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TIME TO DIAGNOSIS OF MIDDLE EAST RESPIRATORY SYNDROME CORONAVIRUS INFECTION AND IMPACT ON CLINICAL OUTCOME, WORLDWIDE STUDY.
THESIS II: PRACTICE & INTEGRATIVE LEARNING EXPERIENCES
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is an emerging infectious disease that considers being a threat to global health security with high lethality and mortality rate of 35.7 %. This study was conducted to estimate the association between the time interval to a confirmed diagnosis after symptom onset and mortality.
The data are publicly available from the World Health Organization (WHO), for which the cases of MERS-CoV for the period of January 2015-December 2018 were accessed and analyzed. Demographic, clinical, and mode of transmission information, as well as other probable risk factors for the mortality of MERS-CoV cases, were taken and analyzed through a multivariable logistic model.
Among the 691 confirmed MERS-CoV cases, 33.14% died from the infection and the mean time of diagnosis was 5.44 days. In the crude model, the association between mortality and time to diagnosis was significant(OR: 1.06, 95% CI: 1.03, 1.10). However, after adjusting for the other covariate the association between mortality and time to diagnosis was not significant (AOR:1.01, 95% CI: 0.97, 1.06).
In conclusion, mortality was greater in older patients, among healthcare workers, and patients admitted to the intensive care unit ICU. The study demonstrates the importance of early identification of MERS-CoV cases, as a delay in diagnosis could be a risk factor in a poor prognosis. Furthermore, awareness of the disease, access to healthcare, and early diagnosis could be considered as modifiable risk factors to reduce the high mortality rate of MERS-CoV.
Keywords: MERS-CoV, Middle East Respiratory Syndrome Coronavirus, Mortality ,time to diagnosis .
Abstract Words Count: 244
Main text Words Counts: 3176
Number Figures: 1
In the past 15 years, the global public health authorities have paid increased attention to two new emerging lethal zoonotic coronaviruses with epidemic potential: severe acute respiratory syndrome (SARS) coronavirus (CoV)[1–3] and Middle East Respiratory syndrome (MERS)-CoV 4,5. Although SARS and MERS-CoV have a similar incubation period of 4.6 days (95% CI: 3.8, 5.8) and 5.2 (95% CI: 1.9, 14.7) respectively, MERS-CoV has a higher case fatality and mortality rate . MERS-CoV has been considered an emergence in countries where the outbreak have occurred . MERS-CoV is a deadly zoonosis that causes death in 35.7% of cases . The World Health Organization (WHO) declared MERS-CoV pathogens to be a threat to global health security . MERS-CoV was first identified in humans in Saudi Arabia in September of 2012 , and later found in several Middle East countries including the United Arab Emirates, Qatar, Oman, Jordan, Kuwait, Yemen, Lebanon, and Iran . The largest outbreak outside The Middle East was in South Korea, with 186 cases and a mortality rate of 20.4% [6-7]. Several other countries in Europe and North America, Africa, and Asia have recorded MERS cases; however, most of these cases were directly or indirectly linked to the Middle East through travel or other means [3,8,9]. The majority of the MERS-CoV outbreak was by either nosocomil or houshold transmation; however, in South Korea it was mainly a health-care associated outbreak . In South Korea, the index case was identified as a traveler who came from the Middle East, became ill and went to several health facilities for medical attention. These multiple visits later resulted in the infection of 186 individuals (including 25 health workers) admitted to these health-care facilities . The occurrence of the outbreak in South Korea shows that MERS-CoV can possibly be transmitted from person to person in a well resourced developed country . The WHO has reported that the number of MERS-CoV cases have been gradually increasing since 2012, reaching 2182 laboratory-confirmed cases of MERS-CoV in 2018 of which 779 resulted in death (35·7% mortality rate) .
Clinical presentation of MERS-CoV ranges from simple flu-like symptoms to, multi-organ failure and death . In general, patients with MERS-CoV presentation start with fever, cough, chills, and sore throat, followed by shortness of breath and then rapidly progresses to pneumonia with Acute Respiratory Distress Syndrome ARDS which often requiring mechanical ventilation within the first week . Moreover, MERS-CoV is considered more severe than SARS as MERS –CoV potentially leads to mechanical ventilation in 11 days compared to 7 days for SARS. Due to a lack of data on viral dynamics and clinical course, the severity of MERS-CoV cannot be explained. .
One of the main risk factors for primary MERS-CoV infection was direct or indirect contact with a camel . In fact, a study was done in Saudi Arabia regarding dromedary camel exposure and MERS-CoV antibodies level which found that, compared to the general public, shepherds and slaughterhouse workers have a significantly increased seroprevalence of MERS-CoV antibodies, approximately 15 times and 23 times respectively . Moreover, a study of the animal herds associated with MERS-Cov infected patients found that the isolated full genome sequences of the ten MERS-CoV camels were identical to their corresponding MERS-Cov infected patients . However, the primary source of infection remains unknown and research is still needed to understand how MERS-CoV infection is acquired from the community .
Human to human transmission of MERS-CoV has been was documented in the household, community, and more prominently in the healthcare setting. The health-care workers in close contact with patients with MERS-CoV are at an increased risk of acquiring a severe infection, whereas healthy adults might develop a mild form of the illness or even an asymptomatic infection. Similar to other forms of coronvirus there is no clear evidence on the exact route of MERS-CoV transmission, but it has been reported to spread through air via coughing, sneezing, or direct personal contact .
To assess the impact of household transmission, a study was done on MERS-CoV infected families in Saudi Arabia, and it was found that among 79 family members, 19 (24%) were infected and 2 died . Furthermore, researchers found that sleeping in the infected patient’s room and touching respiratory secretions from an infected patient increases the risk for household transmission . Despite this, there are no epidemiological studies that have confirmed the initial human-to-human or animal-to-human transmission risk factors.
Health risk factors associated with poor outcome of MERS-CoV infection have been discussed in the literature. It was found that one of the primary factors that may determine or increase the risk of mortality from MERS-CoV is comorbidities such as diabetes mellitus, heart disease, and End-stage renal disease [14,15]. In fact, MERS-CoV patients with comorbidity had around four times the risk of fatal infection than those without after adjusting for age and epidemic period (adjusted hazard ratio of 3.74 (95% CI: 2.57, 5.67) .The literature suggested that factors such as age, the severity of the illness and hospital-acquired infection are also associted with poor outcome of the disease .
Time to diagnosis
Many documented cases were diagnosed in the late stages of the disease; therefore, delay of diagnosis and the association with a poor outcome should be considered as a risk factor . In South Korea, a study was conducted to investigate the clinical progression and cytokine profiles after the first appearance of illness . The results indicate that there was an association between the time to diagnosis and a poor outcome in patients with severe illness. Moreover, it found the median time to diagnosis of MERS-CoV from symptom onset to transfer to the isolation unit was 5 days, with a range of 2–11 days . In addition, another study investigated 37 cases of MERS-Cov in a single hospital in South Korea and found that the median incubation period was 6 days (95% CI: 4-7 days) and the delay of laboratory confirmation of coronavirus was 6.5 days (95% CI: 4-9 days) . Early diagnosis and identification of MERS-CoV may improve the prognosis of patients; hence, reducing the spread of the illness .
Area of Study
The time interval between symptom onset and diagnosis has remained poorly understood. No research, to the best of the author’s knowledge, has addressed the factors associated with the time to a confirmed MERS-CoV diagnosis after symptom onset. The hypothesis of this study was that the time interval between symptom onset and the diagnosis of MERS may differ by age group, sex, country, the severity of illness, and the source of infection. The study aimed to estimate the median time interval to a confirmed diagnosis after symptom onset and the impact on mortality.
The study’s main outcome was mortality, defined as whether the patient had died during the 45 days following the MERS-CoV diagnosis . The main exposure was the time interval from symptom onset to a confirmed diagnosis, defined as the number of days after developing symptoms until diagnosis. Other covariates include date of onset of symptoms, date of confirmation of diagnosis, date of death, gender (female, male), age (<30, 30–59, 60–65, and >65 years), whether the patient was a healthcare worker or not, having comorbidity or not, and being asymptomatic . For the region variable, there were 27 countries in the WHO database; however, due to the distribution of the cases in these countries we categorized the region into three categories: 1) Saudi Arabia, 2) Middle Eastern Countries, and 3) combined South Korea with the rest of the countries due to the small number of cases in the latter group. Saudi Arabia was analyzed separately from other Middle East countries because it has a unique situation and has recorded the largest number of MERS cases. Finally, for the measure of source of infection, we categorized the variable into four distinct categories: 1) camel (if the patient has a history of camel exposure), 2) health-care associated (if the patient acquired the infection in a health care facility setting), 3) household (if the patient acquired the infection after coming in contact with an infected patients within their house), and 4) unknown.
The study’s main outcome was mortality defined as whether the patient had died during the 45 days following the MERS-CoV diagnosis . The main exposure was the time interval from symptom onset to a confirmed diagnosis defined as the number of days after developing symptoms until diagnosis. Other covariates include date of onset of symptoms, date of confirmation of diagnosis, date of death, gender (female, male), age (<30, 30–59, 60–65 and >65 years), whether the patient was a healthcare worker or not, having comorbidity or not, and being asymptomatic . For the region variable, there were 27 countries in the WHO database however due to the distribution of the cases in these countries we categorized region into three categories: 1) Saudi Arabia, 2) Middle Eastern Countries, and 3) combined South Korea with the rest of the countries due to the small number of cases in the latter group. Saudi Arabia was analyzed separately from other Middle East countries because Saudi Arabia has a unique situation and recorded the largest number of MERS cases. Finally, for the measure of source of infection, we categorized the variable into four distinct categories 1) camel (if the patient has history of camel exposure), 2) health-care associated (if the patient acquired the infection in health care facilities setting), 3) household (if the patient acquired the infection from contacting other infected patients within their house), and 4) unknown.
The characteristics of the study population were summarized using the frequency and percentage (%) for categorical variables and the mean and standard deviation (±SD), or median and interquartile range (IQR), for continuous variables. Differences between groups were analyzed with the chi-square (χ2) test and the Fisher’s exact test for categorical variables, and the Student’s t-test for continuous variables. Age and comorbidities will be assessed for effect modification using contingency tables. To analyze survival according to time to diagnosis, Kaplan-Meier plots were ran by region. Then, to understand the overall relationship between time to diagnosis and death in the presence of other, important covariates, logistic regressions were used for analysis. The significant variables in the bivariable analysis were included in a multivariable logistic regression model to assess independent factors associated with late diagnosis and mortality. A crude model with only the exposure and outcome will be developed. Next, the model will be developed by analyzing covariates using forward or backward modeling techniques. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Statistical significance was set at P < 0.05. Cumulative survival for MERS-CoV cases according to time to diagnosis was assessed using the Kaplan- Meier plot for every region. Data were analyzed using Stata, version 14.
A total of 691 MERS-CoV cases were retrieved and included in the study (see Table 1). The majority of the patients (n=344) were between the ages 30-55 (49.78%) followed by 65 years old and above, below 30 years, and 60-65 years old, (193 [27.93%], 79 [11.43%], 74 [10.85%]), respectively. Two-thirds (67.73%) of patients were male, and 71.14% had comorbidity. Moreover, 14.4% of the patients were healthcare workers, and 41.11% have been admitted to the ICU. The majority of the cases were from Saudi Arabia (84.80%) followed by South Korea and other countries (10.27%), and the remaining cases were from Middle Eastern countries (4.92%). The mean time from the onset of symptoms to MERS-CoV diagnosis was 5.44 days (range 0–61 days). Among the 691 symptomatic MERS-CoV patients 229 (33.14 %) have died from MERS-CoV infection. The majority of the sample source of infection were healthcare-associated infection 311 (45.01%), followed by an unknown source, camel exposure, and household (163 [23.59%], 146 [21.13%], 719 [10.27%]) respectively.
The bivariable analysis that assesses the relationship between mortality and relevant covariates is shown in Table 2. The findings suggest that there was a significant relationship between mortality and time to diagnosis (p-value= <0.01). They indicate that the patients who died from MERS-CoV had a mean time to diagnosis of 6.29 days compared to the patients who lived, who had a mean time of diagnosis of 5.03 days. Moreover, mortality was significantly associated with age, sex, country, the severity of the illness, and source of infection. Mortality was significantly associated with sex, which indicated that males were significantly more likely to die from MERS-CoV compared to females (74.24 vs. 25.27%; p <0.01). In addition, patients with comorbidities were more likely to die from MERS-Cov (59.07, p <0.01) compared to patients without comorbidities. Mortality among patients who were health-care workers was much lower than patients who were not health-care workers (2.18% vs. 97%.82; p <0.01). In regards to the source of infection, a hospital-associated infection had the highest prevalence of mortality with 43.23% in this sample compared to the household, camel exposure, and an unknown source. (10.2%, 21.13%, and 27.51%, respectively; p <0.01). ICU admission was significantly associated with mortality; patients who required an ICU admission had higher mortality than those who did not have an ICU admission (72.37 vs 27.63; p <0.01). Moreover, Saudi Arabia had the highest prevalence of mortality compared to South Korea and the rest of the Middle Eastern countries (94.32%, 2.18%, and 3.40% respectively, p = 0.01). The crude survival curves by countries are presented in the Kaplan Meier curve Fig.1. The survival curves for Middle Eastern countries and South Korea were similar to each other, with the survival probabilities higher than Saudi Arabia (Fig.1)
Multiple logistic regression was conducted to examine the relationship between the time to diagnosis and mortality (Table 3). In the crude model, the odds ratio between time to diagnosis and mortality were significant, indicating that a one unit increase in time to diagnosis was associated with 1.06 times the odds of mortality (OR: 1.06 95% CI: 1.03, 1.10). After adjusting for the other covariates, the association between time to diagnosis and mortality were insignificant (AOR: 1.01 95% CI: 0.97,1.06). Despite the insignificant main finding, controlling for the other covariates, patients who are 65 years and older have 1.13 times the likelihood of dying from MERS-CoV compared to the patients who are 30 years and younger (AOR: 2.64, 95% CI: 0.99, 7.02). The odds of mortality were higher among patients who weren’t health care workers compared to patients who were health care workers, controlling for other factors (AOR: 0.25, CI: 0.07, 0.84). In comparison to the different sources of infection, for the patients whose infection source was healthcare-associated and from camel exposure, the odds of mortality were higher compared to patients who were infected in the household, controlling for other factors. (AOR: 3.27 95% CI: 1.26, 8.54, 2.67 95% CI: 1.01, 7.08). Regarding ICU admission, patients who are admitted to ICU have a 6.31 likelihood of dying from MERS-CoV compared to patients not admitted to the ICU controlling for other factors (AOR: 6.31 95% CI: 4.17, 9.58). Finally, mortality was also higher among patients with comorbidities compared to those with no comorbidities (AOR: 6.31 95% CI: 4.17, 9.58), maintaining other factors constant.
The large number of MERS-CoV cases and its related mortality around the world demonstrate that this disease should be considered as a threat to public health. This study appears to be one of the first to assess the association between the time to diagnosis and mortality across several populations. The average time interval to a confirmed diagnosis after symptom onset was 5.25 days in Saudi Arabia and 3.84 days in South Korea, which was consistent with the literature (17,19). In this study, the results indicate that age was significantly associated with a mortality; the likelihood of mortality was 1.16 times higher in patients over the age of 65 years compared to the youngest age group of patients (<30 years) which has been observed in other studies (10,22,23). Non-healthcare workers had a high mortality compared to healthcare workers, which may be explained by non-healthcare workers having less awareness and knowledge of the symptoms of MERS-CoV infection while healthcare workers have more access to healthcare (22.24).
The uniqueness of this study lies in the investigation of the association between mortality with the source of infection in symptomatic cases of MERS-CoV infection. The results suggest that the mortality and time to diagnosis were independently associated with the source of infection. Patients with healthcare-associated infection have a higher mortality compared to household, camel exposure, and Unknown, with 43.23%, 3.06%, 26.20, and 27.51% respectively. Comorbidity was an indicator of poor prognosis for MERS-CoV in this study; patients with comorbidities had three times the odds of dying from MERS-CoV (AOR: 3.27, 95% CI: 1.25, 8.54). Moreover, patients with comorbidities had a prolonged time to diagnosis with a mean of 5.89 days compared to 4.62 days, which could be explained by the fact that patient with comorbidities have a high risk of multiple infectious diseases with overlapping symptoms that could delay the diagnosis of MERS-CoV. Moreover, there is a significant association between mortality and the country where the case originated from; the results indicate that a South Korean has 0.06 times the likelihood of death from MERS-CoV compared to Saudi Arabia and the survival curves shows that South Korea has a higher survival probabilities than Saudi Arabia, which may be attributed to what type of medical management was given to the MERS-CoV patients and the differences in the healthcare system in South Korea compared to Saudi Arabia.
This study has some limitations. The study used public source data, in which specific details of the patients’ demographic data, clinical characteristics, and access to health care had not been reported. Due to the nature of the study, the result indicates an association between the factors rather than causation; therefore, causality cannot be inferred. Moreover, an important indicator of mortality is the type of medical management given to the patients which were missing from the study. Furthermore, potential information bias result from misclassification in the categorization of cases may be due to the patients reporting, such as history of contact with other infected MERS-CoV cases at home or hospital 14 days prior to the onset of symptoms, comorbidity, and the history of contact with camel or camel milk 14 days prior to the onset of symptoms. Furthermore, negligible selection bias might introduce be into the results because some of the MERS-CoV cases that occurred in South Korea were not included in the WHO database.(22).
The findings highlight valuable information about the predictors for mortality from MERS-CoV infection. The study stresses the importance of early identification of symptoms related to MERS-CoV, as a delay in diagnosis could be a risk factor of poor prognosis across various populations Understanding the link between time to diagnosis and it is role in mortality can help researchers develop screener tools to early detect MERS-CoV cases .In conclusion, mortality was greater in older patients, non-healthcare workers, patients admitted with ICU, patients with healthcare-associated infection, and those in the country where the infection occurred. Further prospective studies are necessary to address the impact of all potential risk factors investigated in this study for the mortality in patients with MERS-CoV infection. Moreover, future research should focus on the quality and type of care (clinical, screening) across different populations. Awareness of the disease, access to health care, and early diagnosis could be considered as modifiable factors to reduce high mortality rates in MERS-CoV.
Table 1. Sample Characteristics from the World Health Organization (WHO) data on the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) cases from 2015 to 2017 (n=691)
|Mean time of diagnosis (sd, Range)||5.44 (5.24,0-61)|
|Age group, years|
|Middle East||34 (4.92)|
|Saudi Arabia||586 (84.80)|
|Republic of Korea and other countries||71 (10.27)|
|Severity of illness /ICU admission|
|Source of infection|
|Healthcare associated||311 (45.01)|
SD = standard deviation
Table 2. Bivariate relationship between demographic characteristics and mortality from the World Health Organization (WHO) data on the Middle East Respiratory syndrome coronavirus (MERS-CoV) from 2015 to 2017 (n=691)
|Mean time of diagnosis (sd, range)||691||6.29||5.03||0.001|
|Female||223 (32.27)||59 (25.27)||164 (35.50)||0.001|
|Male||468 (67.73)||170 (74.24)||298 (64.50)|
|Age group, years|
|<30||79 (11.43)||9 (3.93)||70 (15.15)||0.001|
|30-59||344 (49.78)||85 (37.12)||259 (56.06)|
|60-65||75 (10.85)||27 (11.79)||48 (10.39)|
|>65||193 (27.93)||108 (47.16)||85 (18.40)|
|Middle east||34 (4.92)||8 (3.40)||26 (5.63)||0.001|
|Saudi Arabia||586 (84.80)||216 (94.32)||370 (80.09)|
|Republic of Korea and other countries||71 (10.27)||5 (2.18)||66 (14.29)|
|Yes||99 (14.33)||5 (2.18)||94 (20.35)||0.001|
|No||592 (85.67)||224 (97.82)||368 (79.65)|
|Yes||451 (71.14)||241 (59.07)||210 (92.92)||0.001|
|No||183 (28.86)||16 (7.08)||167 (40.93)|
|Yes||608 (88.12)||224 (97.82)||384 (83.30)||0.001|
|No||82 (11.88)||5 (2.18)||77 (16.70)|
|Severity of illness /ICU admission|
|Yes||273 (41.11)||165 (72.37)||108 (24.77)||0.001|
|No||391 (58.89)||63(27.63)||328 (75.23)|
|Source of infection|
|Household||71 (10.27)||7 (3.06)||64 (13.85)||0 .001|
|Camels||146 (21.13)||60 (26.20)||86 (18.61)|
|Healthcare associated||311 (45.01)||99 (43.23)||212 (45.89)|
|Unknown||163 (23.59)||63 (27.51)||100 (21.65)|
Table 3. Multivariable analysis of the association between the mortality and time of diagnosis among cases of Middle East respiratory syndrome coronavirus from the World Health Organization (WHO) data on Middle East respiratory syndrome coronavirus (MERS-CoV) from 2015 to 2017 (n=691).
|Crude OR (95% CI)||Full model
Adjusted OR (95% CI)
|Time of Diagnosis||1.06 (1.03, 1.10)||1.01 (.97,1.06)|
|Male||1.23 (0 .76, 2.001)|
|Age group, years (<30)|
|30-59||1.08 (0.43, 2.71)|
|60-65||1.13 (0.39, 3.29)|
|>65||2.55 (0.96, 6.76|
|Region Saudi Arabia|
|Middle East||0.46 (.17 ,1.30)|
|Republic of Korea/others||0.07 (0.01, 0.36)|
|Yes||0.26 (0.08, 0 .87)|
|Yes||2.52 (.82, 7.75)|
|Severity of illness /ICU admission|
|Source of infection (Household)|
|Healthcare associated||2.52 (.94,6.82)|
|Unknown||2.17 (.82, 5.80)|
CI = Confidence Interval
1. Zaki AM, Van Boheemen S, Bestebroer TM, Osterhaus AD, Fouchier RA. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N ENGL J MED. 2012 Nov 8;367(19):1814-20.
2. World Health Organization. Middle East respiratory syndrome coronavirus (MERS-CoV): summary of current situation, literature update and risk assessment.
3. Lebanon O. First confirmed cases of Middle East respiratory syndrome coronavirus (MERS-CoV) infection in the United States, updated information on the epidemiology of MERS-CoV infection, and guidance for the public, clinicians, and public health authorities—May 2014.
4. Zumla A, Hui DS, Perlman S. Middle East Respiratory Syndrome. Lancet (London, England)., 2015;386(9997):995-1007. doi:10.1016/S0140-6736(15)60454-8.
5. Müller MA, Meyer B, Corman VM, et al. Presence of Middle East respiratory syndrome coronavirus antibodies in Saudi Arabia: a nationwide, cross-sectional, serological study. Lancet Infect Dis. (2015);15:559-564.
6. WHO. Middle East respiratory syndrome coronavirus (MERS-CoV) http://www.who.int/emergencies/mers-cov/en/(accessed Jan 16, 2018).
7. Lee JY, Kim, YJ, Chung EH, et al.The clinical and virological features of the first imported case causing MERS-CoV outbreak in South Korea, 2015. BMC Infect Dis. 2017;17:498
8. Hui DS, Perlman S, Zumla A. Spread of MERS to South Korea and China. Lancet Respir Med .2015 Jan 7;3(7):509-10.
9. Ithete NL, Stoffberg S, Corman VM, Cottontail VM, Richards LR, Schoeman MC, Drosten C, Drexler JF, Preiser W. Close relative of human Middle East respiratory syndrome coronavirus in bat, South Africa. Emerg Infect Dis. 2013 Oct 1;19(10):1697-9.
10. Rivers CM, Majumder MS, Lofgren ET. Risks of Death and Severe Disease in Patients with Middle East Respiratory Syndrome Coronavirus, 2012–2015. Am J Epidemiol. 2016 Sep 6;184:460–4.
11. Samy K, Ibraheem Q, Ali Al-Hufofi, et al. Cross-sectional study of MERS-CoV-specific RNA and antibodies in animals that have had contact with MERS patients in Saudi Arabia. Journal of Infection and Public Health, Copyright © 2017 The Authors.
12. Azhar EI, Hashem AM, El-Kafrawy SA, Sohrab SS, Aburizaiza AS, Farraj SA, Hassan AM, Al-SaeedMS, JamjoomGA, MadaniTA. Detection of the Middle East respiratory syndrome coronavirus genome in an air sample originating from a camel barn owned by an infected patient. MBio. 2014 Aug 29;5(4):e01450-14.
13. Arwady M, Alraddadi BM, Basler C, et al. Middle East Respiratory Syndrome Coronavirus Transmission in Extended Family, Saudi Arabia, 2014. Emerging Infectious Diseases. 2016;22(8):1395-1402. doi:10.3201/eid2208.152015.
14.Alraddadi BM, Watson JT, Almarashi A, Abedi GR, Turkistani A, Sadran M, Housa A, Almazroa MA, Alraihan N, Banjar A, Albalawi E. Risk Factors for Primary Middle East Respiratory Syndrome Coronavirus Illness in Humans, Saudi Arabia, 2014. Emerg Infect Dis. 2016 Jan;22(1):49-55.
15. Al-Tawfiq JA, Hinedi K, Ghandour J, Khairalla H, Musleh S, Ujayli A, Memish ZA. Middle East respiratory syndrome coronavirus: a case-control study of hospitalized patients. Clin Infect Dis. 2014 Apr 9:ciu226.
16. Yang Y-M, Hsu C-Y, Lai C-C, et al. Impact of Comorbidity on Fatality Rate of Patients with Middle East Respiratory Syndrome. Sci Rep. 2017;7:11307. doi:10.1038/s41598-017-10402-1.
coronavirus (MERS-CoV) infection
17. Ahmed, AE. The predictors of 3-and 30-day mortality in 660 MERS-CoV patients. BMC Infectious Diseases 2007;17(1):615.
18. Al-Dorzi HM, Aldawood AS, Khan R, Baharoon S, Alchin JD, Matroud AA, et al. The critical care response to a hospital outbreak of Middle East respiratory syndrome coronavirus (MERS-CoV) infection: an observational study. Ann Intensive Care. 2016 Oct;6:101
19. Kim ES, Choe PG, Park, WB, Oh, HS, Kim, EJ, Nam EY, et al.Clinical Progression and Cytokine Profiles of Middle East Respiratory Syndrome Coronavirus Infection. J Korean Med Sci. 2016 Nov 11;31:1717-1725.
20. Park HY, Lee EJ, Ryu Y W, Kim Y, Kim H, Lee H, Yi S J. Epidemiological investigation of MERS-CoV spread in a single hospital in South Korea, May to June 2015*. Euro Surveill. 2015;20(25):pii=21169. https://doi.org/10.2807/1560-7917.ES2015.20.25.21169.21
21.Ahmed A. Estimating survival rates in MERS-CoV patients 14 and 45 days after experiencing symptoms and determining the differences in survival rates by demographic data, disease characteristics and regions: a worldwide study. Epidemiol Infect. 2017 Dec 22; 146(4):489-495.
22. Assiri A, Al-Tawfiq JA, Al-Rabeeah AA, Al-Rabiah FA, Al-Hajjar S, Al-Barrak A, et al. Epidemiological, demographic, and clinical characteristics of 47 cases of Middle East respiratory syndrome coronavirus disease from Saudi Arabia: a descriptive study. Lancet Infect Dis 2013b Sep 9;13:752–61.
23. Banik GR, Khandaker G, Rashid H. Middle East respiratory syndrome coronavirus “MERS-CoV”: current knowledge gaps. Paediatr Respir Rev. 2015 June 3;16:197–202.
24. Alqahtani AS, Rashid H, Basyouni MH, Alhawassi TM, BinDhim NF. Public response to MERS-CoV in the Middle East: iPhone survey in six countries. J Infect Public Health 2017;(February):6.