Association between Severity of MERS-CoV Infection and Incubation Period

Constrained numerical deconvolution using orthogonal polynomials

In this article, we present an enhanced version of Cutler's deconvolution method to address the limitations of the original algorithm in estimating realistic input and output parameters. Cutler's method, based on orthogonal polynomials, suffers from unconstrained solutions, leading to the lack of realism in the deconvolved signals in some applications. Our proposed approach incorporates constraints using a ridge factor and Lagrangian multipliers in an iterative fashion, maintaining Cutler's iterative projection-based nature. This extension avoids the need for external optimization solvers, making it particularly suitable for applications requiring constraints on inputs and outputs. We demonstrate the effectiveness of the proposed method through two practical applications: the estimation of COVID-19 curves and the study of mavoglurant, an experimental drug. Our results show that the extended method presents a sum of squared residuals in the same order of magnitude of that of the original Cutler's method and other widely known unconstrained deconvolution techniques, but obtains instead physically plausible solutions, correcting the errors introduced by the alternative methods considered, as illustrated in our case studies.

Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study

BACKGROUND

Novel coronaviruses have emerged and caused major epidemics and pandemics in the past 2 decades, including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, which led to the current COVID-19 pandemic. These coronaviruses are marked by their potential to produce disproportionally large transmission clusters from superspreading events (SSEs). As prompt action is crucial to contain and mitigate SSEs, real-time epidemic size estimation could characterize the transmission heterogeneity and inform timely implementation of control measures.

OBJECTIVE

This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses.

METHODS

We developed a statistical framework based on back-calculation to estimate the epidemic size of ongoing coronavirus SSEs. We first validated the framework in simulated scenarios with the epidemiological characteristics of SARS, MERS, and COVID-19 SSEs. As case studies, we retrospectively applied the framework to the Amoy Gardens SARS outbreak in Hong Kong in 2003, a series of nosocomial MERS outbreaks in South Korea in 2015, and 2 COVID-19 outbreaks originating from restaurants in Hong Kong in 2020.

RESULTS

The accuracy and precision of the estimation of epidemic size of SSEs improved with longer observation time; larger SSE size; and more accurate prior information about the epidemiological characteristics, such as the distribution of the incubation period and the distribution of the onset-to-confirmation delay. By retrospectively applying the framework, we found that the 95% credible interval of the estimates contained the true epidemic size after 37% of cases were reported in the Amoy Garden SARS SSE in Hong Kong, 41% to 62% of cases were observed in the 3 nosocomial MERS SSEs in South Korea, and 76% to 86% of cases were confirmed in the 2 COVID-19 SSEs in Hong Kong.

CONCLUSIONS

Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.

Replacement dynamics and the pathogenesis of the Alpha, Delta and Omicron variants of SARS-CoV-2

New SARS-CoV-2 variants causing COVID-19 are a major risk to public health worldwide due to the potential for phenotypic change and increases in pathogenicity, transmissibility and/or vaccine escape. Recognising signatures of new variants in terms of replacing growth and severity are key to informing the public health response. To assess this, we aimed to investigate key time periods in the course of infection, hospitalisation and death, by variant. We linked datasets on contact tracing (Contact Tracing Advisory Service), testing (the Second-Generation Surveillance System) and hospitalisation (the Admitted Patient Care dataset) for the entire length of contact tracing in the England - from March 2020 to March 2022. We modelled, for England, time delay distributions using a Bayesian doubly interval censored modelling approach for the SARS-CoV-2 variants Alpha, Delta, Delta Plus (AY.4.2), Omicron BA.1 and Omicron BA.2. This was conducted for the incubation period, the time from infection to hospitalisation and hospitalisation to death. We further modelled the growth of novel variant replacement using a generalised additive model with a negative binomial error structure and the relationship between incubation period length and the risk of a fatality using a Bernoulli generalised linear model with a logit link. The mean incubation periods for each variant were: Alpha 4.19 (95% credible interval (CrI) 4.13-4.26) days; Delta 3.87 (95% CrI 3.82-3.93) days; Delta Plus 3.92 (95% CrI 3.87-3.98) days; Omicron BA.1 3.67 (95% CrI 3.61-3.72) days and Omicron BA.2 3.48 (95% CrI 3.43-3.53) days. The mean time from infection to hospitalisation was for Alpha 11.31 (95% CrI 11.20-11.41) days, Delta 10.36 (95% CrI 10.26-10.45) days and Omicron BA.1 11.54 (95% CrI 11.38-11.70) days. The mean time from hospitalisation to death was, for Alpha 14.31 (95% CrI 14.00-14.62) days; Delta 12.81 (95% CrI 12.62-13.00) days and Omicron BA.2 16.02 (95% CrI 15.46-16.60) days. The 95th percentile of the incubation periods were: Alpha 11.19 (95% CrI 10.92-11.48) days; Delta 9.97 (95% CrI 9.73-10.21) days; Delta Plus 9.99 (95% CrI 9.78-10.24) days; Omicron BA.1 9.45 (95% CrI 9.23-9.67) days and Omicron BA.2 8.83 (95% CrI 8.62-9.05) days. Shorter incubation periods were associated with greater fatality risk when adjusted for age, sex, variant, vaccination status, vaccination manufacturer and time since last dose with an odds ratio of 0.83 (95% confidence interval 0.82-0.83) (P value < 0.05). Variants of SARS-CoV-2 that have replaced previously dominant variants have had shorter incubation periods. Conversely co-existing variants have had very similar and non-distinct incubation period distributions. Shorter incubation periods reflect generation time advantage, with a reduction in the time to the peak infectious period, and may be a significant factor in novel variant replacing growth. Shorter times for admission to hospital and death were associated with variant severity - the most severe variant, Delta, led to significantly earlier hospitalisation, and death. These measures are likely important for future risk assessment of new variants, and their potential impact on population health.

Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.

Transmission dynamics of monkeypox in the United Kingdom: contact tracing study

OBJECTIVE

To analyse the transmission dynamics of the monkeypox outbreak in the UK, declared a Public Health Emergency of International Concern in July 2022.

DESIGN

Contact tracing study, linking data on case-contact pairs and on probable exposure dates.

SETTING

Case questionnaires from the UK Health Security Agency (UKHSA), United Kingdom.

PARTICIPANTS

2746 people with polymerase chain reaction confirmed monkeypox virus in the UK between 6 May and 1 August 2022.

MAIN OUTCOME MEASURES

The incubation period and serial interval of a monkeypox infection using two bayesian time delay models-one corrected for interval censoring (ICC-interval censoring corrected) and one corrected for interval censoring, right truncation, and epidemic phase bias (ICRTC-interval censoring right truncation corrected). Growth rates of cases by reporting date, when monkeypox virus was confirmed and reported to UKHSA, were estimated using generalised additive models.

RESULTS

The mean age of participants was 37.8 years and 95% reported being gay, bisexual, and other men who have sex with men (1160 out of 1213 reporting). The mean incubation period was estimated to be 7.6 days (95% credible interval 6.5 to 9.9) using the ICC model and 7.8 days (6.6 to 9.2) using the ICRTC model. The estimated mean serial interval was 8.0 days (95% credible interval 6.5 to 9.8) using the ICC model and 9.5 days (7.4 to 12.3) using the ICRTC model. Although the mean serial interval was longer than the incubation period for both models, short serial intervals were more common than short incubation periods, with the 25th centile and the median of the serial interval shorter than the incubation period. For the ICC and ICRTC models, the corresponding estimates ranged from 1.8 days (95% credible interval 1.5 to 1.8) to 1.6 days (1.4 to 1.6) shorter at the 25th centile and 1.6 days (1.5 to 1.7) to 0.8 days (0.3 to 1.2) shorter at the median. 10 out of 13 linked patients had documented pre-symptomatic transmission. Doubling times of cases declined from 9.07 days (95% confidence interval 12.63 to 7.08) on the 6 May, when the first case of monkeypox was reported in the UK, to a halving time of 29 days (95% confidence interval 38.02 to 23.44) on 1 August.

CONCLUSIONS

Analysis of the instantaneous growth rate of monkeypox incidence indicates that the epidemic peaked in the UK as of 9 July and then started to decline. Short serial intervals were more common than short incubation periods suggesting considerable pre-symptomatic transmission, which was validated through linked patient level records. For patients who could be linked through personally identifiable data, four days was the maximum time that transmission was detected before symptoms manifested. An isolation period of 16 to 23 days would be required to detect 95% of people with a potential infection. The 95th centile of the serial interval was between 23 and 41 days, suggesting long infectious periods.

COVID-19 trends, public restrictions policies and vaccination status by economic ranking of countries: a longitudinal study from 110 countries

Background

The coronavirus disease 2019 (COVID-19) pandemic has transitioned to a third phase and many variants have been originated. There has been millions of lives loss as well as billions in economic loss. The morbidity and mortality for COVID-19 varies by country. There were different preventive approaches and public restrictions policies have been applied to control the COVID-19 impacts and usually measured by Stringency Index. This study aimed to explore the COVID-19 trend, public restriction policies and vaccination status with economic ranking of countries.

Methods

We received open access data from Our World in Data. Data from 210 countries were available. Countries (n = 110) data related to testing, which is a key variable in the present study, were included for the analysis and remaining 100 countries were excluded due to incomplete data. The analysis period was set between January 22, 2020 (when COVID-19 was first officially reported) and December 28, 2021. All analyses were stratified by year and the World Bank income group. To analyze the associations among the major variables, we used a longitudinal fixed-effects model.

Results

Out of the 110 countries included in our analysis, there were 9 (8.18%), 25 (22.72%), 31 (28.18%), and 45 (40.90%) countries from low income countries (LIC), low and middle income countries (LMIC), upper middle income countries (UMIC) and high income countries (HIC) respectively. New case per million was similar in LMIC, UMIC and HIC but lower in LIC. The number of new COVID-19 test were reduced in HIC and LMIC but similar in UMIC and LIC. Stringency Index was negligible in LIC and similar in LMIC, UMIC and HIC. New positivity rate increased in LMIC and UMIC. The daily incidence rate was positively correlated with the daily mortality rate in both 2020 and 2021. In 2020, Stringency Index was positive in LIC and HIC but a negative association in LMIC and in 2021 there was a positive association between UMIC and HIC. Vaccination coverage did not appear to change with mortality in 2021.

Conclusion

New COVID-19 cases, tests, vaccinations, positivity rates, and Stringency indices were low in LIC and highest in UMIC. Our findings suggest that the available resources of COVID-19 pandemic would be allocated by need of countries; LIC and UMIC.

A comparative analysis of epidemiological characteristics of MERS-CoV and SARS-CoV-2 in Saudi Arabia

In this study, we determine and compare the incubation duration, serial interval, pre-symptomatic transmission, and case fatality rate of MERS-CoV and COVID-19 in Saudi Arabia based on contact tracing data we acquired in Saudi Arabia. The date of infection and infector-infectee pairings are deduced from travel history to Saudi Arabia or exposure to confirmed cases. The incubation times and serial intervals are estimated using parametric models accounting for exposure interval censoring. Our estimations show that MERS-CoV has a mean incubation time of 7.21 (95% CI: 6.59–7.85) days, whereas COVID-19 (for the circulating strain in the study period) has a mean incubation period of 5.43(95% CI: 4.81–6.11) days. MERS-CoV has an estimated serial interval of 14.13(95% CI: 13.9–14.7) days, while COVID-19 has an estimated serial interval of 5.1(95% CI: 5.0–5.5) days. The COVID-19 serial interval is found to be shorter than the incubation time, indicating that pre-symptomatic transmission may occur in a significant fraction of transmission events. We conclude that during the COVID-19 wave studied, at least 75% of transmission happened prior to the onset of symptoms. The CFR for MERS-CoV is estimated to be 38.1% (95% CI: 36.8–39.5), while the CFR for COVID-19 1.67% (95% CI: 1.63–1.71). This work is expected to help design future surveillance and intervention program targeted at specific respiratory virus outbreaks, and have implications for contingency planning for future coronavirus outbreaks.

Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis

IMPORTANCE

Several studies were conducted to estimate the average incubation period of COVID-19; however, the incubation period of COVID-19 caused by different SARS-CoV-2 variants is not well described.

OBJECTIVE

To systematically assess the incubation period of COVID-19 and the incubation periods of COVID-19 caused by different SARS-CoV-2 variants in published studies.

DATA SOURCES

PubMed, EMBASE, and ScienceDirect were searched between December 1, 2019, and February 10, 2022.

STUDY SELECTION

Original studies of the incubation period of COVID-19, defined as the time from infection to the onset of signs and symptoms.

DATA EXTRACTION AND SYNTHESIS

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline, 3 reviewers independently extracted the data from the eligible studies in March 2022. The parameters, or sufficient information to facilitate calculation of those values, were derived from random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

The mean estimate of the incubation period and different SARS-CoV-2 strains.

RESULTS

A total of 142 studies with 8112 patients were included. The pooled incubation period was 6.57 days (95% CI, 6.26-6.88) and ranged from 1.80 to 18.87 days. The incubation period of COVID-19 caused by the Alpha, Beta, Delta, and Omicron variants were reported in 1 study (with 6374 patients), 1 study (10 patients), 6 studies (2368 patients) and 5 studies (829 patients), respectively. The mean incubation period of COVID-19 was 5.00 days (95% CI, 4.94-5.06 days) for cases caused by the Alpha variant, 4.50 days (95% CI, 1.83-7.17 days) for the Beta variant, 4.41 days (95% CI, 3.76-5.05 days) for the Delta variant, and 3.42 days (95% CI, 2.88-3.96 days) for the Omicron variant. The mean incubation was 7.43 days (95% CI, 5.75-9.11 days) among older patients (ie, aged over 60 years old), 8.82 days (95% CI, 8.19-9.45 days) among infected children (ages 18 years or younger), 6.99 days (95% CI, 6.07-7.92 days) among patients with nonsevere illness, and 6.69 days (95% CI, 4.53-8.85 days) among patients with severe illness.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that SARS-CoV-2 has evolved and mutated continuously throughout the COVID-19 pandemic, producing variants with different enhanced transmission and virulence. Identifying the incubation period of different variants is a key factor in determining the isolation period.

Alveolar macrophages protect mice from MERS-CoV-induced pneumonia and severe disease

Emerging and re-emerging human coronaviruses (hCoVs) cause severe respiratory illness in humans, but the basis for lethal pneumonia in these diseases is not well understood. Alveolar macrophages (AMs) are key orchestrators of host antiviral defense and tissue tolerance during a variety of respiratory infections, and AM dysfunction is associated with severe COVID-19. In this study, using a mouse model of Middle East respiratory syndrome coronavirus (MERS-CoV) infection, we examined the role of AMs in MERS pathogenesis. Our results show that depletion of AMs using clodronate (CL) liposomes significantly increased morbidity and mortality in human dipeptidyl peptidase 4 knock-in (hDPP4-KI) mice. Detailed examination of control and AM-depleted lungs at different days postinfection revealed increased neutrophil activity but a significantly reduced MERS-CoV-specific CD4 T-cell response in AM-deficient lungs during later stages of infection. Furthermore, enhanced MERS severity in AM-depleted mice correlated with lung inflammation and lesions. Collectively, these data demonstrate that AMs are critical for the development of an optimal virus-specific T-cell response and controlling excessive inflammation during MERS-CoV infection.

Distinguishing viruses responsible for influenza-like illness

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.

Epidemiological features of COVID-19 patients with prolonged incubation period and its implications for controlling the epidemics in China

BACKGROUND

COVID-19 patients with long incubation period were reported in clinical practice and tracing of close contacts, but their epidemiological or clinical features remained vague.

METHODS

We analyzed 11,425 COVID-19 cases reported between January-August, 2020 in China. The accelerated failure time model, Logistic and modified Poisson regression models were used to investigate the determinants of prolonged incubation period, as well as their association with clinical severity and transmissibility, respectively.

RESULT

Among local cases, 268 (10.2%) had a prolonged incubation period of > 14 days, which was more frequently seen among elderly patients, those residing in South China, with disease onset after Level I response measures administration, or being exposed in public places. Patients with prolonged incubation period had lower risk of severe illness (OR_adjusted = 0.386, 95% CI: 0.203-0.677). A reduced transmissibility was observed for the primary patients with prolonged incubation period (50.4, 95% CI: 32.3-78.6%) than those with an incubation period of ≤14 days.

CONCLUSIONS

The study provides evidence supporting a prolonged incubation period that exceeded 2 weeks in over 10% for COVID-19. Longer monitoring periods than 14 days for quarantine or persons potentially exposed to SARS-CoV-2 should be justified in extreme cases, especially for those elderly.

Effects of prolonged incubation period and centralized quarantine on the COVID-19 outbreak in Shijiazhuang, China: a modeling study

BACKGROUND

From 2 January to 14 February 2021, a local outbreak of COVID-19 occurred in Shijiazhuang, the capital city of Hebei Province, with a population of 10 million. We analyzed the characteristics of the local outbreak of COVID-19 in Shijiazhuang and evaluated the effects of serial interventions.

METHODS

Publicly available data, which included age, sex, date of diagnosis, and other patient information, were used to analyze the epidemiological characteristics of the COVID-19 outbreak in Shijiazhuang. The maximum likelihood method and Hamiltonian Monte Carlo method were used to estimate the serial interval and incubation period, respectively. The impact of incubation period and different interventions were simulated using a well-fitted SEIR^+q model.

RESULTS

From 2 January to 14 February 2021, there were 869 patients with symptomatic COVID-19 in Shijiazhuang, and most cases (89.6%) were confirmed before 20 January. Overall, 40.2% of the cases were male, 16.3% were aged 0 to 19 years, and 21.9% were initially diagnosed as asymptomatic but then became symptomatic. The estimated incubation period was 11.6 days (95% CI 10.6, 12.7 days) and the estimated serial interval was 6.6 days (0.025^th, 0.975^th: 0.6, 20.0 days). The results of the SEIR^+q model indicated that a longer incubation period led to a longer epidemic period. If the comprehensive quarantine measures were reduced by 10%, then the nucleic acid testing would need to increase by 20% or more to minimize the cumulative number of cases.

CONCLUSIONS

Incubation period was longer than serial interval suggested that more secondary transmission may occur before symptoms onset. The long incubation period made it necessary to extend the isolation period to control the outbreak. Timely contact tracing and implementation of a centralized quarantine quickly contained this epidemic in Shijiazhuang. Large-scale nucleic acid testing also helped to identify cases and reduce virus transmission.

The Incubation Period of COVID-19: Current Understanding and Modeling Technique

This chapter aims to answer the following questions regarding the incubation period of COVID-19. Why is understanding the incubation period of COVID-19 important? How long is the incubation time, and what are the associating factors? How should the incubation period be modeled given the current pandemic situation? Where should we go from here? As a critical epidemiological metric, the incubation period is of public health and clinical importance. While the incubation time of COVID-19 is generally similar to that of SARS and MERS, recent studies identifying factors that impact the incubation period of COVID-19, travel history, for example, only tell part of the story. Therefore, in addition to reviewing current findings, this chapter also explores the modeling technique and future research directions of the incubation period of COVID-19.

Factors Associated With Viral Load Kinetics of Middle East Respiratory Syndrome Coronavirus During the 2015 Outbreak in South Korea

We conducted a retrospective study of Middle East respiratory syndrome coronavirus (MERS-CoV) viral load kinetics using data from patients hospitalized with MERS-CoV infection between 19 May and 20 August 2015. Viral load trajectories were considered over the hospitalization period using 1714 viral load results measured in serial respiratory specimens of 185 patients. The viral load levels were significantly higher among nonsurvivors than among survivors (P = .003). Healthcare workers (P = .001) and nonspreaders (P < .001) had significantly lower viral loads. Viral RNA was present on the day of symptom onset and peaked 4-10 days after symptom onset.

Corona Viruses: A Review on SARS, MERS and COVID-19

After the outbreak of SARS and MERS, the world is now in the grip of another viral disease named COVID-19 caused by a beta Coronavirus - SARS COV-2 which appears to be the only one with a pandemic potential. The case of COVID-19 was reported in the Hubei province of Wuhan city in Central China at the end of December 2019 and it is suspected that the sea food market played a role in this outbreak which was closed abruptly. Subsequently, a Public Health Emergency of International Concern was declared on 30 January 2020 by the World Health Organization. Both SARS and MERS corona viruses had its reservoir in bats and were transferred to humans from palm civets and camels respectively. This virus can be transmitted through airborne droplets. Natural reservoir and intermediate host of COVID-19 is yet to be identified. This paper reviews the occurrences of viral diseases in the recent times including SARS and MERS. As an addition to this, the paper will contain a detailed examination of the COVID-19 Pandemic.

Serial interval and incubation period of COVID-19: a systematic review and meta-analysis

BACKGROUND

Understanding the epidemiological parameters that determine the transmission dynamics of COVID-19 is essential for public health intervention. Globally, a number of studies were conducted to estimate the average serial interval and incubation period of COVID-19. Combining findings of existing studies that estimate the average serial interval and incubation period of COVID-19 significantly improves the quality of evidence. Hence, this study aimed to determine the overall average serial interval and incubation period of COVID-19.

METHODS

We followed the PRISMA checklist to present this study. A comprehensive search strategy was carried out from international electronic databases (Google Scholar, PubMed, Science Direct, Web of Science, CINAHL, and Cochrane Library) by two experienced reviewers (MAA and DBK) authors between the 1st of June and the 31st of July 2020. All observational studies either reporting the serial interval or incubation period in persons diagnosed with COVID-19 were included in this study. Heterogeneity across studies was assessed using the I2 and Higgins test. The NOS adapted for cross-sectional studies was used to evaluate the quality of studies. A random effect Meta-analysis was employed to determine the pooled estimate with 95% (CI). Microsoft Excel was used for data extraction and R software was used for analysis.

RESULTS

We combined a total of 23 studies to estimate the overall mean serial interval of COVID-19. The mean serial interval of COVID-19 ranged from 4. 2 to 7.5 days. Our meta-analysis showed that the weighted pooled mean serial interval of COVID-19 was 5.2 (95%CI: 4.9-5.5) days. Additionally, to pool the mean incubation period of COVID-19, we included 14 articles. The mean incubation period of COVID-19 also ranged from 4.8 to 9 days. Accordingly, the weighted pooled mean incubation period of COVID-19 was 6.5 (95%CI: 5.9-7.1) days.

CONCLUSIONS

This systematic review and meta-analysis showed that the weighted pooled mean serial interval and incubation period of COVID-19 were 5.2, and 6.5 days, respectively. In this study, the average serial interval of COVID-19 is shorter than the average incubation period, which suggests that substantial numbers of COVID-19 cases will be attributed to presymptomatic transmission.

The incubation period of COVID-19: A meta-analysis

Objectives

A valid measurement of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incubation period is needed for case definitions and for adapting appropriate isolation measures but is challenging in an emergency context. Our objective was to systematically review recent literature reporting estimates of the distribution of the incubation period of SARS-CoV-2 and describe the distribution and its variability and dispersion through a meta-analysis.

Methods

A systematic review was carried out on studies published from 1 January 2020 to 10 January 2021 reporting the SARS-CoV-2 incubation period. Individual mean and standard deviation were used to produce the pooled estimate. Sources of heterogeneity were explored by age, gender and study design using a meta-regression.

Results

In total, 99 studies were eligible for analysis in our meta-analysis. The pooled estimate of the mean incubation period across the studies was 6.38 days, 95% CI (5.79; 6.97).

Conclusion

Calculation of the mean incubation period will help with the identification of time of exposure, however, determinants of its variations/range might be explored for potential links with the clinical outcome or pathogenic steps at the early stage of infection. A real-time meta-analysis, named the InCoVid Lyon, is proposed following this initial analysis.

Genetic Variation and Evolution of the 2019 Novel Coronavirus

INTRODUCTION

SARS-CoV-2 is a new type of coronavirus causing a pandemic severe acute respiratory syndrome (SARS-2). Coronaviruses are very diverting genetically and mutate so often periodically. The natural selection of viral mutations may cause host infection selectivity and infectivity.

METHODS

This study was aimed to indicate the diversity between human and animal coronaviruses through finding the rate of mutation in each of the spike, nucleocapsid, envelope, and membrane proteins.

RESULTS

The mutation rate is abundant in all 4 structural proteins. The most number of statistically significant amino acid mutations were found in spike receptor-binding domain (RBD) which may be because it is responsible for a corresponding receptor binding in a broad range of hosts and host selectivity to infect. Among 17 previously known amino acids which are important for binding of spike to angiotensin-converting enzyme 2 (ACE2) receptor, all of them are conservative among human coronaviruses, but only 3 of them significantly are mutated in animal coronaviruses. A single amino acid aspartate-454, that causes dissociation of the RBD of the spike and ACE2, and F486 which gives the strength of binding with ACE2 remain intact in all coronaviruses.

DISCUSSION/CONCLUSION

Observations of this study provided evidence of the genetic diversity and rapid evolution of SARS-CoV-2 as well as other human and animal coronaviruses.

Prediction of the incubation period for COVID-19 and future virus disease outbreaks

Background

A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 pandemic, optimization of quarantine regimes becomes paramount for public health, societal well-being, and global economy. However, biological factors that determine the duration of the virus incubation period remain poorly understood.

Results

We demonstrate a strong positive correlation between the length of the incubation period and disease severity for a wide range of human pathogenic viruses. Using a machine learning approach, we develop a predictive model that accurately estimates, solely from several virus genome features, in particular, the number of protein-coding genes and the GC content, the incubation time ranges for diverse human pathogenic RNA viruses including SARS-CoV-2. The predictive approach described here can directly help in establishing the appropriate quarantine durations and thus facilitate controlling future outbreaks.

Conclusions

The length of the incubation period in viral diseases strongly correlates with disease severity, emphasizing the biological and epidemiological importance of the incubation period. Perhaps, surprisingly, incubation times of pathogenic RNA viruses can be accurately predicted solely from generic features of virus genomes. Elucidation of the biological underpinnings of the connections between these features and disease progression can be expected to reveal key aspects of virus pathogenesis.

Clinical Characteristics, Diagnosis, and Treatment of Major Coronavirus Outbreaks

Human coronavirus infections have been known to cause mild respiratory illness. It changed in the last two decades as three global outbreaks by coronaviruses led to significant mortality and morbidity. SARS CoV-1 led to the first epidemic of the twenty first century due to coronavirus. SARS COV-1 infection had a broad array of symptoms with respiratory and gastrointestinal as most frequent. The last known case was reported in 2004. Middle East respiratory syndrome coronavirus (MERS-CoV) led to the second outbreak in 2012, and case fatality was much higher than SARS. MERS-CoV has a wide array of clinical presentations from mild, moderate to severe, and some patients end up with acute respiratory distress syndrome (ARDS). The third and recent outbreak by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) started in December 2019, which lead to a global pandemic. Patients with SARS-CoV2 infection can be asymptomatic or have a range of symptoms with fever, cough, and shortness of breath being most common. Reverse transcriptase-Polymerase chain reaction (RT-PCR) is a diagnostic test of choice for SARS CoV-1, MERS-CoV, and SARS CoV-2 infections. This review aims to discuss epidemiological, clinical features, diagnosis, and management of human coronaviruses with a focus on SARS CoV-1, MERS-CoV, and SARS CoV-2.

Systematic analysis of infectious disease outcomes by age shows lowest severity in school-age children

The COVID-19 pandemic has ignited interest in age-specific manifestations of infection but surprisingly little is known about relative severity of infectious disease between the extremes of age. In a systematic analysis we identified 142 datasets with information on severity of disease by age for 32 different infectious diseases, 19 viral and 13 bacterial. For almost all infections, school-age children have the least severe disease, and severity starts to rise long before old age. Indeed, for many infections even young adults have more severe disease than children, and dengue was the only infection that was most severe in school-age children. Together with data on vaccine response in children and young adults, the findings suggest peak immune function is reached around 5-14 years of age. Relative immune senescence may begin much earlier than assumed, before accelerating in older age groups. This has major implications for understanding resilience to infection, optimal vaccine scheduling, and appropriate health protection policies across the life course.

Treatment Options for Severe Acute Respiratory Syndrome, Middle East Respiratory Syndrome, and Coronavirus Disease 2019: a Review of Clinical Evidence

Coronaviruses have caused serious Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and Coronavirus Disease 2019 (COVID-19) outbreaks, and only remdesivir has been recently indicated for the treatment of COVID-19. In the line of therapeutic options for SARS and MERS, this study aims to summarize the current clinical evidence of treatment options for COVID-19. In general, the combination of antibiotics, ribavirin, and corticosteroids was considered as a standard treatment for patients with SARS. The addition of this conventional treatment with lopinavir/ritonavir, interferon, and convalescent plasma showed potential clinical improvement. For patients with MERS, ribavirin, lopinavir/ritonavir, interferon, and convalescent plasma were continuously recommended. However, a high-dose of corticosteroid was suggested for severe cases only. The use of lopinavir/ritonavir and convalescent plasma was commonly reported. There was limited evidence for the effect of corticosteroids, other antiviral drugs like ribavirin, and favipiravir. Monoclonal antibody of tocilizumab and antimalarial agents of chloroquine and hydroxychloroquine were also introduced. Among antibiotics for infection therapy, azithromycin was suggested. In conclusion, this study showed the up-to-date evidence of treatment options for COVID-19 that is helpful for the therapy selection and the development of further guidelines and recommendations. Updates of on-going clinical trials and observational studies may confirm the current findings.

Epidemiological characteristics of the first 100 cases of coronavirus disease 2019 (COVID-19) in Hong Kong Special Administrative Region, China, a city with a stringent containment policy

BACKGROUND

Hong Kong (HK) is a densely populated city near the epicentre of the coronavirus disease 2019 (COVID-19) outbreak. Stringent border control together with aggressive case finding, contact tracing, social distancing and quarantine measures were implemented to halt the importation and spread of the virus.

METHODS

We performed an epidemiological study using government information covering the first 100 confirmed cases to examine the epidemic curve, incidence, clusters, reproduction number (Rt), incubation period and time to containment.

RESULTS

A total of 93 of the 100 cases were HK residents (6 infected in Mainland China, 10 on the Diamond Princess Cruise). Seven were visitors infected in Mainland China before entering HK. The majority (76%) were aged ≥45 years, and the incidence increased with age (P < 0.001). Escalation of border control measures correlated with a decrease in the proportion (62.5% to 0%) of cases imported from Mainland China, and a reduction in Rt (1.07 to 0.75). The median incubation period was 4.2 days [95% confidence interval (CI), 4.0-4.5; 5th and 95th percentiles: 1.3 and 14.0). Most clusters with identifiable epidemiological links were households involving 2-4 people. Three medium-spreading events were identified: two from New Year gatherings (6-11 people), and another from environmental contamination of a worship hall (12 people). Despite intensified contact tracing, containment was delayed in 78.9% of cases (mean = 5.96 days, range = 0-24 days). An unusual transmission in a multi-storey building via faulty toilet plumbing was suspected with >100 residents evacuated overnight. Our analysis indicated that faulty plumbing was unlikely to be the source of this transmission.

CONCLUSION

Timely stringent containment policies minimized the importation and transmission of COVID-19 in HK.

Comparison of confirmed COVID-19 with SARS and MERS cases - Clinical characteristics, laboratory findings, radiographic signs and outcomes: A systematic review and meta-analysis

Introduction

Within this large‐scale study, we compared clinical symptoms, laboratory findings, radiographic signs, and outcomes of COVID‐19, SARS, and MERS to find unique features.

Method

We searched all relevant literature published up to February 28, 2020. Depending on the heterogeneity test, we used either random or fixed‐effect models to analyze the appropriateness of the pooled results. Study has been registered in the PROSPERO database (ID 176106).

Result

Overall 114 articles included in this study; 52 251 COVID‐19 confirmed patients (20 studies), 10 037 SARS (51 studies), and 8139 MERS patients (43 studies) were included. The most common symptom was fever; COVID‐19 (85.6%, P < .001), SARS (96%, P < .001), and MERS (74%, P < .001), respectively. Analysis showed that 84% of Covid‐19 patients, 86% of SARS patients, and 74.7% of MERS patients had an abnormal chest X‐ray. The mortality rate in COVID‐19 (5.6%, P < .001) was lower than SARS (13%, P < .001) and MERS (35%, P < .001) between all confirmed patients.

Conclusions

At the time of submission, the mortality rate in COVID‐19 confirmed cases is lower than in SARS‐ and MERS‐infected patients. Clinical outcomes and findings would be biased by reporting only confirmed cases, and this should be considered when interpreting the data.

Certainty of success: three critical parameters in coronavirus vaccine development

Vaccines for 17 viral pathogens have been licensed for use in humans. Previously, two critical biological parameters of the pathogen and the host–pathogen interaction—incubation period and broadly protective, relative immunogenicity—were proposed to account for much of the past successes in vaccine development, and to be useful in estimating the “certainty of success” of developing an effective vaccine for viral pathogens for which a vaccine currently does not exist. In considering the “certainty of success” in development of human coronavirus vaccines, particularly SARS-CoV-2, a third, related critical parameter is proposed—infectious inoculum intensity, at an individual-level, and force of infection, at a population-level. Reducing the infectious inoculum intensity (and force of infection, at a population-level) is predicted to lengthen the incubation period, which in turn is predicted to reduce the severity of illness, and increase the opportunity for an anamnestic response upon exposure to the circulating virus. Similarly, successfully implementing individual- and population-based behaviors that reduce the infectious inoculum intensity and force of infection, respectively, while testing and deploying COVID-19 vaccines is predicted to increase the “certainty of success” of demonstrating vaccine efficacy and controlling SARS-CoV-2 infection, disease, death, and the pandemic itself.

Nomogram for the Individualized Prediction of Survival Among Patients with H7N9 Infection

Background

Until recently, almost all of these studies have identified multiple risk factors but did not offer practical instruments for routine use in predicting individualized survival in human H7N9 infection cases. The objective of this study is to create a practical instrument for use in predicting an individualized survival probability of H7N9 patients.

Methods

A matched case–control study (1:2 ratios) was performed in Zhejiang Province between 2013 and 2019. We reviewed specific factors and outcomes regarding patients with H7N9 virus infection (VI) to determine relationships and developed a nomogram to calculate individualized survival probability. This tool was used to predict each individual patient’s probability of survival based on results obtained from the multivariable Cox proportional hazard regression analysis.

Results

We examined 227 patients with H7N9 VI enrolled in our study. Stepwise selection was applied to the data, which resulted in a final model with 8 independent predictors [including initial PaO₂/FiO₂ ratio ≤300 mmHg, age ≥60 years, chronic diseases, poor hand hygiene, time from illness onset to the first medical visit, incubation period ≤5 days, peak C-reactive protein ≥120 mg/L], and initial bilateral lung infection. The concordance index of this nomogram was 0.802 [95% confidence interval (CI): 0.694–0.901] and 0.793 (95% CI: 0.611–0.952) for the training and validation sets, respectively, which indicates adequate discriminatory power. The calibration curves for the survival showed optimal agreement between nomogram prediction and actual observation in the training and validation sets, respectively.

Conclusion

We established and validated a novel nomogram that can accurately predict the survival probability of patients with H7N9 VI. This nomogram can serve an important role in counseling patients with H7N9 VI and guide treatment decisions.

Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2-14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3-4 days without truncation and at 5-9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

The characteristics of hDPP4 transgenic mice subjected to aerosol MERS coronavirus infection via an animal nose-only exposure device

BACKGROUND

Middle East respiratory syndrome coronavirus (MERS-CoV), which is not fully understood in regard to certain transmission routes and pathogenesis and lacks specific therapeutics and vaccines, poses a global threat to public health.

METHODS

To simulate the clinical aerosol transmission route, hDPP4 transgenic mice were infected with MERS-CoV by an animal nose-only exposure device and compared with instillation-inoculated mice. The challenged mice were observed for 14 consecutive days and necropsied on days 3, 5, 7, and 9 to analyze viral load, histopathology, viral antigen distribution, and cytokines in tissues.

RESULTS

MERS-CoV aerosol-infected mice with an incubation period of 5-7 days showed weight loss on days 7-11, obvious lung lesions on day 7, high viral loads in the lungs on days 3-9 and in the brain on days 7-9, and 60% survival. MERS-CoV instillation-inoculated mice exhibited clinical signs on day 1, obvious lung lesions on days 3-5, continuous weight loss, 0% survival by day 5, and high viral loads in the lungs and brain on days 3-5. Viral antigen and high levels of proinflammatory cytokines and chemokines were detected in the aerosol and instillation groups. Disease, lung lesion, and viral replication progressions were slower in the MERS-CoV aerosol-infected mice than in the MERS-CoV instillation-inoculated mice.

CONCLUSION

hDPP4 transgenic mice were successfully infected with MERS-CoV aerosols via an animal nose-only exposure device, and aerosol- and instillation-infected mice simulated the clinical symptoms of moderate diffuse interstitial pneumonia. However, the transgenic mice exposed to aerosol MERS-CoV developed disease and lung pathology progressions that more closely resembled those observed in humans.

Epidemiology and predictors of survival of MERS-CoV infections in Riyadh region, 2014-2015

Background

MERS-CoV emerged as a zoonotic disease in Saudi Arabia with 1437 cases as of July 2016. This study aimed at describing the epidemiology of MERS-CoV infection, clinical aspects of the disease and the determinants of survival.

Methods

The medical records of Prince Mohamed Bin Abdulaziz Hospital were reviewed between April 2014 and December 2015 to identify admission and discharge with MERS-CoV. Patient’s characteristics, epidemiologic and clinical data and laboratory results were extracted and described. Logistic regression analyses were used to model the determinants of the survival of these patients. Significance of the results were judged at the 5% level.

Results

249 confirmed cases were admitted mostly in August (20.48%) and September (14.86%) of the year 2015. A third (39.36%) reported contact with an index case, developed the disease after 6.2 days and continued to shed the virus for 13.17 days on average. The case fatality rate was 20.08%. Independent predictors of being discharged alive among confirmed cases were younger age (OR_A = 0.953), breathing ambient air (OR_A = 8.981), not being transferred to the ICU (OR_A = 24.240) and not receiving renal replacement therapy (OR_A = 8.342). These variables explain 63.9% of the variability of patients’ status at discharge.

Conclusion

MERS-CoV spread from human-to-human as community acquired and nosocomial infection. The study identified high risk patients in need for special medical attention in order to improve patients’ outcome.

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.

Middle East respiratory syndrome: what we learned from the 2015 outbreak in the Republic of Korea

Middle East Respiratory Syndrome coronavirus (MERS-CoV) was first isolated from a patient with severe pneumonia in 2012. The 2015 Korea outbreak of MERSCoV involved 186 cases, including 38 fatalities. A total of 83% of transmission events were due to five superspreaders, and 44% of the 186 MERS cases were the patients who had been exposed in nosocomial transmission at 16 hospitals. The epidemic lasted for 2 months and the government quarantined 16,993 individuals for 14 days to control the outbreak. This outbreak provides a unique opportunity to fill the gap in our knowledge of MERS-CoV infection. Therefore, in this paper, we review the literature on epidemiology, virology, clinical features, and prevention of MERS-CoV, which were acquired from the 2015 Korea outbreak of MERSCoV.

Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases

During outbreaks of deadly emerging pathogens (e.g., Ebola, MERS-CoV) and bioterror threats (e.g., smallpox), actively monitoring potentially infected individuals aims to limit disease transmission and morbidity. Guidance issued by CDC on active monitoring was a cornerstone of its response to the West Africa Ebola outbreak. There are limited data on how to balance the costs and performance of this important public health activity. We present a framework that estimates the risks and costs of specific durations of active monitoring for pathogens of significant public health concern. We analyze data from New York City’s Ebola active monitoring program over a 16-month period in 2014–2016. For monitored individuals, we identified unique durations of active monitoring that minimize expected costs for those at “low (but not zero) risk” and “some or high risk”: 21 and 31 days, respectively. Extending our analysis to smallpox and MERS-CoV, we found that the optimal length of active monitoring relative to the median incubation period was reduced compared to Ebola due to less variable incubation periods. Active monitoring can save lives but is expensive. Resources can be most effectively allocated by using exposure-risk categories to modify the duration or intensity of active monitoring.

Effects of Timely Control Intervention on the Spread of Middle East Respiratory Syndrome Coronavirus Infection

Objectives

The 2015 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreak in Korea caused major economic and social problems. The control intervention was conducted during the MERS-CoV outbreak in Korea immediately after the confirmation of the index case. This study investigates whether the early risk communication with the general public and mass media is an effective preventive strategy.

Methods

The SEIR (Susceptible, Exposed, Infectious, Recovered) model with estimated parameters for the time series data of the daily MERS-CoV incidence in Korea was considered from May to December 2015. For 10,000 stochastic simulations, the SEIR model was computed using the Gillespie algorithm. Depending on the time of control intervention on the 20th, 40th, and 60th days after the identification of the index case, the box plots of MERS-CoV incidences in Korea were computed, and the results were analyzed via ANOVA.

Results

The box plots showed that there was a significant difference between the non-intervention and intervention groups (the 20th day, 40th day, and 60th day groups) and seemed to show no significant difference based on the time of intervention. However, the ANOVA revealed that early intervention was a good strategy to control the disease.

Conclusion

Appropriate risk communication can secure the confidence of the general public in the public health authorities.

An Opportunistic Pathogen Afforded Ample Opportunities: Middle East Respiratory Syndrome Coronavirus

The human coronaviruses (CoV) include HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU1, some of which have been known for decades. The severe acute respiratory syndrome (SARS) CoV briefly emerged into the human population but was controlled. In 2012, another novel severely human pathogenic CoV—the Middle East Respiratory Syndrome (MERS)-CoV—was identified in the Kingdom of Saudi Arabia; 80% of over 2000 human cases have been recorded over five years. Targeted research remains key to developing control strategies for MERS-CoV, a cause of mild illness in its camel reservoir. A new therapeutic toolbox being developed in response to MERS is also teaching us more about how CoVs cause disease. Travel-related cases continue to challenge the world’s surveillance and response capabilities, and more data are needed to understand unexplained primary transmission. Signs of genetic change have been recorded, but it remains unclear whether there is any impact on clinical disease. How camels came to carry the virus remains academic to the control of MERS. To date, human-to-human transmission has been inefficient, but virus surveillance, characterisation, and reporting are key to responding to any future change. MERS-CoV is not currently a pandemic threat; it is spread mainly with the aid of human habit and error.

Middle East Respiratory Syndrome (MERS)

Since the identification of the first patients with Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, over 1,600 cases have been reported as of February 2016. Most cases have occurred in Saudi Arabia or in other countries on or near the Arabian Peninsula, but travel-associated cases have also been seen in countries outside the Arabian Peninsula. MERS-CoV causes a severe respiratory illness in many patients, with a case fatality rate as high as 40%, although when contacts are investigated, a significant proportion of patients are asymptomatic or only have mild symptoms. At this time, no vaccines or treatments are available. Epidemiological and other data suggest that the source of most primary cases is exposure to camels. Person-to-person transmission occurs in household and health care settings, although sustained and efficient person-to-person transmission has not been observed. Strict adherence to infection control recommendations has been associated with control of previous outbreaks. Vigilance is needed because genomic changes in MERS-CoV could result in increased transmissibility, similar to what was seen in severe acute respiratory syndrome coronavirus (SARS-CoV).

High fatality rates and associated factors in two hospital outbreaks of MERS in Daejeon, the Republic of Korea

OBJECTIVES

To explore the epidemiological and clinical factors predictive of the case fatality rate (CFR) of Middle East respiratory syndrome-coronavirus (MERS-CoV) infection in an outbreak in Daejeon, the Republic of Korea.

METHODS

We reviewed the outbreak investigation reports and medical records of 1 index case and 25 additional MERS cases in hospitals A (14 cases) and B (11 cases), and conducted an in-depth interview with the index case.

RESULTS

The CFR in hospital B was higher than that in hospital A (63.6% vs. 28.6%, respectively). Higher MERS-CoV exposure conditions were also found in hospital B, including aggravated pneumonia in the index case and nebulizer use in a six-bed admission room. The host factors associated with high CFR were pre-existing pneumonia, smoking history, an incubation period of less than 5 days, leukocytosis, abnormal renal function at diagnosis, and respiratory symptoms such as sputum and dyspnea.

CONCLUSIONS

The conditions surrounding MERS-CoV exposure and the underlying poor pulmonary function due to a smoking history or pre-existing pneumonia may explain the high CFR in hospital B. The clinical features described above may enable prediction of the prognosis of MERS cases.

Middle East Respiratory Syndrome: Emergence of a Pathogenic Human Coronavirus

In 2012, a zoonotic coronavirus was identified as the causative agent of Middle East respiratory syndrome and was named MERS coronavirus (MERS-CoV). As of August 11, 2016, the virus has infected 1,791 patients, with a mortality rate of 35.6%. Although MERS-CoV generally causes subclinical or mild disease, infection can result in serious outcomes, including acute respiratory distress syndrome and multi-organ failure in patients with comorbidities. The virus is endemic in camels in the Arabian Peninsula and Africa and thus poses a consistent threat of frequent reintroduction into human populations. Disease prevalence will increase substantially if the virus mutates to increase human-to-human transmissibility. No therapeutics or vaccines are approved for MERS; thus, development of novel therapies is needed. Further, since many MERS cases are acquired in healthcare settings, public health measures and scrupulous attention to infection control are required to prevent additional MERS outbreaks.

Comparison of incubation period distribution of human infections with MERS-CoV in South Korea and Saudi Arabia

The incubation period is an important epidemiologic distribution, it is often incorporated in case definitions, used to determine appropriate quarantine periods, and is an input to mathematical modeling studies. Middle East Respiratory Syndrome coronavirus (MERS) is an emerging infectious disease in the Arabian Peninsula. There was a large outbreak of MERS in South Korea in 2015. We examined the incubation period distribution of MERS coronavirus infection for cases in South Korea and in Saudi Arabia. Using parametric and nonparametric methods, we estimated a mean incubation period of 6.9 days (95% credibility interval: 6.3–7.5) for cases in South Korea and 5.0 days (95% credibility interval: 4.0–6.6) among cases in Saudi Arabia. In a log-linear regression model, the mean incubation period was 1.42 times longer (95% credibility interval: 1.18–1.71) among cases in South Korea compared to Saudi Arabia. The variation that we identified in the incubation period distribution between locations could be associated with differences in ascertainment or reporting of exposure dates and illness onset dates, differences in the source or mode of infection, or environmental differences.

Presentation and outcome of Middle East respiratory syndrome in Saudi intensive care unit patients

BACKGROUND

Middle East respiratory syndrome coronavirus infection is associated with high mortality rates but limited clinical data have been reported. We describe the clinical features and outcomes of patients admitted to an intensive care unit (ICU) with Middle East respiratory syndrome coronavirus (MERS-CoV) infection.

METHODS

Retrospective analysis of data from all adult (>18 years old) patients admitted to our 20-bed mixed ICU with Middle East respiratory syndrome coronavirus infection between October 1, 2012 and May 31, 2014. Diagnosis was confirmed in all patients using real-time reverse transcription polymerase chain reaction on respiratory samples.

RESULTS

During the observation period, 31 patients were admitted with MERS-CoV infection (mean age 59 ± 20 years, 22 [71 %] males). Cough and tachypnea were reported in all patients; 22 (77.4 %) patients had bilateral pulmonary infiltrates. Invasive mechanical ventilation was applied in 27 (87.1 %) and vasopressor therapy in 25 (80.6 %) patients during the intensive care unit stay. Twenty-three (74.2 %) patients died in the ICU. Nonsurvivors were older, had greater APACHE II and SOFA scores on admission, and were more likely to have received invasive mechanical ventilation and vasopressor therapy. After adjustment for the severity of illness and the degree of organ dysfunction, the need for vasopressors was an independent risk factor for death in the ICU (odds ratio = 18.33, 95 % confidence interval: 1.11-302.1, P = 0.04).

CONCLUSIONS

MERS-CoV infection requiring admission to the ICU is associated with high morbidity and mortality. The need for vasopressor therapy is the main risk factor for death in these patients.

Association between the Severity of Influenza A(H7N9) Virus Infections and Length of the Incubation Period

BACKGROUND

In early 2013, a novel avian-origin influenza A(H7N9) virus emerged in China, and has caused sporadic human infections. The incubation period is the delay from infection until onset of symptoms, and varies from person to person. Few previous studies have examined whether the duration of the incubation period correlates with subsequent disease severity.

METHODS AND FINDINGS

We analyzed data of period of exposure on 395 human cases of laboratory-confirmed influenza A(H7N9) virus infection in China in a Bayesian framework using a Weibull distribution. We found a longer incubation period for the 173 fatal cases with a mean of 3.7 days (95% credibility interval, CrI: 3.4-4.1), compared to a mean of 3.3 days (95% CrI: 2.9-3.6) for the 222 non-fatal cases, and the difference in means was marginally significant at 0.47 days (95% CrI: -0.04, 0.99). There was a statistically significant correlation between a longer incubation period and an increased risk of death after adjustment for age, sex, geographical location and underlying medical conditions (adjusted odds ratio 1.70 per day increase in incubation period; 95% credibility interval 1.47-1.97).

CONCLUSIONS

We found a significant association between a longer incubation period and a greater risk of death among human H7N9 cases. The underlying biological mechanisms leading to this association deserve further exploration.