2ForecastingGlobal HealthHumansPandemicsWorld Health Organizationcorrelated countrycorrelationCOVID-19COVID-19 trendsfuture casesprediction

Identifiers

PMID39075858
PMCPMC11289804

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LicenceCC BY-NC
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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.