Assessing the risk of observing multiple generations of Middle East respiratory syndrome (MERS) cases given an imported case

Mathematical analysis of stochastic epidemic model of MERS-corona & application of ergodic theory

The "Middle East Respiratory" (MERS-Cov) is among the world's dangerous diseases that still exist. Presently it is a threat to Arab countries, but it is a horrible prediction that it may propagate like COVID-19. In this article, a stochastic version of the epidemic model, MERS-Cov, is presented. Initially, a mathematical form is given to the dynamics of the disease while incorporating some unpredictable factors. The study of the underlying model shows the existence of positive global solution. Formulating appropriate Lyapunov functionals, the paper will also explore parametric conditions which will lead to the extinction of the disease from a community. Moreover, to reveal that the infection will persist, ergodic stationary distribution will be carried out. It will also be shown that a threshold quantity exists, which will determine some essential parameters for exploring other dynamical aspects of the main model. With the addition of some examples, the underlying stochastic model of MERS-Cov will be studied graphically for more illustration.

Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review

BACKGROUND

Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19) have caused substantial public health burdens and global health threats. Understanding the superspreading potentials of these viruses are important for characterizing transmission patterns and informing strategic decision-making in disease control. This systematic review aimed to summarize the existing evidence on superspreading features and to compare the heterogeneity in transmission within and among various betacoronavirus epidemics of SARS, MERS and COVID-19.

METHODS

PubMed, MEDLINE, and Embase databases were extensively searched for original studies on the transmission heterogeneity of SARS, MERS, and COVID-19 published in English between January 1, 2003, and February 10, 2021. After screening the articles, we extracted data pertaining to the estimated dispersion parameter (k) which has been a commonly-used measurement for superspreading potential.

FINDINGS

We included a total of 60 estimates of transmission heterogeneity from 26 studies on outbreaks in 22 regions. The majority (90%) of the k estimates were small, with values less than 1, indicating an over-dispersed transmission pattern. The point estimates of k for SARS and MERS ranged from 0.12 to 0.20 and from 0.06 to 2.94, respectively. Among 45 estimates of individual-level transmission heterogeneity for COVID-19 from 17 articles, 91% were derived from Asian regions. The point estimates of k for COVID-19 ranged between 0.1 and 5.0.

CONCLUSIONS

We detected a substantial over-dispersed transmission pattern in all three coronaviruses, while the k estimates varied by differences in study design and public health capacity. Our findings suggested that even with a reduced R value, the epidemic still has a high resurgence potential due to transmission heterogeneity.

Outbreak analytics: a developing data science for informing the response to emerging pathogens

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012–2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015–2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (R₀) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤R₀≤2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region.

There is currently no way to anticipate MERS-CoV epidemic outbreaks and strategies for disease prediction and containment are largely undermined by the limited knowledge of its epidemiological cycle. Not an effective treatment nor a vaccine for MERS-CoV exist to date. Instead, using three two-strain mathematical models that incorporate human social behavior as different disease incidence functions (e.g. bilinear, non-monotone and saturated), the best model combinations successfully anticipate the occurrence of the peak week in the season and the incidence at the peak. Our results confirm there are currently 2 strains co-circulating in the most populated regions in Saudi Arabia and highlight the high risk for large epidemic outbreaks, while the role of super-spreaders appears irrelevant for disease spread.

A database of geopositioned Middle East Respiratory Syndrome Coronavirus occurrences

As a World Health Organization Research and Development Blueprint priority pathogen, there is a need to better understand the geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and its potential to infect mammals and humans. This database documents cases of MERS-CoV globally, with specific attention paid to zoonotic transmission. An initial literature search was conducted in PubMed, Web of Science, and Scopus; after screening articles according to the inclusion/exclusion criteria, a total of 208 sources were selected for extraction and geo-positioning. Each MERS-CoV occurrence was assigned one of the following classifications based upon published contextual information: index, unspecified, secondary, mammal, environmental, or imported. In total, this database is comprised of 861 unique geo-positioned MERS-CoV occurrences. The purpose of this article is to share a collated MERS-CoV database and extraction protocol that can be utilized in future mapping efforts for both MERS-CoV and other infectious diseases. More broadly, it may also provide useful data for the development of targeted MERS-CoV surveillance, which would prove invaluable in preventing future zoonotic spillover.

Measurement(s)	Middle East Respiratory Syndrome • geographic location
Technology Type(s)	digital curation
Factor Type(s)	geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) • year
Sample Characteristic - Organism	Middle East respiratory syndrome-related coronavirus
Sample Characteristic - Location	Earth (planet)

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11108801

A Case Study Evaluating the Risk of Infection from Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV) in a Hospital Setting Through Bioaerosols

Middle Eastern respiratory syndrome, an emerging viral infection with a global case fatality rate of 35.5%, caused major outbreaks first in 2012 and 2015, though new cases are continuously reported around the world. Transmission is believed to mainly occur in healthcare settings through aerosolized particles. This study uses Quantitative Microbial Risk Assessment to develop a generalizable model that can assist with interpreting reported outbreak data or predict risk of infection with or without the recommended strategies. The exposure scenario includes a single index patient emitting virus-containing aerosols into the air by coughing, leading to short- and long-range airborne exposures for other patients in the same room, nurses, healthcare workers, and family visitors. Aerosol transport modeling was coupled with Monte Carlo simulation to evaluate the risk of MERS illness for the exposed population. Results from a typical scenario show the daily mean risk of infection to be the highest for the nurses and healthcare workers (8.49 × 10^-4 and 7.91 × 10^-4 , respectively), and the lowest for family visitors and patients staying in the same room (3.12 × 10^-4 and 1.29 × 10^-4 , respectively). Sensitivity analysis indicates that more than 90% of the uncertainty in the risk characterization is due to the viral concentration in saliva. Assessment of risk interventions showed that respiratory masks were found to have a greater effect in reducing the risks for all the groups evaluated (>90% risk reduction), while increasing the air exchange was effective for the other patients in the same room only (up to 58% risk reduction).

A Methodology for Determining Which Diseases Warrant Care in a High-Level Containment Care Unit

Although the concept of high-level containment care (HLCC or 'biocontainment'), dates back to 1969, the 2014-2016 outbreak of Ebola virus disease (EVD) brought with it a renewed emphasis on the use of specialized HLCC units in the care of patients with EVD. Employment of these units in the United States and Western Europe resulted in a significant decrease in mortality compared to traditional management in field settings. Moreover, this employment appeared to significantly lessen the risk of nosocomial transmission of disease; no secondary cases occurred among healthcare workers in these units. While many now accept the wisdom of utilizing HLCC units and principles in the management of EVD (and, presumably, of other transmissible and highly hazardous viral hemorrhagic fevers, such as those caused by Marburg and Lassa viruses), no consensus exists regarding additional diseases that might warrant HLCC. We propose here a construct designed to make such determinations for existing and newly discovered diseases. The construct examines infectivity (as measured by the infectious dose needed to infect 50% of a given population (ID50)), communicability (as measured by the reproductive number (R0)), and hazard (as measured by morbidity and mortality). Diseases fulfilling all three criteria (i.e., those that are highly infectious, communicable, and highly hazardous) are considered candidates for HLCC management if they also meet a fourth criterion, namely that they lack effective and available licensed countermeasures.

Comparative Analysis of Eleven Healthcare-Associated Outbreaks of Middle East Respiratory Syndrome Coronavirus (Mers-Cov) from 2015 to 2017

Since its emergence in 2012, 2,260 cases and 803 deaths due to Middle East respiratory syndrome coronavirus (MERS-CoV) have been reported to the World Health Organization. Most cases were due to transmission in healthcare settings, sometimes causing large outbreaks. We analyzed epidemiologic and clinical data of laboratory-confirmed MERS-CoV cases from eleven healthcare-associated outbreaks in the Kingdom of Saudi Arabia and the Republic of Korea between 2015–2017. We quantified key epidemiological differences between outbreaks. Twenty-five percent (n = 105/422) of MERS cases who acquired infection in a hospital setting were healthcare personnel. In multivariate analyses, age ≥65 (OR 4.8, 95%CI: 2.6–8.7) and the presence of underlying comorbidities (OR: 2.7, 95% CI: 1.3–5.7) were associated with increased mortality whereas working as healthcare personnel was protective (OR 0.07, 95% CI: 0.01–0.34). At the start of these outbreaks, the reproduction number ranged from 1.0 to 5.7; it dropped below 1 within 2 to 6 weeks. This study provides a comprehensive characterization of MERS HCA-outbreaks. Our results highlight heterogeneities in the epidemiological profile of healthcare-associated outbreaks. The limitations of our study stress the urgent need for standardized data collection for high-threat respiratory pathogens, such as MERS-CoV.

Efficacy of an Adjuvanted Middle East Respiratory Syndrome Coronavirus Spike Protein Vaccine in Dromedary Camels and Alpacas

MERS-CoV is present in dromedary camels throughout the Middle East and Africa. Dromedary camels are the primary zoonotic reservoir for human infections. Interruption of the zoonotic transmission chain from camels to humans, therefore, may be an effective strategy to control the ongoing MERS-CoV outbreak. Here we show that vaccination with an adjuvanted MERS-CoV Spike protein subunit vaccine confers complete protection from MERS-CoV disease in alpaca and results in reduced and delayed viral shedding in the upper airways of dromedary camels. Protection in alpaca correlates with high serum neutralizing antibody titers. Lower titers of serum neutralizing antibodies correlate with delayed and significantly reduced shedding in the nasal turbinates of dromedary camels. Together, these data indicate that induction of robust neutralizing humoral immune responses by vaccination of naïve animals reduces shedding that potentially could diminish the risk of zoonotic transmission.

What Have We Learned About Middle East Respiratory Syndrome Coronavirus Emergence in Humans? A Systematic Literature Review

BACKGROUND

Middle East respiratory syndrome coronavirus (MERS-CoV) was first identified in humans in 2012. A systematic literature review was conducted to synthesize current knowledge and identify critical knowledge gaps.

MATERIALS AND METHODS

We conducted a systematic review on MERS-CoV using PRISMA guidelines. We identified 407 relevant, peer-reviewed publications and selected 208 of these based on their contributions to four key areas: virology; clinical characteristics, outcomes, therapeutic and preventive options; epidemiology and transmission; and animal interface and the search for natural hosts of MERS-CoV.

RESULTS

Dipeptidyl peptidase 4 (DPP4/CD26) was identified as the human receptor for MERS-CoV, and a variety of molecular and serological assays developed. Dromedary camels remain the only documented zoonotic source of human infection, but MERS-like CoVs have been detected in bat species globally, as well as in dromedary camels throughout the Middle East and Africa. However, despite evidence of camel-to-human MERS-CoV transmission and cases apparently related to camel contact, the source of many primary cases remains unknown. There have been sustained health care-associated human outbreaks in Saudi Arabia and South Korea, the latter originating from one traveler returning from the Middle East. Transmission mechanisms are poorly understood; for health care, this may include environmental contamination. Various potential therapeutics have been identified, but not yet evaluated in human clinical trials. At least one candidate vaccine has progressed to Phase I trials.

CONCLUSIONS

There has been substantial MERS-CoV research since 2012, but significant knowledge gaps persist, especially in epidemiology and natural history of the infection. There have been few rigorous studies of baseline prevalence, transmission, and spectrum of disease. Terms such as "camel exposure" and the epidemiological relationships of cases should be clearly defined and standardized. We strongly recommend a shared and accessible registry or database. Coronaviruses will likely continue to emerge, arguing for a unified "One Health" approach.

Individual and network characteristic associated with hospital-acquired Middle East Respiratory Syndrome coronavirus

BACKGROUND

During outbreaks of infectious diseases, transmission of the pathogen can form networks of infected individuals connected either directly or indirectly.

METHODS

Network centrality metrics were used to characterize hospital-acquired Middle East Respiratory Syndrome Coronavirus (HA-MERS) outbreaks in the Kingdom of Saudi Arabia between 2012 and 2016. Covariate-adjusted multivariable logistic regression models were applied to assess the effect of individual level risk factors and network level metrics associated with increase in length of hospital stay and risk of deaths from MERS.

RESULTS

About 27% of MERS cases were hospital acquired during the study period. The median age of healthcare workers and hospitalized patients were 35 years and 63 years, respectively, Although HA-MERS were more connected, we found no significant difference in degree centrality metrics between HA-MERS and non-HA-MERS cases. Pre-existing medical conditions (adjusted Odds ratio (aOR)=2.43, 95% confidence interval: (CI) [1.11-5.33]) and hospitalized patients (aOR=29.99, 95% CI [1.80-48.65]) were the strongest risk predictors of death from MERS. The risk of death associated with 1-day increased length of stay was significantly higher for patients with comorbidities.

CONCLUSION

Our investigation also revealed that patients with an HA-MERS infection experienced a significantly longer hospital stay and were more likely to die from the disease. Healthcare worker should be reminded of their potential role as hubs for pathogens because of their proximity to and regular interaction with infected patients. On the other hand, this study has shown that while healthcare workers acted as epidemic attenuators, hospitalized patients played the role of an epidemic amplifier.

Identified Transmission Dynamics of Middle East Respiratory Syndrome Coronavirus Infection During an Outbreak: Implications of an Overcrowded Emergency Department

A total 130 cases of Middle East respiratory syndrome coronavirus were identified during a large hospital outbreak in Saudi Arabia; 87 patients and 43 healthcare workers. The majority (80%) of transmission was healthcare-acquired (HAI) infection, with 4 generations of HAI transmission. The emergency department was the main location of exposure.

Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

Contact history is crucial during an infectious disease outbreak and vital when seeking to understand and predict the spread of infectious diseases in human populations. The transmission connectivity networks of people infected with highly contagious Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia were assessed to identify super-spreading events among the infected patients between 2012 and 2016. Of the 1379 MERS cases recorded during the study period, 321 (23.3%) cases were linked to hospital infection, out of which 203 (14.7%) cases occurred among healthcare workers. There were 1113 isolated cases while the number of recorded contacts per MERS patient is between 1 (n=210) and 17 (n=1), with a mean of 0.27 (SD = 0.76). Five super-important nodes were identified based on their high number of connected contacts worthy of prioritization (at least degree of 5). The number of secondary cases in each SSE varies (range, 5–17). The eigenvector centrality was significantly (p < 0.05) associated with place of exposure, with hospitals having on average significantly higher eigenvector centrality than other places of exposure. Results suggested that being a healthcare worker has a higher eigenvector centrality score on average than being nonhealthcare workers. Pathogenic droplets are easily transmitted within a confined area of hospitals; therefore, control measures should be put in place to curtail the number of hospital visitors and movements of nonessential staff within the healthcare facility with MERS cases.

Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches

BACKGROUND

The Zika virus was first discovered in 1947. It was neglected until a major outbreak occurred on Yap Island, Micronesia, in 2007. Teratogenic effects resulting in microcephaly in newborn infants is the greatest public health threat. In 2016, the Zika virus epidemic was declared as a Public Health Emergency of International Concern (PHEIC). Consequently, mathematical models were constructed to explicitly elucidate related transmission dynamics.

SURVEY METHODOLOGY

In this review article, two steps of journal article searching were performed. First, we attempted to identify mathematical models previously applied to the study of vector-borne diseases using the search terms "dynamics," "mathematical model," "modeling," and "vector-borne" together with the names of vector-borne diseases including chikungunya, dengue, malaria, West Nile, and Zika. Then the identified types of model were further investigated. Second, we narrowed down our survey to focus on only Zika virus research. The terms we searched for were "compartmental," "spatial," "metapopulation," "network," "individual-based," "agent-based" AND "Zika." All relevant studies were included regardless of the year of publication. We have collected research articles that were published before August 2017 based on our search criteria. In this publication survey, we explored the Google Scholar and PubMed databases.

RESULTS

We found five basic model architectures previously applied to vector-borne virus studies, particularly in Zika virus simulations. These include compartmental, spatial, metapopulation, network, and individual-based models. We found that Zika models carried out for early epidemics were mostly fit into compartmental structures and were less complicated compared to the more recent ones. Simple models are still commonly used for the timely assessment of epidemics. Nevertheless, due to the availability of large-scale real-world data and computational power, recently there has been growing interest in more complex modeling frameworks.

DISCUSSION

Mathematical models are employed to explore and predict how an infectious disease spreads in the real world, evaluate the disease importation risk, and assess the effectiveness of intervention strategies. As the trends in modeling of infectious diseases have been shifting towards data-driven approaches, simple and complex models should be exploited differently. Simple models can be produced in a timely fashion to provide an estimation of the possible impacts. In contrast, complex models integrating real-world data require more time to develop but are far more realistic. The preparation of complicated modeling frameworks prior to the outbreaks is recommended, including the case of future Zika epidemic preparation.

Estimating and modelling the transmissibility of Middle East Respiratory Syndrome CoronaVirus during the 2015 outbreak in the Republic of Korea

Background

Emerging respiratory infections represent a significant public health threat. Because of their novelty, there are limited measures available to control their early spread. Learning from past outbreaks is important for future preparation. The Middle Eastern Respiratory Syndrome CoronaVirus (MERS‐CoV ) 2015 outbreak in the Republic of Korea (ROK) provides one such opportunity.

Objectives

We demonstrated through quantitative methodologies how to estimate MERS‐CoV's transmissibility and identified the effective countermeasures that stopped its spread.

Methods

Using the outbreak data, statistical methods were employed to estimate the basic reproductive number R₀, the average number of secondary cases produced by a typical primary case during its entire infectious period in a fully susceptible population. A transmission dynamics model was also proposed to estimate R₀ and to identify the most effective countermeasures. The consistency between results will provide cross‐validation of the approaches.

Results

R₀ ranged from 2.5 with 95% confidence interval (CI): [1.7, 3.1] (using the sequential Bayesian method) to 7.2 with 95% CI: [5.3, 9.4] (using the Nowcasting method). Estimates from transmission model were higher but overlapped with these. Personal protection and rapid confirmation of cases were identified as the most important countermeasures.

Conclusions

Our estimates were in agreement with others from the ROK outbreak, albeit significantly higher than estimates based on other small outbreaks and sporadic cases of MERS‐CoV. The large‐scale outbreak in the ROK was jointly due to the high transmissibility in the healthcare‐associated setting and the Korean culture‐associated contact behaviour. Limiting such behaviour by rapidly identifying and isolating cases and avoiding high‐risk contacts effectively stopped further transmission.

The clinical and virological features of the first imported case causing MERS-CoV outbreak in South Korea, 2015

BACKGROUND

In 2015, the largest outbreak of Middle East respiratory syndrome coronavirus (MERS-CoV) infection outside the Middle East occurred in South Korea. We summarized the epidemiological, clinical, and laboratory findings of the first Korean case of MERS-CoV and analyzed whole-genome sequences of MERS-CoV derived from the patient.

CASE PRESENTATION

A 68-year-old man developed fever and myalgia 7 days after returning to Korea, following a 10-day trip to the Middle East. Before diagnosis, he visited 4 hospitals, potentially resulting in secondary transmission to 28 patients. On admission to the National Medical Center (day 9, post-onset of clinical illness), he presented with drowsiness, hypoxia, and multiple patchy infiltrations on the chest radiograph. He was intubated (day 12) because of progressive acute respiratory distress syndrome (ARDS) and INF-α2a and ribavirin treatment was commenced. The treatment course was prolonged by superimposed ventilator associated pneumonia. MERS-CoV PCR results converted to negative from day 47 and the patient was discharged (day 137), following rehabilitation therapy. The complete genome sequence obtained from a sputum sample (taken on day 11) showed the highest sequence similarity (99.59%) with the virus from an outbreak in Riyadh, Saudi Arabia, in February 2015.

CONCLUSIONS

The first case of MERS-CoV infection had high transmissibility and was associated with a severe clinical course. The patient made a successful recovery after early treatment with antiviral agents and adequate supportive care. This first case in South Korea became a super-spreader because of improper infection control measures, rather than variations of the virus.

Comparative epidemiology of Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia and South Korea

MERS-CoV infection emerged in the Kingdom of Saudi Arabia (KSA) in 2012 and has spread to 26 countries. However, 80% of all cases have occurred in KSA. The largest outbreak outside KSA occurred in South Korea (SK) in 2015. In this report, we describe an epidemiological comparison of the two outbreaks. Data from 1299 cases in KSA (2012-2015) and 186 cases in SK (2015) were collected from publicly available resources, including FluTrackers, the World Health Organization (WHO) outbreak news and the Saudi MOH (MOH). Descriptive analysis, t-tests, Chi-square tests and binary logistic regression were conducted to compare demographic and other characteristics (comorbidity, contact history) of cases by nationality. Epidemic curves of the outbreaks were generated. The mean age of cases was 51 years in KSA and 54 years in SK. Older males (⩾70 years) were more likely to be infected or to die from MERS-CoV infection, and males exhibited increased rates of comorbidity in both countries. The epidemic pattern in KSA was more complex, with animal-to-human, human-to-human, nosocomial and unknown exposure, whereas the outbreak in SK was more clearly nosocomial. Of the 1186 MERS cases in KSA with reported risk factors, 158 (13.3%) cases were hospital associated compared with 175 (94.1%) in SK, and an increased proportion of cases with unknown exposure risk was found in KSA (710, 59.9%). In a globally connected world, travel is a risk factor for emerging infections, and health systems in all countries should implement better triage systems for potential imported cases of MERS-CoV to prevent large epidemics.

Assessing the transmission dynamics of measles in Japan, 2016

OBJECTIVES

Despite the verification of measles elimination, Japan experienced multiple generations of measles transmission following importation events in 2016. The purpose of the present study was to analyze the transmission dynamics of measles in Japan, 2016, estimating the transmission potential in the partially vaccinated population.

METHODS

All diagnosed measles cases were notified to the government, and the present study analyzed two pieces of datasets independently, i.e., the transmission tree of the largest outbreak in Osaka (n=49) and the final size distribution of all importation events (n=23 events). Branching process model was employed to estimate the effective reproduction number R_v, and the analysis of transmission tree in Osaka enabled us to account for the timing of introducing contact tracing and case isolation.

RESULTS

Employing a negative binomial distribution for the offspring distribution, the model with time-dependent decline in R_v due to interventions appeared to best fit to the transmission tree data with R_v of 9.20 (95% CI (confidence interval): 2.08, 150.68) and the dispersion parameter 0.32 (95% CI: 0.07, 1.17) before interventions were introduced. The relative transmissibility in the presence of interventions from week 34 was estimated at 0.005. Analyzing the final size distribution, models for subcritical and supercritical processes fitted equally well to the observed data, and the estimated reproduction number from both models did not exclude the possibility that R_v>1.

CONCLUSIONS

Our results likely reflect the highly contagious nature of measles, indicating that Japan is at risk of observing multiple generations of measles transmission given imported cases. Considering that importation events may continue in the future, supplementary vaccination of adults needs to be considered.

The global spread of Middle East respiratory syndrome: an analysis fusing traditional epidemiological tracing and molecular phylodynamics

BACKGROUND

Since its discovery in 2012, over 1700 confirmed cases of Middle East Respiratory Syndrome (MERS) have been documented worldwide and more than a third of those cases have died. While the greatest number of cases has occurred in Saudi Arabia, the recent export of MERS-coronavirus (MERS-CoV) to Republic of Korea showed that a pandemic is a possibility that cannot be ignored. Due to the deficit of knowledge in transmission methodology, targeted treatment and possible vaccines, understanding this virus should be a priority. Our aim was to combine epidemiological data from literature with genetic information from viruses sequenced around the world to present a phylodynamic picture of MERS spread molecular level to global scale.

METHODS

We performed a qualitative meta-analysis of all laboratory confirmed cases worldwide to date based on literature, with emphasis on international transmission and healthcare associated infections. In parallel, we used publicly available MERS-CoV genomes from GenBank to create a phylogeographic tree, detailing geospatial timeline of viral evolution.

RESULTS

Several healthcare associated outbreaks starting with the retrospectively identified hospital outbreak in Jordan to the most recent outbreak in Riyadh, Saudi Arabia have occurred. MERS has also crossed many oceans, entering multiple nations in eight waves between 2012 and 2015. In this paper, the spatiotemporal history of MERS cases, as documented epidemiologically, was examined by Bayesian phylogenetic analysis. Distribution of sequences into geographic clusters and interleaving of MERS-CoV sequences from camels among those isolated from humans indicated that multiple zoonotic introductions occurred in endemic nations. We also report a summary of basic reproduction numbers for MERS-CoV in humans and camels.

CONCLUSION

Together, these analyses can help us identify factors associated with viral evolution and spread as well as establish efficacy of infection control measures. The results are especially pertinent to countries without current MERS-CoV endemic, since their unfamiliarity makes them particularly susceptible to uncontrollable spread of a virus that may be imported by travelers.

Risk of MERS importation and onward transmission: a systematic review and analysis of cases reported to WHO

BACKGROUND

The continuing circulation of MERS in the Middle East makes the international dissemination of the disease a permanent threat. To inform risk assessment, we investigated the spatiotemporal pattern of MERS global dissemination and looked for factors explaining the heterogeneity observed in transmission events following importation.

METHODS

We reviewed imported MERS cases worldwide up to July 2015. We modelled importations in time based on air travel combined with incidence in Middle East. We used the detailed history of MERS case management after importation (time to hospitalization and isolation, number of hospitals visited,…) in logistic regression to identify risk factors for secondary transmission. We assessed changes in time to hospitalization and isolation in relation to collective and public health attention to the epidemic, measured by three indicators (Google Trends, ProMED-mail, Disease Outbreak News).

RESULTS

Modelled importation events were found to reproduce both the temporal and geographical structure of those observed - the Pearson correlation coefficient between predicted and observed monthly time series was large (r = 0.78, p < 10(-4)). The risk of secondary transmission following importation increased with the time to case isolation or death (OR = 1.7 p = 0.04) and more precisely with the duration of hospitalization (OR = 1.7, p = 0.02). The average daily number of secondary cases was 0.02 [0.0,0.12] in the community and 0.20 [0.03,9.0] in the hospital. Time from hospitalisation to isolation decreased in periods of high public health attention (2.33 ± 0.34 vs. 6.44 ± 0.97 days during baseline attention).

CONCLUSIONS

Countries at risk of importation should focus their resources on strict infection control measures for the management of potential cases in healthcare settings and on prompt MERS cases identification. Individual and collective awareness are key to substantially improve such preparedness.

A dynamic compartmental model for the Middle East respiratory syndrome outbreak in the Republic of Korea: A retrospective analysis on control interventions and superspreading events

The 2015 Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea has provided an opportunity to improve our understanding of the spread of MERS linked to healthcare settings. Here we designed a dynamic transmission model to analyze the MERS outbreak in the Republic of Korea based on confirmed cases reported during the period May 20-July 4, 2015. Our model explicitly incorporates superspreading events and time-dependent transmission and isolation rates. Our model was able to provide a good fit to the trajectory of the outbreak and was useful to analyze the role of hypothetical control scenarios. Specifically, we assessed the impact of the timing of control measures, especially associated with a reduction of the transmission rate and diagnostic delays on outbreak size and duration. Early interventions within 1week after the epidemic onset, for instance, including the initial government announcement to the public about the list of hospitals exposed to MERS coronavirus (MERS-CoV), show a promising means to reduce the size (>71%) and duration (>35%) of the MERS epidemic. Finally, we also present results of an uncertainty analysis focused on the role of superspreading events.

Predicting the international spread of Middle East respiratory syndrome (MERS)

Background

The Middle East respiratory syndrome (MERS) associated coronavirus has been imported via travelers into multiple countries around the world. In order to support risk assessment practice, the present study aimed to devise a novel statistical model to quantify the country-level risk of experiencing an importation of MERS case.

Methods

We analyzed the arrival time of each reported MERS importation around the world, i.e., the date on which imported cases entered a specific country, which was modeled as a dependent variable in our analysis. We also used openly accessible data including the airline transportation network to parameterize a hazard-based risk prediction model. The hazard was assumed to follow an inverse function of the effective distance (i.e., the minimum effective length of a path from origin to destination), which was calculated from the airline transportation data, from Saudi Arabia to each country. Both country-specific religion and the incidence data of MERS in Saudi Arabia were used to improve our model prediction.

Results

Our estimates of the risk of MERS importation appeared to be right skewed, which facilitated the visual identification of countries at highest risk of MERS importations in the right tail of the distribution. The simplest model that relied solely on the effective distance yielded the best predictive performance (Area under the curve (AUC) = 0.943) with 100 % sensitivity and 79.6 % specificity. Out of the 30 countries estimated to be at highest risk of MERS case importation, 17 countries (56.7 %) have already reported at least one importation of MERS. Although model fit measured by Akaike Information Criterion (AIC) was improved by including country-specific religion (i.e. Muslim majority country), the predictive performance as measured by AUC was not improved after accounting for this covariate.

Conclusions

Our relatively simple statistical model based on the effective distance derived from the airline transportation network data was found to help predicting the risk of importing MERS at the country level. The successful application of the effective distance model to predict MERS importations, particularly when computationally intensive large-scale transmission models may not be immediately applicable could have been benefited from the particularly low transmissibility of the MERS coronavirus.

Risk of global spread of Middle East respiratory syndrome coronavirus (MERS-CoV) via the air transport network

BACKGROUND

Middle East respiratory syndrome coronavirus (MERS-CoV) emerged from the Kingdom of Saudi Arabia (KSA) in 2012 and has since spread to 26 countries. All cases reported so far have either been in the Middle East or linked to the region through passenger air travel, with the largest outbreak outside KSA occurring in South Korea. Further international spread is likely due to the high travel volumes of global travel, as well as the occurrence of large annual mass gathering such as the Haj and Umrah pilgrimages that take place in the region.

METHODS

In this study, a transport network modelling framework was used to quantify the risk of MERS-CoV spreading internationally via air travellers. All regions connected to MERS-CoV affected countries via air travel are considered, and the countries at highest risk of travel-related importations of MERS-CoV were identified, ranked and compared with actual spread of MERS cases.

RESULTS

The model identifies all countries that have previously reported a travel acquired case to be in the top 50 at-risk countries. India, Pakistan and Bangladesh are the highest risk countries which have yet to report a case, and should be prepared for the possibility of (pilgrims and general) travellers returning infected with MERS-CoV. In addition, the UK, Egypt, Turkey and the USA are at risk of more cases.

CONCLUSIONS

We have demonstrated a risk-analysis approach, using travel patterns, to prioritize countries at highest risk for MERS-CoV importations. In order to prevent global outbreaks such as the one seen in South Korea, it is critical for high-risk countries to be prepared and have appropriate screening and triage protocols in place to identify travel-related cases of MERS-CoV. The results from the model can be used by countries to prioritize their airport and hospital screening and triage protocols.

Estimates of the risk of large or long-lasting outbreaks of Middle East respiratory syndrome after importations outside the Arabian Peninsula

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MERS outbreak clusters outside the Arabian Peninsula ranged in size from 1 to 186.

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Cluster data show declining transmission rate in later transmission generations.

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Model projects tempered risk of large, long-lasting outbreaks after importations.

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Explosive outbreaks are possible, but control measures are likely to be effective.

We quantify outbreak risk after importations of Middle East respiratory syndrome outside the Arabian Peninsula. Data from 31 importation events show strong statistical support for lower transmissibility after early transmission generations. Our model projects the risk of ≥10, 100, and 500 transmissions as 11%, 2%, and 0.02%, and ≥1, 2, 3, and 4 generations as 23%, 14%, 0.9%, and 0.05%, respectively. Our results suggest tempered risk of large, long-lasting outbreaks with appropriate control measures.

Middle East Respiratory Syndrome Coronavirus NS4b Protein Inhibits Host RNase L Activation

Middle East respiratory syndrome coronavirus (MERS-CoV) is the first highly pathogenic human coronavirus to emerge since severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002. Like many coronaviruses, MERS-CoV carries genes that encode multiple accessory proteins that are not required for replication of the genome but are likely involved in pathogenesis. Evasion of host innate immunity through interferon (IFN) antagonism is a critical component of viral pathogenesis. The IFN-inducible oligoadenylate synthetase (OAS)-RNase L pathway activates upon sensing of viral double-stranded RNA (dsRNA). Activated RNase L cleaves viral and host single-stranded RNA (ssRNA), which leads to translational arrest and subsequent cell death, preventing viral replication and spread. Here we report that MERS-CoV, a lineage C Betacoronavirus, and related bat CoV NS4b accessory proteins have phosphodiesterase (PDE) activity and antagonize OAS-RNase L by enzymatically degrading 2′,5′-oligoadenylate (2-5A), activators of RNase L. This is a novel function for NS4b, which has previously been reported to antagonize IFN signaling. NS4b proteins are distinct from lineage A Betacoronavirus PDEs and rotavirus gene-encoded PDEs, in having an amino-terminal nuclear localization signal (NLS) and are localized mostly to the nucleus. However, the expression level of cytoplasmic MERS-CoV NS4b protein is sufficient to prevent activation of RNase L. Finally, this is the first report of an RNase L antagonist expressed by a human or bat coronavirus and provides a specific mechanism by which this occurs. Our findings provide a potential mechanism for evasion of innate immunity by MERS-CoV while also identifying a potential target for therapeutic intervention.

Middle East respiratory syndrome coronavirus (MERS-CoV) is the first highly pathogenic human coronavirus to emerge since severe acute respiratory syndrome coronavirus (SARS-CoV). MERS-CoV, like other coronaviruses, carries genes that encode accessory proteins that antagonize the host antiviral response, often the type I interferon response, and contribute to virulence. We found that MERS-CoV NS4b and homologs from related lineage C bat betacoronaviruses BtCoV-SC2013 (SC2013) and BtCoV-HKU5 (HKU5) are members of the 2H-phosphoesterase (2H-PE) enzyme family with phosphodiesterase (PDE) activity. Like murine coronavirus NS2, a previously characterized PDE, MERS NS4b, can antagonize activation of the OAS-RNase L pathway, an interferon-induced potent antiviral activity. Furthermore, MERS-CoV mutants with deletion of genes encoding accessory proteins NS3 to NS5 or NS4b alone or inactivation of the PDE can activate RNase L during infection of Calu-3 cells. Our report may offer a potential target for therapeutic intervention if NS4b proves to be critical to pathogenesis in in vivo models of MERS-CoV infection.

Identifying determinants of heterogeneous transmission dynamics of the Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea, 2015: a retrospective epidemiological analysis

OBJECTIVES

To investigate the heterogeneous transmission patterns of Middle East respiratory syndrome (MERS) in the Republic of Korea, with a particular focus on epidemiological characteristics of superspreaders.

DESIGN

Retrospective epidemiological analysis.

SETTING

Multiple healthcare facilities of secondary and tertiary care centres in an urban setting.

PARTICIPANTS

A total of 185 laboratory-confirmed cases with partially known dates of illness onset and most likely sources of infection.

PRIMARY AND SECONDARY OUTCOME MEASURES

Superspreaders were identified using the transmission tree. The reproduction number, that is, the average number of secondary cases produced by a single primary case, was estimated as a function of time and according to different types of hosts.

RESULTS

A total of five superspreaders were identified. The reproduction number throughout the course of the outbreak was estimated at 1.0 due to reconstruction of the transmission tree, while the variance of secondary cases generated by a primary case was 52.1. All of the superspreaders involved in this outbreak appeared to have generated a substantial number of contacts in multiple healthcare facilities (association: p<0.01), generating on average 4.0 (0.0-8.6) and 28.6 (0.0-63.9) secondary cases among patients who visited multiple healthcare facilities and others. The time-dependent reproduction numbers declined substantially below the value of 1 on and after 13 June 2015.

CONCLUSIONS

Superspreaders who visited multiple facilities drove the epidemic by generating a disproportionate number of secondary cases. Our findings underscore the need to limit the contacts in healthcare settings. Contact tracing efforts could assist early laboratory testing and diagnosis of suspected cases.

Modeling the Transmission of Middle East Respirator Syndrome Corona Virus in the Republic of Korea

The 2015 epidemic of Middle East respiratory syndrome (MERS) in the Republic of Korea has been the largest outbreak outside Middle East. This epidemic had caused 185 laboratory-confirmed cases and 36 deaths in the Republic of Korea until September 2, 2015, which attracted public’s attention. Based on the detailed data of patients released by World Health Organization (WHO) and actual propagation of the epidemic, we construct two dynamical models to simulate the propagation processes from May 20 to June 8 and from June 9 to July 10, 2015, respectively and find that the basic reproduction number R₀ reaches up to 4.422. The numerical analysis shows that the reasons of the outbreak spread quickly are lack of self-protection sense and targeted control measures. Through partial correction analysis, the parameters β₁ and γ have strong correlations with R₀, i.e., the infectivity and proportion of the asymptomatic infected cases have much influence on the spread of disease. By sensitivity analysis, strengthening self-protection ability of susceptible and quickly isolating or monitoring close contacts are effective measures to control the disease.

Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach

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We modelled MERS epidemic in the Middle East region up to September 2014.

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We assessed spatiotemporal variation in zoonotic and human transmission.

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Spring 2014 wave showed a 17-fold and 3-fold increase in the above transmissions.

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Zoonotic transmission has a larger spatial heterogeneity than human transmission.

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Human transmission is more frequent than expected (75% of cases vs. 34%).

MERS coronavirus cases notified in the Middle East region since the identification of the virus in 2012 have displayed variations in time and across geography. Through a combined modelling approach, we estimate the rates of generation of cases along the zoonotic and human-to-human transmission routes and assess their spatiotemporal heterogeneity. We consider all cases notified to WHO from March 2012 to mid-September 2014. We use a stochastic modelling of the time series of case incidence in the Middle East region to estimate time- and space-dependent zoonotic and human-to-human transmission parameters. The model also accounts for possible lack of identification of secondary transmissions among notified cases. This approach is combined with the analysis of imported cases out of the region to assess the rate of underreporting of cases. Out of a total of 32 possible models, based on different parameterisation and scenario considered, the best-fit model is characterised by a large heterogeneity in time and across space for both zoonotic and human-to-human transmission. The variation in time that occurred during Spring 2014 led to a 17-fold and 3-fold increase in the two transmissions, respectively, bringing the reproductive rate to values above 1 during that period for all regions under study. The model suggests that 75% of MERS-CoV cases are secondary cases (human-to-human transmission), which is substantially higher than the 34% of reported cases with an epidemiological link to another case. Overall, estimated reporting rate is 0.26. Our findings show a higher level of spatial heterogeneity in zoonotic transmission compared to human-to-human, highlighting the strong environmental component of the epidemic. Since sporadic introductions are predicted to be a small proportion of notified cases and are responsible for triggering secondary transmissions, a more comprehensive understanding of zoonotic source and path of transmission could be critical to limit the epidemic spread.

2015 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) nosocomial outbreak in South Korea: insights from modeling

Background. Since the emergence of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012, more than 1,300 laboratory confirmed cases of MERS-CoV infections have been reported in Asia, North Africa, and Europe by July 2015. The recent MERS-CoV nosocomial outbreak in South Korea quickly became the second largest such outbreak with 186 total cases and 36 deaths in a little more than one month, second only to Saudi Arabia in country-specific number of reported cases. Methods. We use a simple mathematical model, the Richards model, to trace the temporal course of the South Korea MERS-CoV outbreak. We pinpoint its outbreak turning point and its transmissibility via basic reproduction number R 0 in order to ascertain the occurrence of this nosocomial outbreak and how it was quickly brought under control. Results. The estimated outbreak turning point of ti = 23.3 days (95% CI [22.6-24.0]), or 23-24 days after the onset date of the index case on May 11, pinpoints June 3-4 as the time of the turning point or the peak incidence for this outbreak by onset date. R 0 is estimated to range between 7.0 and 19.3. Discussion and Conclusion. The turning point of the South Korea MERS-CoV outbreak occurred around May 27-29, when control measures were quickly implemented after laboratory confirmation of the first cluster of nosocomial infections by the index patient. Furthermore, transmissibility of MERS-CoV in the South Korea outbreak was significantly higher than those reported from past MERS-CoV outbreaks in the Middle East, which is attributable to the nosocomial nature of this outbreak. Our estimate of R 0 for the South Korea MERS-CoV nosocomial outbreak further highlights the importance and the risk involved in cluster infections and superspreading events in crowded settings such as hospitals. Similar to the 2003 SARS epidemic, outbreaks of infectious diseases with low community transmissibility like MERS-CoV could still occur initially with large clusters of nosocomial infections, but can be quickly and effectively controlled with timely intervention measures.

Estimating the risk of Middle East respiratory syndrome (MERS) death during the course of the outbreak in the Republic of Korea, 2015

OBJECTIVES

A large cluster of the Middle East respiratory syndrome (MERS) linked to healthcare setting occurred from May to July 2015 in the Republic of Korea. The present study aimed to estimate the case fatality ratio (CFR) by appropriately taking into account the time delay from illness onset to death. We then compare our estimate against previously published values of the CFR for MERS, i.e., 20% and 40%.

METHODS

Dates of illness onset and death of the MERS outbreak in the Republic of Korea were extracted from secondary data sources. Using the known distribution of time from illness onset to death and an integral equation model, we estimated the delay-adjusted risk of MERS death for the South Korean cluster.

RESULTS

Our most up-to-date estimate of CFR for the MERS outbreak in South Korea was estimated at 20.0% (95% confidence intervals (CI): 14.6, 26.2). During the course of the outbreak, estimate of the CFR in real time appeared to have decreased and become significantly lower than 40%.

CONCLUSIONS

The risk of MERS death in Korea was consistent with published CFR. The estimate decreased with time perhaps due to time-dependent increase in case ascertainment. Crude ratio of cumulative deaths to cases underestimates the actual risk of MERS death because of time delay from illness onset to death.

Real-time characterization of risks of death associated with the Middle East respiratory syndrome (MERS) in the Republic of Korea, 2015

BACKGROUND

An outbreak of the Middle East respiratory syndrome (MERS), comprising 185 cases linked to healthcare facilities, occurred in the Republic of Korea from May to July 2015. Owing to the nosocomial nature of the outbreak, it is particularly important to gain a better understanding of the epidemiological determinants characterizing the risk of MERS death in order to predict the heterogeneous risk of death in medical settings.

METHODS

We have devised a novel statistical model that identifies the risk of MERS death during the outbreak in real time. While accounting for the time delay from illness onset to death, risk factors for death were identified using a linear predictor tied to a logit model. We employ this approach to (1) quantify the risks of death and (2) characterize the temporal evolution of the case fatality ratio (CFR) as case ascertainment greatly improved during the course of the outbreak.

RESULTS

Senior persons aged 60 years or over were found to be 9.3 times (95% confidence interval (CI), 5.3-16.9) more likely to die compared to younger MERS cases. Patients under treatment were at a 7.8-fold (95% CI, 4.0-16.7) significantly higher risk of death compared to other MERS cases. The CFR among patients aged 60 years or older under treatment was estimated at 48.2% (95% CI, 35.2-61.3) as of July 31, 2015, while the CFR among other cases was estimated to lie below 15%. From June 6, 2015, onwards, the CFR declined 0.3-fold (95% CI, 0.1-1.1) compared to the earlier epidemic period, which may perhaps reflect enhanced case ascertainment following major contact tracing efforts.

CONCLUSIONS

The risk of MERS death was significantly associated with older age as well as treatment for underlying diseases after explicitly adjusting for the delay between illness onset and death. Because MERS outbreaks are greatly amplified in the healthcare setting, enhanced infection control practices in medical facilities should strive to shield risk groups from MERS exposure.

Transmission characteristics of MERS and SARS in the healthcare setting: a comparative study

BACKGROUND

The Middle East respiratory syndrome (MERS) coronavirus has caused recurrent outbreaks in the Arabian Peninsula since 2012. Although MERS has low overall human-to-human transmission potential, there is occasional amplification in the healthcare setting, a pattern reminiscent of the dynamics of the severe acute respiratory syndrome (SARS) outbreaks in 2003. Here we provide a head-to-head comparison of exposure patterns and transmission dynamics of large hospital clusters of MERS and SARS, including the most recent South Korean outbreak of MERS in 2015.

METHODS

To assess the unexpected nature of the recent South Korean nosocomial outbreak of MERS and estimate the probability of future large hospital clusters, we compared exposure and transmission patterns for previously reported hospital clusters of MERS and SARS, based on individual-level data and transmission tree information. We carried out simulations of nosocomial outbreaks of MERS and SARS using branching process models rooted in transmission tree data, and inferred the probability and characteristics of large outbreaks.

RESULTS

A significant fraction of MERS cases were linked to the healthcare setting, ranging from 43.5 % for the nosocomial outbreak in Jeddah, Saudi Arabia, in 2014 to 100 % for both the outbreak in Al-Hasa, Saudi Arabia, in 2013 and the outbreak in South Korea in 2015. Both MERS and SARS nosocomial outbreaks are characterized by early nosocomial super-spreading events, with the reproduction number dropping below 1 within three to five disease generations. There was a systematic difference in the exposure patterns of MERS and SARS: a majority of MERS cases occurred among patients who sought care in the same facilities as the index case, whereas there was a greater concentration of SARS cases among healthcare workers throughout the outbreak. Exposure patterns differed slightly by disease generation, however, especially for SARS. Moreover, the distributions of secondary cases per single primary case varied highly across individual hospital outbreaks (Kruskal-Wallis test; P < 0.0001), with significantly higher transmission heterogeneity in the distribution of secondary cases for MERS than SARS. Simulations indicate a 2-fold higher probability of occurrence of large outbreaks (>100 cases) for SARS than MERS (2 % versus 1 %); however, owing to higher transmission heterogeneity, the largest outbreaks of MERS are characterized by sharper incidence peaks. The probability of occurrence of MERS outbreaks larger than the South Korean cluster (n = 186) is of the order of 1 %.

CONCLUSIONS

Our study suggests that the South Korean outbreak followed a similar progression to previously described hospital clusters involving coronaviruses, with early super-spreading events generating a disproportionately large number of secondary infections, and the transmission potential diminishing greatly in subsequent generations. Differences in relative exposure patterns and transmission heterogeneity of MERS and SARS could point to changes in hospital practices since 2003 or differences in transmission mechanisms of these coronaviruses.

Probable transmission chains of Middle East respiratory syndrome coronavirus and the multiple generations of secondary infection in South Korea

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As of July 14, 2015, the South Korean outbreak of Middle East respiratory syndrome coronavirus (MERS-CoV) infection has involved 185 secondary infections belonging to three overlapping generations of cases who have contracted the virus almost exclusively in the healthcare environment.

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Fomite transmission may explain a significant proportion of the infections occurring in the absence of direct contact with infected cases.

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The analysis of publicly available data collected from multiple sources, including the media, is useful for describing the epidemic history of an infectious disease outbreak.

Background

In May 2015, South Korea reported its first case of Middle East respiratory syndrome coronavirus (MERS-CoV) infection in a 68-year-old man with a history of travel in the Middle East. In the presence of secondary infections, an understanding of the transmission dynamics of the virus is crucial. The aim of this study was to characterize the transmission chains of MERS-CoV infection in the current South Korean outbreak.

Methods

Individual-level data from multiple sources were collected and used for epidemiological analyses.

Results

As of July 14, 2015, 185 confirmed cases of MERS have been reported in the Korean outbreak. Three generations of secondary infection, with over half belonging to the second generation, could be delineated. Hospital infection was found to be the most important cause of virus transmission, affecting largely non-healthcare workers (154/184). Healthcare switching has probably accounted for the emergence of multiple generations of secondary infection. Fomite transmission may explain a significant proportion of the infections occurring in the absence of direct contact with infected cases.

Conclusions

Publicly available data from multiple sources, including the media, are useful to describe the epidemic history of an outbreak. The effective control of MERS-CoV hinges on the upholding of infection control standards and an understanding of health-seeking behaviours in the community.

The role of superspreading in Middle East respiratory syndrome coronavirus (MERS-CoV) transmission

As at 15 June 2015, a large transmission cluster of Middle East respiratory syndrome coronavirus (MERSCoV)was ongoing in South Korea. To examine the potential for such events, we estimated the level of heterogeneity in MERS-CoV transmission by analyzing data on cluster size distributions. We found substantial potential for superspreading; even though it is likely that R0 < 1 overall, our analysis indicates that cluster sizes of over 150 cases are not unexpected forMERS-CoV infection.