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The data for the last 4 years were collected form the King Abdulaziz medical city, Riyadh, KSA. Further data were collected from WHO S1 Table and also from ministry of health portals of the GCC countries as follows: Bahrain http://www.moh.gov.bh/, Kuwait www.moh.gov.kw, Oman www.moh.gov.om, Qatar www.moph.gov.qa, United Arab Emirates http://www.moh.gov.ae/, and Saudi Arabia http://www.moh.gov.sa/. We also consulted the GCC countries’ reports and websites for the incidence of MERS-CoV infections between June 2012 and July 2016. Furthermore, we searched PubMed database for articles form GCC countries reporting MERS-CoV infections.
Epidemiological data including age, sex, symptoms, date of onset, and date of sampling were collected and entered into Excel worksheets. Descriptive analysis, frequencies and percentages were calculated using SPSS vr. 20 statistical software.
Data, which included epidemiological and laboratory diagnostic test results, were retrieved from SARI surveillance database of the Ministry of Public Health in Qatar. Initially, we checked and cleaned the datasheets from errors and duplicates before conducting any analysis. To estimate accurately the rates of infections, subjects who had similar test results within 14 days were considered as duplicates. We conducted a descriptive statistical analysis to analyze the demographic and clinical characteristics of the study subjects; and to identify the frequency, patterns, and seasonality of all viruses. The rate of infection of each virus was calculated as the proportion of positive specimens for that particular virus out of the total tested samples per month or year. All samples (n = 43,597) were tested for influenza viruses, while 37,929 samples were subjected for the detection of influenza and other respiratory pathogens as described above. Accordingly, the rates of influenza infections were calculated out of 43,597, while infection rates of other respiratory pathogens were calculated out of 37,929. To determine the relation significance between categorical variables, Pearson chi-square or Fisher Exact test were used. Results were considered statistically significant at p-value < 0.05.
The study included analysis of data obtained from ILI national surveillance system without patients’ identification information. The study was approved by Qatar University, MOPH as well as HMC-MRC: approval # 16335/16.
Between June 2012 and July 2016, a total of 1797 confirmed MERS-CoV cases were reported worldwide with a mortality rate of 38.2% (n = 687). The regional distributions of MERS-CoV were as follows: Middle East had the highest number cases (88.4%), followed by Asia (10.7%), Europe (0.8%) and USA with only 2 cases officially reported (0.1%) The data are summarized in Table 1 and Fig 1A.
We analyzed the spatiotemporal clustering of the MERS-CoV incidence in Saudi Arabia between 2012 and 2019 at the city level by using Kulldorff’s spatial scan statistics via SaTScan 9.6. We used purely temporal, seasonal, purely spatial and spatiotemporal retrospective analyses to scan, detect and evaluate the periods and geographical areas with the highest MERS-CoV risk incidence clusters. The purely spatial scan statistic is defined by a circular window on the map. The window is sequentially centered on each of several possible cities that are positioned throughout the study area. The spatiotemporal scan statistic imposes a cylindrical window with a circular geographic base and height corresponding to time. The temporal scan statistic uses a window that moves in one dimension, time, defined in the same way as the height of the cylinder used by the spatiotemporal scan statistic. The key feature that distinguishes the seasonal scan statistic from the purely temporal scan statistic is that the former ignores the year of the observation and retains only the day and month. The number of MERS-CoV cases by city was used as the case file, the city population estimated from the 2010 census was used as the population file and the latitude and longitude of each city were used in the coordinates file.
In SaTScan, an analysis was conducted by progressively scanning a window across time and/or space through a comparison of the number of observed and expected cases of MERS-CoV incidence, assuming random distribution, inside the window at each city. The null hypothesis is that the incidence of MERS-CoV is randomly distributed, and the alternative hypothesis is that the incidence increases more inside the window than in areas outside it. The log likelihood ratio (LLR) is the hypothesis-testing statistic estimated based on Monte Carlo randomization. The window with the maximum likelihood ratio is the most likely cluster; that is, it identifies the cluster that is least likely to occur by chance. In addition to the most likely cluster, SaTScan also designates secondary clusters for purely spatial and spatiotemporal analyses and ranks them in relation to their estimated LLR statistic. SaTScan scans for clusters by using different criteria; the criterion recommended by SaTScan is the percentage of the population at risk, with a value of 50%. We tested the percentage of the population at risk from 10% to 50%, and from the result, 30% performed best; that is, the value of 30% did not include neighboring cities that have a non-elevated risk. The four types of analyses (purely temporal, seasonal, purely spatial and spatiotemporal) were conducted using the Poisson discrete-based model with 999 Monte Carlo permutations to test for statistical significance. Only clusters with significance levels of 0.05 and only scans of cities with high rates were reported. For temporal analysis, values of 1 day, 1 month and 1 year were set as the time aggregation units for the daily, monthly and annual clusters, respectively, whereas for the seasonal cluster, 1 month was set. For spatiotemporal analysis, 1 month and 1 year were set for the monthly and annual spatiotemporal analyses, respectively.
An in-house anti-MERS-CoV IgG ELISA kit was developed based on the purified spike protein receptor binding domain. This highly sensitive and specific ELISA was previously validated for use with samples from camel and human (Zohaib et al.2018). This anti-MERS-CoV IgG ELISA was used with minor modifications. Camel samples were tested at 1:20 dilution and goat anti-camel IgG-horseradish peroxidase conjugate (Alpha Diagnostic International, San Antonio, TX, USA) was used as the secondary antibody at 1:3000. Based on the microneutralization test, a cut-off value of 0.35 was determined. For human samples, plasma was tested at a dilution of 1:20 and anti-human IgG-horseradish peroxidase conjugated monoclonal antibody (Kyab Biotech Co., Ltd, Wuhan, China) was used as the secondary antibody at 1:15000.
The prevalence of intrinsic variance instability in estimating incidence rates as a result of the variation in populations across spatial units, which can possibly identify outliers, has received broad attention in the disease mapping field. To address this issue, we used GeoDa software for generating EB smoothed rate maps for MERS-CoV incidence. The number of MERS-CoV incidence cases for the governorates was used as an event variable, and the populations of governorates were estimated from the 2010 census and used as base variables.
Specimens were shipped on dry ice to the University of Hong Kong. Serum samples were tested for MERS-CoV antibodies at a screening dilution of 1:20 using an extensively validated MERS-CoV (strain EMC) spike pseudoparticle neutralisation test. Selected positive sera were confirmed using microneutralisation tests in biosafety level (BSL)3 containment. Total nucleic acid was extracted from swab samples using the EasyMag (Biomerieux) system and tested for the presence of MERS-CoV RNA using the upstream of the envelope gene (UpE) reverse transcription-quantitative PCR (RT-qPCR) hydrolysis probe assay. All positive specimens were confirmed by a second RT-qPCR assay targeting the open reading frame (ORF) 1a region of the genome as previously described.
Robust laboratory-based real-time RT-PCR diagnostic tools were described immediately after MERS-CoV was discovered and remain reliable. Several different research-based antibody detection protocols have also been reported for human and animal studies. Molecular and serological kits are commercially available. While current molecular methods are rapid, there are many steps that combine to delay publication of a final test result; from the initial decision to request a MERS-CoV test, to sample collection, nucleic acid extraction, PCR, additional sampling, repeat testing, and reporting processes. Molecular, rapid, and sensitive point-of-care tests (POCTs) are not available but would help support infection control in healthcare settings.
The World Health Organisation recommends testing of appropriate samples for MERS-CoV RNA using real-time RT-PCR methods with subgenomic sequencing to confirm screening results, as necessary. Repeat testing is often required. Virus isolation by culture is not a recommended tool. Detecting antibodies against MERS-CoV may be useful to identify a seroconversion that can define a probable case when confirmation by RT-PCR has been unsuccessful or impossible.
Mild and subclinical MERS cases are reported among younger people including healthcare workers and children. It remains unclear what proportion of MERS-CoV infections are truly subclinical after one study found many were initially incorrectly classified. Difficulty identifying a useful diagnostic antibody response in mild and subclinical disease means seroprevalence studies likely under-report the history of MERS-CoV; MERS-CoV specific CD8+ testing may be helpful. The usefulness of serology needs clarification.
Human sera were tested for MERS-CoV IgG antibodies using a MERS-CoV S1 spike ELISA (EI 2604–9601 G kit, Euroimmun, Lübeck, Germany) according to the manufacturer’s instructions, at the Medical Virology and BSL-3 Laboratory at Institut Pasteur du Maroc, Casablanca, Morocco. The extinction value of the calibrator included in the test defines the upper limit of the reference range in non-infected humans and this value was set as the cut-off. The ELISA was made semi-quantitative by calculating the ratio of the extinction value of the serum sample over the extinction value of the calibrator. The manufacturer recommends cut-off ratios of < 0.8 be interpreted as negative, ≥ 0.8 and < 1.1 as borderline, and ≥ 1.1 as positive. Because subsequent publications suggested a lower ELISA cut-off of ratio ≥ 0.3 for screening purposes for the selection of sera to be confirmed by neutralisation tests, we have also included ELISA optical density (OD) ratios of ≥ 0.3 in our analysis.
All sera were also screened in triplicate in a MERS-CoV pseudoparticle neutralisation test (ppNT) as described previously. All sera positive at a ppNT screening dilution of 1:10 were titrated to end-point in the ppNT assay, as well as in a plaque reduction neutralisation test (PRNT) conducted in BSL-3 containment. The end-point for the ppNT assay was the highest serum dilution giving a ≥ 90% reduction in the luciferase signal compared with negative control. The end-point in the PRNT was the highest serum dilution that gave ≥ 50% (PRNT50) or ≥ 90% (PRNT90) reduction of virus plaques compared with control. The methods have been described elsewhere.
Sera-positive at a titre of ≥ 1:20 in ppNT and ≥ 1:10 in PRNT90 assays were regarded as positive. Sera-positive at a titre of ≥ 1:20 in ppNT and ≥ 1:10 in PRNT50, but negative in PRNT90 assays were regarded as a borderline positive neutralisation result. All other sera were regarded as negative.
Camel’s function and sex were strongly associated with each other (Table 2 and Figure 1B–D) (CrV = 0.86), as were region and lifestyle (CrV = 0.78); herd category and region (CrV = 0.70); region and function (CrV = 0.61); herd category and country (CrV = 0.50); function and lifestyle (CrV = 0.41). The strongest association was between region and type of specimens (i.e. farm or abattoir) with a Cramer’s V equal to 1. Region and age were also slightly collinear with a R2 of 0.20.
Total nucleic acid was extracted from camel nasal swabs using EasyMag (Biomerieux, France) and screened for MERS-CoV RNA using a reverse-transcription qPCR (RT-qPCR) assay targeting the upstream elements of the Envelope (UpE) gene. Positive samples were confirmed by testing with a second RT-qPCR targeting the open reading frame 1a (ORF1a) gene.
Human sera were tested for MERS-CoV antibody using a MERS-CoV S1 spike enzyme-linked immunosorbent assay (ELISA; Euroimmun, Lübeck, Germany) according to manufacturer’s instructions and by a pseudoparticle neutralisation (ppNT) assay as described previously. A ≥ 90% reduction of signal was considered as evidence of neutralisation in the ppNT assay.
The contact investigation was carried out for both family contacts and healthcare worker contacts. Among the family contacts, 7 out of 36 (19.4%) tested positive and 1 of 51 (2%) healthcare worker contacts tested positive for MERS-CoV (p = 0.0078).
The microneutralization assay was used to determine antibodies against MERS-CoV. The assay was conducted as previously described and positive and negative camel antisera were included in all runs.14 Results were only accepted if results from the positive and negative antisera were as expected. Test samples (milk and sera) were initially screened at a dilution of 1:10 in duplicate. Any sample that had a positive result when screened was then repeated in duplicate to obtain the end-point titer that was expressed as the reciprocal of the dilution that provided complete neutralization. Titers ≥1:20 were considered positive.
A microneutralization assay was performed as described previously (Perera et al. 2013). Briefly, Vero B4 cells were seeded into 96-well plates. Plasma samples were incubated at 56 °C for 1 h. MERS-CoV (EMC strain) was diluted with DMEM to 100 TCID50/50 μL. The plasma samples were diluted by twofold in DMEM and incubated with MERS-CoV at 37 °C for 30 min. The medium was removed from the cells and 50 μL virus-plasma mixture was added. The virus-plasma mixture was removed after 1 h and 100 μL DMEM plus 2% fetal bovine serum (FBS) and 1% penicillin/streptomycin was added. The cells were incubated at 37 °C with 5% CO2, and the cytopathic effect (CPE) was observed and recorded at 4 days post-infection. Samples that inhibited CPE at a dilution of 1:20 were considered as positive.
We tested 479 human sera for anti-MERS-CoV S1 IgG antibodies by ELISA. Using the ELISA kit recommended cut-offs, 20 sera (4.2%) were reactive with OD ratio of ≥ 1.1 and 21 (4.4%) were borderline reactive with OD ratio of ≥ 0.8 to < 1.1 (Table 2). Using the lower screening cut-off recommended by Muller et al., 173 additional sera would be regarded as suspected positives requiring testing by a neutralisation test. We tested all 479 sera irrespective of ELISA results for MERS-CoV neutralising activity using the ppNT assay; three (0.6%) were positive at a ppNT titre of ≥ 1:20 while one (0.2%) was positive at a ppNT titre of 1:10 (Table 3). Two of these were positive by ELISA (OD ratio cut-off ≥ 1.1), one borderline (OD ratio 0.83) and the other negative (OD ratio of 0.47) as per kit instructions, but would be recommended for confirmatory testing in the algorithm used by Muller et al.. Of these four ppNT-positive sera at a dilution of 1:10, three (0.6%) were confirmed with PRNT90 reactivity, regarded as confirmed neutralising sera, while the one with a ppNT 1:10 and ELISA OD ratio of 0.83 reduced plaque numbers by between 50% to 90%, i.e. positive in PRNT50 but not PRNT90, and regarded as borderline neutralising in the confirmatory test (Table 2).
The 200 sera from Hong Kong serving as negative controls were all negative in PRNT50 and PRNT90 assays. Validation of the ppNT test with 528 negative control sera has been previously reported.
Of the 41 sera that were positive or borderline by ELISA, i.e had an OD ratio ≥ 0.8, 38 were negative by ppNT. The other 438 sera were negative by both tests. There was a significant association between the results of the two tests (chi-squared with Yates correction: 14.9; p = 0.0001). However, the scatterplot between the ELISA and ppNT assays did not reveal a high level of correlation (correlation coefficient R-value: 0.13; 95% CI: 0.039–0.22) (Figure). If we consider that sera positive in the screening ppNT assay and confirmed by PRNT90 as true MERS-CoV positive sera, ELISA with the cut off recommended by the manufacturer had a sensitivity of 66.7% and a positive predictive value (PPV) of 10%. The serum with borderline PRNT activity was also borderline in ELISA reactivity. If the lower OD ratio of ≥ 0.3 is used as the cut-off for selecting sera for confirmatory testing as recommended by Mueller et al., then the sensitivity of the ELISA for screening for sera subsequently confirmed as positive or borderline neutralising positive was 100% but the PPV was only 0.19%.
Nine (6.6%) of 137 slaughterhouse workers, three (1.9%) of 156 camel herders and eight (4.3%) of 186 people from the general population were MERS-CoV antibody-positive in the ELISA test, using the ELISA kit recommended cut-off values (Table 2). Three (2.2%) of 137 slaughterhouse workers, none of 156 camel herders and one (0.5%) of 186 people from the general population were MERS-CoV neutralising antibody positive by ppNT assay. All four MERS-CoV neutralising antibody-positive sera also reduced virus plaque numbers by ≥ 50%; three of them reduced plaque counts by ≥ 90% (Table 2). There was no statistically significant association between exposure groups and MERS-CoV seropositivity by either ELISA or neutralisation tests. It should be noted all groups were resident in camel herding areas likely had some exposures to camels or camel products.
The WHO testing algorithm for MERS-CoV was implemented.19 Viral RNA was extracted from nasal, rectal, milk and urine samples using QIAmp viral RNA minikit (Qiagen, Dusseldorf, Germany). RNA was then tested for the presence of MERS-CoV RNA using the upE real-time RT-PCR assay as described previously.20 Samples testing positive by the upE assay regardless of CT-value were then confirmed by at least one other RT-PCR assay including the ORF1a, RdRpseq and Nseq assays as described previously.21
Sanger sequencing was performed as part of the confirmatory RdRpseq and Nseq assays. Partial Spike protein gene sequences for genotyping were also obtained for 21 viruses (1 from a local camel and 20 from imported camels) from nasal swabs as per a previously published protocol.22 These 21 sequences were submitted to GenBank under accession numbers KU942355-KU942375. Phylogenetic tree of the partial Spike protein gene (around 600 bp) was constructed using MEGA 6 with bootstrap method and Kimura 2-parameter model.23
This was a retrospective chart review of confirmed cases of MERS-CoV in King Saud University Medical City, Riyadh, Saudi Arabia. The review covered all cases of MERS from January 2015 to October 2018. All confirmed cases of MERS-CoV that were diagnosed in the hospital or anywhere from the kingdom and were transferred to the hospital were collected. The collected data included sociodemographic characteristics (e.g., age, gender, marital status, nationality, employment in health care sector, and geographic region) as well as medical information (e.g., comorbidities, length of hospital stay, presenting symptoms, exposure to camels or their products or to a MERS case during the preceding 2 weeks, recent (2 weeks) hospitalization, and having H1N1 influenza prior to the admission).
Micro-costing was used to estimate the cost of hospitalization for each patient. These expenses were then used to calculate the mean cost for all of the confirmed MERS cases. The lowest, the highest, and the mean cost were used to project the direct medical cost of all MERS cases in the Kingdom. The information regarding items to be included in the cost analysis was retrieved from electronic medical records.
The expenses composed of the cost of personal protective equipment (e.g., N95 masks, gowns, protective eyewear), intravenous fluids and medications (e.g., antivirals, antibiotics, and other prescription medications), laboratory and diagnostic tests (e.g., CBC, liver and cardiac enzymes, swabs, cultures, chest X-rays and CT scans), room fees (e.g., isolation rooms, intensive care unit, extended care rooms), and health care professionals (physicians and nurses) expenses based on the ministry of health hourly rates. The inpatient costs were obtained from the cost center at the Saudi Arabian Ministry of Health, and the medication costs were obtained from the online drug database of the Saudi Food and Drug Authority (SFDA). The costs in Saudi riyals were converted to US dollars using the exchange rate of 1 USD=3.75 SAR.
The study was approved by the institutional review board of the College of Medicine at King Saud University, Riyadh, Saudi Arabia. The data are presented using descriptive statistics (mean, frequencies, and percentages). All analyses were conducted using the SAS statistical software (version 9.2, SAS Institute Inc., Cary, NC, USA).
The uncertainty of pathogen transmission, lack of vaccine against MERS-CoV and deficit in MERS specific treatments make public health interventions challenging to design. With ease of international travel, the possibility of MERS spread is present in all nations. Notably, countries without MERS endemic are unfamiliar with the infectious agent that may be imported by travelers and are, therefore, particularly vulnerable.
In this paper we reviewed epidemiological contact tracing information from public health agencies and peer-reviewed literature, in order to see geographic and temporal distribution of MERS cases around the globe. Concurrently, we used genetic sequences to infer transmission dynamics and inter-host evolution of MERS-CoV. The combined analysis was used to present a phylodynamic picture, detailing international, zoonotic and healthcare associated transmissions at genetic and population levels. These analyses can be used to understand pathogen spread and to implement public health measures to curb a pandemic.
In one study investigating experimental infection of camels, MERS-CoV shedding started 1–2 days post-infection (dpi). In that study, infectious virus could be detected until 7 dpi, and viral RNA until 35 dpi in nasal swab samples and, in lower amounts, in oral swab samples. No infectious virus or viral RNA was detected in faecal or urine samples. Viral RNA detection in nasal, but also rectal swabs of camels after experimental infection until day 14, has been confirmed in a recent vaccine study.
In the field surveys included in this review, MERS-CoV RNA has been described in rectal swab samples, although other field studies report negative results [3, 22–24] and when viral RNA can be detected, the positivity rate of rectal swabs is lower compared with nasal swab samples [19, 25–27]. Oral swabs are usually negative or show a lower positivity rate even when nasal swabs test positive for MERS-CoV RNA [3, 19, 26]. Some studies have reported MERS-CoV RNA in milk samples [27, 28]. Longitudinal studies of camel herds show that PCR results of nasal swabs can remain positive after 2 weeks [27, 29]. When an interval of sampling of 1 or 2 months was maintained, nasal swabs become negative for viral RNA in the next sampling round [24, 30].
MERS-CoV infections have also been detected in camels with MERS-CoV antibodies, both in calves with maternal antibodies as well as older camels that had already acquired antibodies from a previous infection. However, virus replication and thus the virus load is generally lower in infected seropositive animals compared with seronegative camels [19, 21, 23, 24, 30, 31].
Little is known about the longevity of antibody titres after infection from longitudinal studies. A study following camels on a closed farm found that neutralizing antibodies remained consistent during a year, while other studies found that antibody titres rapidly drop by 1–4-fold within a period often as short as 2 weeks [24, 27].
Case data reported to WHO includes information on exposures during the 14 days before MERS symptom onset or when laboratory confirmation was reported (i.e., travel history, contact with confirmed or probable human MERS case, any contact with dromedaries, recent healthcare facility visits). Direct exposure to dromedaries was defined as physical contact (e.g., touching, feeding, cleaning, slaughtering, milking, assisting with birth of camelids, or other activities involving physical contact with dromedaries) in the 14 days before symptom onset or when laboratory confirmation was reported. Indirect exposure to dromedaries is defined as visiting camel areas (e.g., markets, racing tracks, farms) without directly touching a camel, or as consumption of dromedary products (e.g., raw/unpasteurized dromedary milk, raw or undercooked dromedary meat, or other products derived from dromedaries, including urine) in the 14 days before symptom onset, or when laboratory confirmation was reported. Any contact with dromedaries is defined as any direct or indirect contact. Direct and indirect contacts were not considered mutually exclusive for the purposes of this analysis as MERS cases may have reported both or either direct and indirect contact with dromedaries prior to symptom onset or laboratory confirmation of infection.
This retrospective analysis was carried out in the infectious diseases departments of two university hospitals (Bichat Claude Bernard and Pitié-Salpêtrière) in Paris, France. Both departments are part of the Paris/Ile de France regional plan for the management of contagious emerging infectious diseases in adults. They serve as referral centers for emerging infectious diseases in the Paris area and have isolation wards and dedicated rooms with anterooms and negative pressure. We enrolled patients who had been admitted to these two wards after being classified as possible cases of MERS-CoV infection, as defined by the WHO epidemiological bulletins.
In hospitalized patients, epidemiological data were collected: demographic characteristics, travel history, purpose of the travel, contact with animals or sick people and inpatient or outpatient visits during the travel. Also recorded were the nature of the initial symptoms, and the lag time between symptom onset and both the date of departure from the at-risk region, and hospitalization in an isolation ward.
Upon admission to the isolation wards, clinical symptoms and comorbidities were studied, the initial laboratory findings were assessed, and chest X-ray was defined as normal or showing alveolar and interstitial infiltrates.
Clinical management was evaluated in terms of the place of hospitalization, antibiotic treatment, antiviral use and oxygen administration. Microbiological parameters recorded included all bacteriological and virological tests performed during the hospital stay of the patient. Nasopharyngeal specimens were collected for real-time RT-PCR analysis. Respiratory specimens were obtained as soon as possible during the course of the illness (within 21 days after symptom onset). Laboratory confirmation of MERS-CoV infection has been performed on site since 2013, during the opening hours of the local laboratories and in the Pasteur Institute reference laboratory at night and weekends. Confirmation was performed by specific real-time RT-PCR assay, using two different MERS-CoV genomic target sites, the region upstream of the envelope gene and the site of ORF1. Film Array Rapid multiplex PCR was performed for simultaneous qualitative detection and identification of multiple respiratory viral and bacterial nucleic acids in nasopharyngeal swabs (FilmArray® Respiratory Panel Biomérieux Lyon France): adenovirus, coronaviruses, human metapneumovirus, influenza A and B viruses, parainfluenza viruses, respiratory A and B viruses, Bordetella pertussis, Chlamydophila pneumoniae, and Mycoplasma pneumoniae. Bacteria were documented using diverse methods including blood cultures, serology, urinary antigens, sputum and pulmonary samples, respectively. Blood smear for malaria was performed in at-risk travelers. Isolation was maintained until a negative result was obtained for MERS-CoV if the symptoms were more than 4 days old (otherwise a second sample was needed). Duration of isolation and total duration of hospitalization were recorded.
Comparisons were performed using Kruskal-Wallis tests for continuous variables and Pearson’s χ2 or Fisher’s exact test for categorical variables. Statistical analysis was performed using R (v3.2.0, Vienna, Austria); significance was set at a p-value < 0.05.
According to the French Health Public Law (CSP Art L1121–1.1), such an investigation does not require specific informed consent or ethics committee approval because it is a retrospective study without medical intervention.
A chi-squared test was used to assess the associations of comorbidities and clinical presentations, with p values <0.05 considered significant.
The findings of this study highlight the enormous expenses incurred by the Saudi health system due to the MERS-CoV outbreak. Although the accuracy of the direct medical cost estimation presented in this study is limited since it was merely a review of medical charts for 24 patients, an extensive effort was made to identify all relevant data about each eligible patient and capture every single cost item. To improve the accuracy of the current analysis on direct medical cost estimation for MERS patients, health researchers should be granted access to the medical files of all MERS cases. This may help in highlighting the deficiencies in the infection control policies. Finally, an enforceable nationwide policy for infection control, and standardized protocols for the management of MERS-CoV infections should be implemented.
Patients with MERS have a wide range of symptoms from being completely asymptomatic to suffering from severe respiratory illnesses. Fever cough, chills and myalgia are some of the most commonly reported symptoms in mild cases, but respiratory distress, kidney failure and septic shock have been reported in acute cases [17, 18]. There are neither vaccines nor specific medications against MERS-CoV, so treatments are usually palliative in nature [17, 19]. More than a third of those infected with MERS-CoV die. For comparative perspective, case fatality was one in ten for the SARS pandemic of 2003.
Research is yet to be done on the relationship between symptoms and transmissibility. Given that clinical procedures for acute patients can generate aerosolized viral particles, patients with severe respiratory distress would be more likely to transmit the virus compared to asymptomatic patients, but transmissibility of airborne MERS-CoV is unknown. In addition, research in transmission is essential with regards to ‘superspreaders’ who are sources for large number of cases for healthcare associated outbreaks.