Fatality risks for nosocomial outbreaks of Middle East respiratory syndrome coronavirus in the Middle East and South Korea

Middle East Respiratory Syndrome (MERS) Virus-Pathophysiological Axis and the Current Treatment Strategies

Middle East respiratory syndrome (MERS) is a lethal respiratory disease with its first case reported back in 2012 (Jeddah, Saudi Arabia). It is a novel, single-stranded, positive-sense RNA beta coronavirus (MERS-CoV) that was isolated from a patient who died from a severe respiratory illness. Later, it was found that this patient was infected with MERS. MERS is endemic to countries in the Middle East regions, such as Saudi Arabia, Jordan, Qatar, Oman, Kuwait and the United Arab Emirates. It has been reported that the MERS virus originated from bats and dromedary camels, the natural hosts of MERS-CoV. The transmission of the virus to humans has been thought to be either direct or indirect. Few camel-to-human transmissions were reported earlier. However, the mode of transmission of how the virus affects humans remains unanswered. Moreover, outbreaks in either family-based or hospital-based settings were observed with high mortality rates, especially in individuals who did not receive proper management or those with underlying comorbidities, such as diabetes and renal failure. Since then, there have been numerous reports hypothesising complications in fatal cases of MERS. Over the years, various diagnostic methods, treatment strategies and preventive measures have been strategised in containing the MERS infection. Evidence from multiple sources implicated that no treatment options and vaccines have been developed in specific, for the direct management of MERS-CoV infection. Nevertheless, there are supportive measures outlined in response to symptom-related management. Health authorities should stress more on infection and prevention control measures, to ensure that MERS remains as a low-level threat to public health.

Epidemiological parameters of COVID-19 and its implication for infectivity among patients in China, 1 January to 11 February 2020

BackgroundThe natural history of disease in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remained obscure during the early pandemic.AimOur objective was to estimate epidemiological parameters of coronavirus disease (COVID-19) and assess the relative infectivity of the incubation period.MethodsWe estimated the distributions of four epidemiological parameters of SARS-CoV-2 transmission using a large database of COVID-19 cases and potential transmission pairs of cases, and assessed their heterogeneity by demographics, epidemic phase and geographical region. We further calculated the time of peak infectivity and quantified the proportion of secondary infections during the incubation period.ResultsThe median incubation period was 7.2 (95% confidence interval (CI): 6.9‒7.5) days. The median serial and generation intervals were similar, 4.7 (95% CI: 4.2‒5.3) and 4.6 (95% CI: 4.2‒5.1) days, respectively. Paediatric cases < 18 years had a longer incubation period than adult age groups (p = 0.007). The median incubation period increased from 4.4 days before 25 January to 11.5 days after 31 January (p < 0.001), whereas the median serial (generation) interval contracted from 5.9 (4.8) days before 25 January to 3.4 (3.7) days after. The median time from symptom onset to discharge was also shortened from 18.3 before 22 January to 14.1 days after. Peak infectivity occurred 1 day before symptom onset on average, and the incubation period accounted for 70% of transmission.ConclusionThe high infectivity during the incubation period led to short generation and serial intervals, necessitating aggressive control measures such as early case finding and quarantine of close contacts.

Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach

BACKGROUND

The rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to quickly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread of this virus.

OBJECTIVE

The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data.

METHODS

We estimated county-level COVID-19 occurrences for the period March 14 to 31, 2020, based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features. We developed a three-stage model using XGBoost, a machine learning algorithm, to quantify the probability of COVID-19 occurrence and estimate the number of potential occurrences for unaffected counties. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county.

RESULTS

The model predictions showed a sensitivity over 71% and specificity over 94% for models built using data from March 14 to 31, 2020. We found that population, population density, percentage of people aged >70 years, and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We observed a positive association at the county level between urbanicity and vulnerability to COVID-19.

CONCLUSIONS

The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduce significant variation in reported cases, which produces a bias in the model.

Spatial variability of Middle East respiratory syndrome coronavirus survival rates and mortality hazard in Saudi Arabia, 2012–2019

About 83% of laboratory-confirmed Middle East respiratory syndrome coronavirus (MERS-CoV) cases have emerged from Saudi Arabia, which has the highest overall mortality rate worldwide. This retrospective study assesses the impact of spatial/patient characteristics for 14-and 45-day MERS-CoV mortality using 2012–2019 data reported across Saudi regions and provinces. The Kaplan–Meier estimator was employed to estimate MERS-CoV survival rates, Cox proportional-hazards (CPH) models were applied to estimate hazard ratios (HRs) for 14-and 45-day mortality predictors, and univariate local spatial autocorrelation and multivariate spatial clustering analyses were used to assess the spatial correlation. The 14-day, 45-day and overall mortality rates (with estimated survival rates) were 25.52% (70.20%), 32.35% (57.70%) and 37.30% (56.50%), respectively, with no significant rate variations between Saudi regions and provinces. Nationally, the CPH multivariate model identified that being elderly (age ≥ 61), being a non-healthcare worker (non-HCW), and having an underlying comorbidity were significantly related to 14-day mortality (HR = 2.10, 10.12 and 4.11, respectively; p < 0.0001). The 45-day mortality model identified similar risk factors but with an additional factor: patients aged 41–60 (HR = 1.44; p < 0.0001). Risk factors similar to those in the national model were observed in the Central, East and West regions and Riyadh, Makkah, Eastern, Madinah and Qassim provinces but with varying HRs. Spatial clusters of MERS-CoV mortality in the provinces were identified based on the risk factors (r2 = 0.85–0.97): Riyadh (Cluster 1), Eastern, Makkah and Qassim (Cluster 2), and other provinces in the north and south of the country (Cluster 3). The estimated HRs for the 14-and 45-day mortality varied spatially by province. For 45-day mortality, the highest HRs were found in Makkah (age ≥ 61 and non-HCWs), Riyadh (comorbidity) and Madinah (age 41–60). Coming from Makkah (HR = 1.30 and 1.27) or Qassim province (HR = 1.77 and 1.70) was independently related to higher 14-and 45-day mortality, respectively. MERS-CoV patient survival could be improved by implementing appropriate interventions for the elderly, those with comorbidities and non-HCW patients.

Middle East respiratory syndrome

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a lethal zoonotic pathogen that was first identified in humans in Saudi Arabia and Jordan in 2012. Intermittent sporadic cases, community clusters, and nosocomial outbreaks of MERS-CoV continue to occur. Between April 2012 and December 2019, 2499 laboratory-confirmed cases of MERS-CoV infection, including 858 deaths (34·3% mortality) were reported from 27 countries to WHO, the majority of which were reported by Saudi Arabia (2106 cases, 780 deaths). Large outbreaks of human-to-human transmission have occurred, the largest in Riyadh and Jeddah in 2014 and in South Korea in 2015. MERS-CoV remains a high-threat pathogen identified by WHO as a priority pathogen because it causes severe disease that has a high mortality rate, epidemic potential, and no medical countermeasures. This Seminar provides an update on the current knowledge and perspectives on MERS epidemiology, virology, mode of transmission, pathogenesis, diagnosis, clinical features, management, infection control, development of new therapeutics and vaccines, and highlights unanswered questions and priorities for research, improved management, and prevention.

Advances in MERS-CoV Vaccines and Therapeutics Based on the Receptor-Binding Domain

Middle East respiratory syndrome (MERS) coronavirus (MERS-CoV) is an infectious virus that was first reported in 2012. The MERS-CoV genome encodes four major structural proteins, among which the spike (S) protein has a key role in viral infection and pathogenesis. The receptor-binding domain (RBD) of the S protein contains a critical neutralizing domain and is an important target for development of MERS vaccines and therapeutics. In this review, we describe the relevant features of the MERS-CoV S-protein RBD, summarize recent advances in the development of MERS-CoV RBD-based vaccines and therapeutic antibodies, and illustrate potential challenges and strategies to further improve their efficacy.

MERS transmission and risk factors: a systematic review

Background

Since Middle East respiratory syndrome (MERS) infection was first reported in 2012, many studies have analysed its transmissibility and severity. However, the methodology and results of these studies have varied, and there has been no systematic review of MERS. This study reviews the characteristics and associated risk factors of MERS.

Method

We searched international (PubMed, ScienceDirect, Cochrane) and Korean databases (DBpia, KISS) for English- or Korean-language articles using the terms “MERS” and “Middle East respiratory syndrome”. Only human studies with > 20 participants were analysed to exclude studies with low representation. Epidemiologic studies with information on transmissibility and severity of MERS as well as studies containing MERS risk factors were included.

Result

A total of 59 studies were included. Most studies from Saudi Arabia reported higher mortality (22–69.2%) than those from South Korea (20.4%). While the R₀ value in Saudi Arabia was < 1 in all but one study, in South Korea, the R₀ value was 2.5–8.09 in the early stage and decreased to < 1 in the later stage. The incubation period was 4.5–5.2 days in Saudi Arabia and 6–7.8 days in South Korea. Duration from onset was 4–10 days to confirmation, 2.9–5.3 days to hospitalization, 11–17 days to death, and 14–20 days to discharge. Older age and concomitant disease were the most common factors related to MERS infection, severity, and mortality.

Conclusion

The transmissibility and severity of MERS differed by outbreak region and patient characteristics. Further studies assessing the risk of MERS should consider these factors.

MERS transmission and risk factors: a systematic review

BACKGROUND

Since Middle East respiratory syndrome (MERS) infection was first reported in 2012, many studies have analysed its transmissibility and severity. However, the methodology and results of these studies have varied, and there has been no systematic review of MERS. This study reviews the characteristics and associated risk factors of MERS.

METHOD

We searched international (PubMed, ScienceDirect, Cochrane) and Korean databases (DBpia, KISS) for English- or Korean-language articles using the terms "MERS" and "Middle East respiratory syndrome". Only human studies with > 20 participants were analysed to exclude studies with low representation. Epidemiologic studies with information on transmissibility and severity of MERS as well as studies containing MERS risk factors were included.

RESULT

A total of 59 studies were included. Most studies from Saudi Arabia reported higher mortality (22-69.2%) than those from South Korea (20.4%). While the R₀ value in Saudi Arabia was < 1 in all but one study, in South Korea, the R₀ value was 2.5-8.09 in the early stage and decreased to < 1 in the later stage. The incubation period was 4.5-5.2 days in Saudi Arabia and 6-7.8 days in South Korea. Duration from onset was 4-10 days to confirmation, 2.9-5.3 days to hospitalization, 11-17 days to death, and 14-20 days to discharge. Older age and concomitant disease were the most common factors related to MERS infection, severity, and mortality.

CONCLUSION

The transmissibility and severity of MERS differed by outbreak region and patient characteristics. Further studies assessing the risk of MERS should consider these factors.

Factors associated with recovery delay in a sample of patients diagnosed by MERS-CoV rRT-PCR: A Saudi Arabian multicenter retrospective study

Background

Research evidence exists that poor prognosis is common in Middle East respiratory syndrome coronavirus (MERS‐CoV) patients.

Objectives

This study estimates recovery delay intervals and identifies associated factors in a sample of Saudi Arabian patients admitted for suspected MERS‐CoV and diagnosed by rRT‐PCR assay.

Methods

A multicenter retrospective study was conducted on 829 patients admitted between September 2012 and June 2016 and diagnosed by rRT‐PCR procedures to have MERS‐CoV and non‐MERS‐CoV infection in which 396 achieved recovery. Detailed medical charts were reviewed for each patient who achieved recovery. Time intervals in days were calculated from presentation to the initial rRT‐PCR diagnosis (diagnosis delay) and from the initial rRT‐PCR diagnosis to recovery (recovery delay).

Results

The median recovery delay in our sample was 5 days. According to the multivariate negative binomial model, elderly (age ≥ 65), MERS‐CoV infection, ICU admission, and abnormal radiology findings were associated with longer recovery delay (adjusted relative risk (aRR): 1.741, 2.138, 2.048, and 1.473, respectively). Camel contact and the presence of respiratory symptoms at presentation were associated with a shorter recovery delay (expedited recovery) (aRR: 0.267 and 0.537, respectively). Diagnosis delay is a positive predictor for recovery delay (r = .421; P = .001).

Conclusions

The study evidence supports that longer recovery delay was seen in patients of older age, MERS‐CoV infection, ICU admission, and abnormal radiology findings. Shorter recovery delay was found in patients who had camel contact and respiratory symptoms at presentation. These findings may help us understand clinical decision making on directing hospital resources toward prompt screening, monitoring, and implementing clinical recovery and treatment strategies.

Epidemiological factors and worldwide pattern of Middle East respiratory syndrome coronavirus from 2013 to 2016

Background

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging threat to global health security with high intensity and lethality. This study was conducted to investigate epidemiological factors and patterns related to this disease.

Methods

Full details of MERS-CoV cases available on the disease outbreak news section of the World Health Organization official website from January 2013 to November 2016 were retrieved; demographic and clinical information, global distribution status, potential contacts, and probable risk factors for the mortality of laboratory-confirmed MERS-CoV cases were extracted and analyzed by following standard statistical methods.

Results

Details of 1,094 laboratory-confirmed cases were recorded, including 421 related deaths. Significant differences were observed in the presentation of the disease from year to year, and all studied parameters differed during the years under study (all P-values <0.05). Evaluation of the effects of various potential risk factors of the final outcome (dead/survived) revealed that two factors, namely, the morbid case being native and travel history, are significant based on a unifactorial analysis (P <0.05). From 2013 to 2016, these factors remained important. However, factors that were significant in predicting mortality varied in different years.

Conclusion

These findings point to interesting potential dimensions in the dynamic of this disease. Furthermore, effective national and international preparedness plans and actions are essential to prevent, control, and predict such viral outbreaks; improve patient management; and ensure global health security.

The predictors of 3- and 30-day mortality in 660 MERS-CoV patients

Background

The mortality rate of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) patients is a major challenge in all healthcare systems worldwide. Because the MERS-CoV risk-standardized mortality rates are currently unavailable in the literature, the author concentrated on developing a method to estimate the risk-standardized mortality rates using MERS-CoV 3- and 30-day mortality measures.

Methods

MERS-CoV data in Saudi Arabia is publicly reported and made available through the Saudi Ministry of Health (SMOH) website. The author studied 660 MERS-CoV patients who were reported by the SMOH between December 2, 2014 and November 12, 2016. The data gathered contained basic demographic information (age, gender, and nationality), healthcare worker, source of infection, pre-existing illness, symptomatic, severity of illness, and regions in Saudi Arabia. The status and date of mortality were also reported. Cox-proportional hazard (CPH) models were applied to estimate the hazard ratios for the predictors of 3- and 30-day mortality.

Results

3-day, 30-day, and overall mortality were found to be 13.8%, 28.3%, and 29.8%, respectively. According to CPH, multivariate predictors of 3-day mortality were elderly, non-healthcare workers, illness severity, and hospital-acquired infections (adjusted hazard ratio (aHR) =1.7; 8.8; 6.5; and 2.8, respectively). Multivariate predictors of 30-day mortality were elderly, non-healthcare workers, pre-existing illness, severity of illness, and hospital-acquired infections (aHR =1.7; 19.2; 2.1; 3.7; and 2.9, respectively).

Conclusions

Several factors were identified that could influence mortality outcomes at 3 days and 30 days, including age (elderly), non-healthcare workers, severity of illness, and hospital-acquired infections. The findings can serve as a guide for healthcare practitioners by appropriately identifying and managing potential patients at high risk of death.