Evidence map›Paper›PMID 39569349›Full record

ArticleRoyal Society open science2024

The time between symptom onset and various clinical outcomes: a statistical analysis of MERS-CoV patients in Saudi Arabia.

Yehya M Althobaity, Muhammad H Alkhudaydi, Edward M Hill, Robin N Thompson, Michael J Tildesley

Abstract read
In one paragraph

Article in Royal Society open science, 2024. 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. 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

5 authors.

Yehya M AlthobaityDepartment of Mathematics, Taif University, Taif 11099, Saudi Arabia.ORCID https://orcid.org/0000-0001-9934-381X
Muhammad H AlkhudaydiDepartment of Mathematics, Taif University, Taif 11099, Saudi Arabia.
Edward M HillThe Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.ORCID https://orcid.org/0000-0002-2992-2004
Robin N ThompsonMathematical Institute, University of Oxford, Oxford OX2 6GG, UK.ORCID https://orcid.org/0000-0001-8545-5212
Michael J TildesleyThe Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.ORCID https://orcid.org/0000-0002-6875-7232

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we investigate the impact of demographic characteristics on Middle East respiratory syndrome coronavirus (MERS-CoV) cases in Saudi Arabia, specifically focusing on the time intervals between symptom onset and key events such as hospitalization, case confirmation, reporting and death. We estimate these intervals using data from 2196 cases occurring between June 2012 and January 2020, partitioning the data into four age groups (0-24 years, 25-49 years, 50-74 years and 75-100 years). The duration from symptom onset to hospitalization varies between age cohorts, ranging from 4.03 to 4.75 days, with the 75-100 age group experiencing the longest delay. The interval from symptom onset to case confirmation spans 5.83-8.24 days, and again, the 75-100 age group faces the lengthiest delay. The interval from symptom onset and case reporting ranges from 7.0 to 9.8 days, with the 75-100 age group experiencing the longest delay. The period from symptom onset to death varies across age groups (12.3-16.1 days), with elevated mortality rates during outbreaks. Importantly, we observe age-based differences in the risk of hospitalization and other measures of infection severity, including the probability of death conditional on hospitalization. Careful quantification of epidemiological characteristics, including inference of key epidemiological periods and assessments of differences between cases of different ages, plays a crucial role in understanding the progression of MERS-CoV outbreaks and formulating effective public health strategies to mitigate their impact.

Indexed as

delay distributionmathematical modellingMERS-CoVstatistical analysis

Identifiers

PMID39569349
PMCPMC11576103

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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.