Evidence map›Paper›PMID 35346262›Full record

ArticleGlobalization and health2022

Predicting the number of COVID-19 infections and deaths in USA.

Amarachukwu Felix Ebubeogu, Chamberline Ekene Ozigbu, Kholoud Maswadi, Azizi Seixas, Paulinus Ofem, Donaldson F Conserve

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Globalization and health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

6 authors.

Amarachukwu Felix Ebubeogu *Department of Software Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia. felixbosken@hotmail.com.ORCID http://orcid.org/0000-0001-8389-4860
Chamberline Ekene OzigbuDepartment of Health Services Policy and Management, Arnold School of Public, Health, Columbia, 29208, SC, United States.
Kholoud MaswadiDepartment of Management Information Systems, Jazan University, Jazan, 45142, Saudi Arabia.
Azizi SeixasDepartment of Psychiatry and Behavioral Sciences, The University of Miami Miller School of Medicine, Miami, 33136, FL, United States.
Paulinus OfemDepartment of Software Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia.
Donaldson F ConserveDepartment of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, Washington, 20052, United States.

Funding

DORMIR: Determinants, Outcomes, Responses and Markers of Insufficient sleep in Rural-Urban settingsR01HL152453 · NHLBI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI SEIXAS, AZIZI · 2020 to 2024
$3.7M
NHLBI NIH HHS R01 HL152453
6 · The paper itself

Abstract

backgroundUncertainties surrounding the 2019 novel coronavirus (COVID-19) remain a major global health challenge and requires attention. Researchers and medical experts have made remarkable efforts to reduce the number of cases and prevent future outbreaks through vaccines and other measures. However, there is little evidence on how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection entropy can be applied in predicting the possible number of infections and deaths. In addition, more studies on how the COVID-19 infection density contributes to the rise in infections are needed. This study demonstrates how the SARS-COV-2 daily infection entropy can be applied in predicting the number of infections within a given period. In addition, the infection density within a given population attributes to an increase in the number of COVID-19 cases and, consequently, the new variants.

resultsUsing the COVID-19 initial data reported by Johns Hopkins University, World Health Organization (WHO) and Global Initiative on Sharing All Influenza Data (GISAID), the result shows that the original SAR-COV-2 strain has R

conclusionThe proposed technique will contribute to the ongoing investigation of the COVID-19 pandemic and a blueprint to address the uncertainties surrounding the pandemic.

Indexed as

COVID-19Disease OutbreaksGlobal HealthHumansPandemicsSARS-CoV-2United States2019 novel coronavirusCOVID-19Infection densityInfection entropySARS-CoV-2

Identifiers

PMID35346262
PMCPMC8959784

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.