The Middle East Respiratory Syndrome coronavirus (MERS-CoV) is one of the deadliest and least understood emerging pathogens in the 21st century and continues to pose a threat to global public health because of its high fatality rate [1]. As of January 22, 2016, the World Health Organization (WHO) reported 1,626 laboratory confirmed cases of MERS-CoV, including at least 586 related deaths in 26 countries worldwide [2]. MERS-CoV originates in the coronavirus family, a single-stranded RNA virus with high mutation rates1. MERS-CoV is considered a zoonotic virus that has crossed the species barrier to humans and has been strongly linked to camels in the Arabian Peninsula [3]. Since its outbreak, the WHO issued a worldwide alert, working to educate and organize the surveillance and control of this disease. The novelty of the disease and gaps in knowledge of the epidemiology of this disease create challenges in developing recommendations for diagnosis, treatment, prevention, and control. In an effort to gain a better understanding of the epidemiology of MERS-CoV, this paper presents an attempt to analyze all publically available global surveillance data of MERS-CoV cases reported by WHO to date.

We analyzed all disease outbreak news archives from WHO about MERS-CoV between September 23, 2012 and December 4, 2015 [4,5]. We included all cases that contained data about age and gender (at minimum) and excluded cases reported as clusters without clinical details due to difficulty of merging individual patient data. Data on age, gender, profession, comorbidities, contact with camels or contact with laboratory confirmed MERS-CoV cases, date of symptoms onset, and date of laboratory confirmation were retrieved and analyzed.

Descriptive statistics of the demographic variables are presented and followed by mean comparison tests of observing time between date of symptoms onset and date of laboratory confirmation with age group, gender, profession and presence of comorbidities. Where data was not normally distributed, we used non-parametric Wilcoxon Rank-Sum and Kruskal-Wallis tests using a statistical package software, Stata version 12 (StataCorp^®).

The WHO released 226 outbreak news reports, from which 1006 detailed cases were included. Ages ranged between 10 months and 109 years, with a mean of 52.7 years and a standard deviation (SD) of 18.1 years. Males comprised 68.1% (685) of cases. Comorbidities were reported in 47.1% (474) of cases, with age mean of 60.1 years (SD 15.3 year), and where male 71.7% (340) were male. Healthcare workers reported in 15.5% (156) of cases, with age mean of 38.5 years (SD 11.2 year), and where males comprised 51.9% (81) of cases, of which 19.2% (30) had comorbidities. History of camel contact was reported in 9.8% (99) of cases, with age mean of 56.7 years (SD 15.0 year). Males comprised 91.9% (91) of these cases, of which 77.8% (77) had comorbidities. Contact with laboratory confirmed MERS-CoV cases were reported in 21.6% (217) of cases, with mean age 43.4 years (SD 16.7 years). Males comprised 59.4% (129) of these cases, of which 35.0 % (76) had comorbidities and 30.9% (67) were healthcare workers. Date of symptoms onset and date of laboratory confirmation were reported in 86.4% (869) and 57.6% (581) of cases respectively. The overall mean (SD) of time from symptoms onset to date of laboratory confirmation was 5.2 days (4.2 days), CI 95% [4.9-5.6]. We further stratified the time from symptoms onset to date of laboratory confirmation based on age, gender profession, comorbidities and reporting year (Table 1).

Table 1: Associations of Observing Period from Date of Symptoms Onset to Date of Laboratory Confirmation Based on Age, Gender, Comorbidities, healthcare worker and reporting years.

This study describes the largest sample of confirmed MERS-CoV cases and confirms previous study findings indicating males are predominately affected by this disease [6,7] and is in comparison to Severe Acute Respiratory Syndrome (SARS), where there is a predominance of females in all cases [8]. Healthcare workers were at high risk and accounted for one-sixth of all cases, in comparison to SARS in which healthcare workers accounted for one-fifth of all cases.

It was notable there was a statistically significant shorter observation period from date of symptoms onset to date of laboratory confirmation among female patients and patients not reported to have comorbidities. This may be due to the complexity of patients presenting with comorbidities, in addition to lacking specific symptoms of MERS-CoV. Healthcare workers also have a significantly shorter observation period from date of symptoms onset to date of laboratory confirmation. This may be due to better access to the healthcare system or higher suspicion when symptoms first develop. We could not demonstrate a statistically significant association in the period from date of symptoms onset to date of laboratory confirmation among age groups. However, we did find that the observation period from date of symptoms onset to date of laboratory confirmation was significantly shorter in 2015 compared with 2014. This can be explained by increased awareness about the disease due to public education and time effect.

The general limitations of analyzing outbreak data apply to this study. Specific limitations in this study include the inconsistencies in the format of reporting different variables, the lack of updates on contact tracing results and patient outcome after the initial reporting, and cases that did not specify and define comorbidities.

Our study was based on publically available global surveillance data. Standardized national surveillance and reporting of MERS-CoV would enable better global analysis of this pathogen. Therefore, further analysis of an available MERS-CoV database could provide better understanding and guidance for future research activities.

Additional Contributions: We thank Ms. Najla Alassmi (Department of Emergency Medicine,), Ma. Theresa Fullente, RN (Department of Emergency Medicine,) and ZieYan Law, RN (Intensive care department,) for contributing in data extraction. No compensation was received for these contributions.

Authors have no potential conflicts of interest to disclose.