tetano
Editor, Senior Moderator
R Soc Open Sci
. 2024 Nov 20;11(11):240094.
doi: 10.1098/rsos.240094. eCollection 2024 Nov. The time between symptom onset and various clinical outcomes: a statistical analysis of MERS-CoV patients in Saudi Arabia
Yehya M Althobaity[SUP] 1 [/SUP], Muhammad H Alkhudaydi[SUP] 1 [/SUP], Edward M Hill[SUP] 2 [/SUP], Robin N Thompson[SUP] 3 [/SUP], Michael J Tildesley[SUP] 2 [/SUP]
Affiliations
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.
Keywords: MERS-CoV; delay distribution; mathematical modelling; statistical analysis.
. 2024 Nov 20;11(11):240094.
doi: 10.1098/rsos.240094. eCollection 2024 Nov. The time between symptom onset and various clinical outcomes: a statistical analysis of MERS-CoV patients in Saudi Arabia
Yehya M Althobaity[SUP] 1 [/SUP], Muhammad H Alkhudaydi[SUP] 1 [/SUP], Edward M Hill[SUP] 2 [/SUP], Robin N Thompson[SUP] 3 [/SUP], Michael J Tildesley[SUP] 2 [/SUP]
Affiliations
- PMID: 39569349
- PMCID: PMC11576103
- DOI: 10.1098/rsos.240094
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.
Keywords: MERS-CoV; delay distribution; mathematical modelling; statistical analysis.