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

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.

A comparative overview of SARS-CoV-2 and its variants of concern

In December 2019, the severe acute respiratory syndrome 2 (SARS-CoV-2) coronavirus outbreak began in Wuhan, China, and quickly spread to practically every corner of the globe, killing millions of people. SARS-CoV-2 produced numerous variants, five of which have been identified as variants of concern (VOC) by the World Health Organization (WHO) (Alpha, Beta, Gamma, Delta, and Omicron). We conducted a comparative epidemiological analysis of SARS-CoV-2 and its VOC in this paper. We compared the effects of various spike (S) protein mutations in SARS-CoV-2 and its VOC on transmissibility, illness severity, hospitalization risk, fatality rate, immunological evasion, and vaccine efficacy in this review. We also looked into the clinical characteristics of patients infected with SARS-CoV-2 and its VOC.

Modeling Global COVID-19 Dissemination Data After the Emergence of Omicron Variant Using Multipronged Approaches

The COVID-19 pandemic has followed a wave pattern, with an increase in new cases followed by a drop. Several factors influence this pattern, including vaccination efficacy over time, human behavior, infection management measures used, emergence of novel variants of SARS-CoV-2, and the size of the vulnerable population, among others. In this study, we used three statistical approaches to analyze COVID-19 dissemination data collected from 15 November 2021 to 09 January 2022 for the prediction of further spread and to determine the behavior of the pandemic in the top 12 countries by infection incidence at that time, namely Distribution Fitting, Time Series Modeling, and Epidemiological Modeling. We fitted various theoretical distributions to data sets from different countries, yielding the best-fit distribution for the most accurate interpretation and prediction of the disease spread. Several time series models were fitted to the data of the studied countries using the expert modeler to obtain the best fitting models. Finally, we estimated the infection rates (β), recovery rates (γ), and Basic Reproduction Numbers ([Formula: see text]) for the countries using the compartmental model SIR (Susceptible-Infectious-Recovered). Following more research on this, our findings may be validated and interpreted. Therefore, the most refined information may be used to develop the best policies for breaking the disease's chain of transmission by implementing suppressive measures such as vaccination, which will also aid in the prevention of future waves of infection.

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.

Why do some coronaviruses become pandemic threats when others do not?

Despite multiple spillover events and short chains of transmission on at least 4 continents, Middle East Respiratory Syndrome Coronavirus (MERS-CoV) has never triggered a pandemic. By contrast, its relative, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has, despite apparently little, if any, previous circulation in humans. Resolving the unsolved mystery of the failure of MERS-CoV to trigger a pandemic could help inform how we understand the pandemic potential of pathogens, and probing it underscores a need for a more holistic understanding of the ways in which viral genetic changes scale up to population-level transmission.

Inter-individual Variation in Receptor Expression Influences MERS-CoV Infection and Immune Responses in Airway Epithelia

Middle East respiratory syndrome coronavirus (MERS-CoV) causes respiratory infection in humans, with symptom severity that ranges from asymptomatic to severe pneumonia. Known risk factors for severe MERS include male sex, older age, and the presence of various comorbidities. MERS-CoV gains entry into cells by binding its receptor, dipeptidyl peptidase 4 (DPP4), on the surface of airway epithelia. We hypothesized that expression of this receptor might be an additional determinant of outcomes in different individuals during MERS-CoV infection. To learn more about the role of DPP4 in facilitating MERS-CoV infection and spread, we used ELISA and immunofluorescent staining to characterize DPP4 expression in well-differentiated primary human airway epithelia (HAE). We noted wide inter-individual variation in DPP4 abundance, varying by as much as 1000-fold between HAE donors. This variability appears to influence multiple aspects of MERS-CoV infection and pathogenesis, with greater DPP4 abundance correlating with early, robust virus replication and increased cell sloughing. We also observed increased induction of interferon and some interferon-stimulated genes in response to MERS-CoV infection in epithelia with the greatest DPP4 abundance. Overall, our results indicate that inter-individual differences in DPP4 abundance are one host factor contributing to MERS-CoV replication and host defense responses, and highlight how HAE may serve as a useful model for identifying risk factors associated with heightened susceptibility to serious respiratory pathogens.

Recent Advancements on COVID-19: A Comprehensive Review

Over the last few decades, there have been several global outbreaks of severe respiratory infections. The causes of these outbreaks were coronaviruses that had infected birds, mammals and humans. The outbreaks predominantly caused respiratory tract and gastrointestinal tract symptoms and other mild to very severe clinical signs. The current coronavirus disease-2019 (COVID-19) outbreak, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a rapidly spreading illness affecting millions of people worldwide. Among the countries most affected by the disease are the United States of America (USA), India, Brazil, and Russia, with France recording the highest infection, morbidity, and mortality rates. Since early January 2021, thousands of articles have been published on COVID-19. Most of these articles were consistent with the reports on the mode of transmission, spread, duration, and severity of the sickness. Thus, this review comprehensively discusses the most critical aspects of COVID-19, including etiology, epidemiology, pathogenesis, clinical signs, transmission, pathological changes, diagnosis, treatment, prevention and control, and vaccination.

Outdoor PM2.5 concentration and rate of change in COVID-19 infection in provincial capital cities in China

This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = -0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = -0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = -0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.

Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread

In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.

Viral RNA in City Wastewater as a Key Indicator of COVID-19 Recrudescence and Containment Measures Effectiveness

In recent years, and more specifically at the beginning of the COVID-19 crisis, wastewater surveillance has been proposed as a tool to monitor the epidemiology of human viral infections. In the present work, from July to December 2020, the number of copies of SARS-CoV-2 RNA in Marseille's wastewater was correlated with the number of new positive cases diagnosed in our Institute of Infectious Disease, which tested about 20% of the city's population. Number of positive cases and number of copies of SARS-CoV-2 RNA in wastewater were significantly correlated (p = 0.013). During the great epidemic peak, from October to December 2020, the curves of virus in the sewers and the curves of positive diagnoses were perfectly superposed. During the summer period, the superposition of curves was less evident as subject to many confounding factors that were discussed. We also tried to correlate the effect of viral circulation in wastewater with containment measures, probably the most unbiased correlation on their potential inflection effect of epidemic curves. Not only is this correlation not obvious, but it also clearly appears that the drop in cases as well as the drop in the viral load in the sewers occur before the containment measures. In fact, this suggests that there are factors that initiate the end of the epidemic peak independently of the containment measure. These factors will therefore need to be explored more deeply in the future.

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.

Zoonotic coronavirus epidemics: Severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019

OBJECTIVE

To review the virology, immunology, epidemiology, clinical manifestations, and treatment of the following 3 major zoonotic coronavirus epidemics: severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19).

DATA SOURCES

Published literature obtained through PubMed database searches and reports from national and international public health agencies.

STUDY SELECTIONS

Studies relevant to the basic science, epidemiology, clinical characteristics, and treatment of SARS, MERS, and COVID-19, with a focus on patients with asthma, allergy, and primary immunodeficiency.

RESULTS

Although SARS and MERS each caused less than a thousand deaths, COVID-19 has caused a worldwide pandemic with nearly 1 million deaths. Diagnosing COVID-19 relies on nucleic acid amplification tests, and infection has broad clinical manifestations that can affect almost every organ system. Asthma and atopy do not seem to predispose patients to COVID-19 infection, but their effects on COVID-19 clinical outcomes remain mixed and inconclusive. It is recommended that effective therapies, including inhaled corticosteroids and biologic therapy, be continued to maintain disease control. There are no reports of COVID-19 among patients with primary innate and T-cell deficiencies. The presentation of COVID-19 among patients with primary antibody deficiencies is variable, with some experiencing mild clinical courses, whereas others experiencing a fatal disease. The landscape of treatment for COVID-19 is rapidly evolving, with both antivirals and immunomodulators demonstrating efficacy.

CONCLUSION

Further data are needed to better understand the role of asthma, allergy, and primary immunodeficiency on COVID-19 infection and outcomes.

Incubation period for COVID-19: a systematic review and meta-analysis

Aim

This study aims to conduct a review of the existing literature about incubation period for COVID-19, which can provide insights to the transmission dynamics of the disease.

Methods

A systematic review followed by meta-analysis was performed for the studies providing estimates for the incubation period of COVID-19. The heterogeneity and bias in the included studies were tested by various statistical measures, including I² statistic, Cochran's Q test, Begg's test and Egger's test.

Results

Fifteen studies with 16 estimates of the incubation period were selected after implementing the inclusion and exclusion criteria. The pooled estimate of the incubation period is 5.74 (5.18, 6.30) from the random effects model. The heterogeneity in the selected studies was found to be 95.2% from the I² statistic. There is no potential bias in the included studies for meta-analysis.

Conclusion

This review provides sufficient evidence for the incubation period of COVID-19 through various studies, which can be helpful in planning preventive and control measures for the disease. The pooled estimate from the meta-analysis is a valid and reliable estimate of the incubation period for COVID-19.

Prevalence and risk indicators of first-wave COVID-19 among oral health-care workers: A French epidemiological survey

BACKGROUND

Previous studies have highlighted the increased risk of contracting the COVID-19 for health-care workers and suggest that oral health-care workers may carry the greatest risk. Considering the transmission route of the SARS-CoV-2 infection, a similar increased risk can be hypothesized for other respiratory infections. However, no study has specifically assessed the risk of contracting COVID-19 within the dental profession.

METHODS

An online survey was conducted within a population of French dental professionals between April 1 and April 29, 2020. Univariable and multivariable logistic regression analyses were performed to explore risk indicators associated with laboratory-confirmed COVID-19 and COVID-19-related clinical phenotypes (i.e. phenotypes present in 15% or more of SARS-CoV-2-positive cases).

RESULTS

4172 dentists and 1868 dental assistants responded to the survey, representing approximately 10% of French oral health-care workers. The prevalence of laboratory-confirmed COVID-19 was 1.9% for dentists and 0.8% for dental assistants. Higher prevalence was found for COVID-19-related clinical phenotypes both in dentists (15.0%) and dental assistants (11.8%). Chronic kidney disease and obesity were associated with increased odds of laboratory-confirmed COVID-19, whereas working in a practice limited to endodontics was associated with decreased odds. Chronic obstructive pulmonary disease, use of public transportation and having a practice limited to periodontology were associated with increased odds of presenting a COVID-19-related clinical phenotype. Moreover, changes in work rhythm or clinical practice were associated with decreased odds of both outcomes.

CONCLUSIONS

Although oral health-care professionals were surprisingly not at higher risk of COVID-19 than the general population, specific risk indicators could exist, notably among high aerosol-generating dental subspecialties such as periodontology. Considering the similarities between COVID-19-related clinical phenotypes other viral respiratory infections, lessons can be learned from the COVID-19 pandemic regarding the usefulness of equipping and protecting oral health-care workers, notably during seasonal viral outbreaks, to limit infection spread.

IMPACT

Results from this study may provide important insights for relevant health authorities regarding the overall infection status of oral health-care workers in the current pandemic and draw attention to particular at-risk groups, as illustrated in the present study. Protecting oral health-care workers could be an interesting public health strategy to prevent the resurgence of COVID-19 and/or the emergence of new pandemics.

Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.

Human and novel coronavirus infections in children: a review

Coronaviruses, seven of which are known to infect humans, can cause a spectrum of clinical presentations ranging from asymptomatic infection to severe illness and death. Four human coronaviruses (hCoVs)-229E, HKU1, NL63 and OC43-circulate globally, commonly infect children and typically cause mild upper respiratory tract infections. Three novel coronaviruses of zoonotic origin have emerged during the past two decades: severe acute respiratory syndrome coronavirus (SARS-CoV-1), Middle East respiratory syndrome coronavirus (MERS-CoV) and the recently discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. These novel coronaviruses are known to cause severe illness and death predominantly in older adults and those with underlying comorbidities. Consistent with what has been observed during the outbreaks of SARS and MERS, children with COVID-19 are more likely to be asymptomatic or to have mild-to-moderate illness, with few deaths reported in children globally thus far. Clinical symptoms and laboratory and radiological abnormalities in children have been similar to those reported in adults but are generally less severe. A rare multisystem inflammatory syndrome in children (MIS-C) which has resulted in critical illness and some deaths has recently been described. Clinical trials for therapeutics and vaccine development should include paediatric considerations. Children may play an important role in the transmission of infection and outbreak dynamics and could be a key target population for effective measures to control outbreaks. The unintended consequences of the unprecedented scale and duration of pandemic control measures for children and families around the world should be carefully examined.Abbreviations: 2019-nCoV, 2019 novel coronavirus; ADEM, acute demyelinating encephalomyelitis; AAP, American Academy of Pediatrics; ACE-2, angiotensin-converting enzyme 2; ARDS, acute respiratory distress syndrome; BCG, bacillus Calmette-Guérin; BNP, brain natriuretic peptide; CDC, Centers for Disease Control and Prevention; CRP, C-reactive protein; CSF, cerebrospinal fluid; COVID-19, coronavirus disease 2019; CT, computed tomography; CXR, chest X-ray; DOL, day of life; hCoV, human coronavirus; ICU, intensive care unit; IL, interleukin; IVIG, intravenous immunoglobulin; KD, Kawasaki disease; LDH, lactate dehydrogenase; MERS, Middle East respiratory syndrome; MERS-CoV, Middle East respiratory syndrome coronavirus; MEURI, monitored emergency use of unregistered and experimental interventions; MIS-C, multi-system inflammatory syndrome in children; PCR, polymerase chain reaction; PICU, paediatric intensive care unit; RNA, ribonucleic acid; RCT, randomised-controlled trial; RSV, respiratory syncytial virus; SARS, severe acute respiratory syndrome; SARS-CoV-1, severe acute respiratory syndrome coronavirus 1; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; TNF-alpha, tumour necrosis factor alpha; UK United Kingdom; UNICEF, United Nations Children's Fund; USA, United States of America; WHO, World Health Organization.

Incubation period of COVID-19: A systematic review and meta-analysis

BACKGROUND AND OBJECTIVE

The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19.

METHODS

For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables.

RESULTS

We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years.

CONCLUSION

Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

Subcritical Transmission in the Early Stage of COVID-19 in Korea

While the coronavirus disease 2019 (COVID-19) outbreak has been ongoing in Korea since January 2020, there were limited transmissions during the early stages of the outbreak. In the present study, we aimed to provide a statistical characterization of COVID-19 transmissions that led to this small outbreak. We collated the individual data of the first 28 confirmed cases reported from 20 January to 10 February 2020. We estimated key epidemiological parameters such as reporting delay (i.e., time from symptom onset to confirmation), incubation period, and serial interval by fitting probability distributions to the data based on the maximum likelihood estimation. We also estimated the basic reproduction number (R0) using the renewal equation, which allows for the transmissibility to differ between imported and locally transmitted cases. There were 16 imported and 12 locally transmitted cases, and secondary transmissions per case were higher for the imported cases than the locally transmitted cases (nine vs. three cases). The mean reporting delays were estimated to be 6.76 days (95% CI: 4.53, 9.28) and 2.57 days (95% CI: 1.57, 4.23) for imported and locally transmitted cases, respectively. The mean incubation period was estimated to be 5.53 days (95% CI: 3.98, 8.09) and was shorter than the mean serial interval of 6.45 days (95% CI: 4.32, 9.65). The R0 was estimated to be 0.40 (95% CI: 0.16, 0.99), accounting for the local and imported cases. The fewer secondary cases and shorter reporting delays for the locally transmitted cases suggest that contact tracing of imported cases was effective at reducing further transmissions, which helped to keep R0 below one and the overall transmissions small.

Incubation period of COVID-19: A systematic review and meta-analysis

Background and objective

The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19.

Methods

For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables.

Results

We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I² 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I² 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2–6.0) to 6.7 days (95% CI: 6.0–7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years.

Conclusion

Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

Does incubation period of COVID-19 vary with age? A study of epidemiologically linked cases in Singapore

This study estimates the incubation period of COVID-19 among locally transmitted cases, and its association with age to better inform public health measures in containing COVID-19. Epidemiological data of all PCR-confirmed COVID-19 cases from all restructured hospitals in Singapore were collected between 23 January 2020 and 2 April 2020. Activity mapping and detailed epidemiological investigation were conducted by trained personnel. Positive cases without clear exposure to another positive case were excluded from the analysis. One hundred and sixty-four cases (15.6% of patients) met the inclusion criteria during the defined period. The crude median incubation period was 5 days (range 1-12 days) and median age was 42 years (range 5-79 years). The median incubation period among those 70 years and older was significantly longer than those younger than 70 years (8 vis-à-vis 5 days, P = 0.040). Incubation period was negatively correlated with day of illness in both groups. These findings support current policies of 14-day quarantine periods for close contacts of confirmed cases and 28 days for monitoring infections in known clusters. An elderly person who may have a longer incubation period than a younger counterpart may benefit from earlier and proactive testing, especially after exposure to a positive case.

Innate Immune Responses to Highly Pathogenic Coronaviruses and Other Significant Respiratory Viral Infections

The new pandemic virus SARS-CoV-2 emerged in China and spread around the world in <3 months, infecting millions of people, and causing countries to shut down public life and businesses. Nearly all nations were unprepared for this pandemic with healthcare systems stretched to their limits due to the lack of an effective vaccine and treatment. Infection with SARS-CoV-2 can lead to Coronavirus disease 2019 (COVID-19). COVID-19 is respiratory disease that can result in a cytokine storm with stark differences in morbidity and mortality between younger and older patient populations. Details regarding mechanisms of viral entry via the respiratory system and immune system correlates of protection or pathogenesis have not been fully elucidated. Here, we provide an overview of the innate immune responses in the lung to the coronaviruses MERS-CoV, SARS-CoV, and SARS-CoV-2. This review provides insight into key innate immune mechanisms that will aid in the development of therapeutics and preventive vaccines for SARS-CoV-2 infection.

Estimation of incubation period distribution of COVID-19 using disease onset forward time: A novel cross-sectional and forward follow-up study

We have proposed a novel, accurate low-cost method to estimate the incubation-period distribution of COVID-19 by conducting a cross-sectional and forward follow-up study. We identified those presymptomatic individuals at their time of departure from Wuhan and followed them until the development of symptoms. The renewal process was adopted by considering the incubation period as a renewal and the duration between departure and symptoms onset as a forward time. Such a method enhances the accuracy of estimation by reducing recall bias and using the readily available data. The estimated median incubation period was 7.76 days [95% confidence interval (CI): 7.02 to 8.53], and the 90th percentile was 14.28 days (95% CI: 13.64 to 14.90). By including the possibility that a small portion of patients may contract the disease on their way out of Wuhan, the estimated probability that the incubation period is longer than 14 days was between 5 and 10%.

Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example

During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.

Estimation of incubation period and serial interval of COVID-19: analysis of 178 cases and 131 transmission chains in Hubei province, China

A novel coronavirus disease, designated as COVID-19, has become a pandemic worldwide. This study aims to estimate the incubation period and serial interval of COVID-19. We collected contact tracing data in a municipality in Hubei province during a full outbreak period. The date of infection and infector–infectee pairs were inferred from the history of travel in Wuhan or exposed to confirmed cases. The incubation periods and serial intervals were estimated using parametric accelerated failure time models, accounting for interval censoring of the exposures. Our estimated median incubation period of COVID-19 is 5.4 days (bootstrapped 95% confidence interval (CI) 4.8–6.0), and the 2.5th and 97.5th percentiles are 1 and 15 days, respectively; while the estimated serial interval of COVID-19 falls within the range of −4 to 13 days with 95% confidence and has a median of 4.6 days (95% CI 3.7–5.5). Ninety-five per cent of symptomatic cases showed symptoms by 13.7 days (95% CI 12.5–14.9). The incubation periods and serial intervals were not significantly different between male and female, and among age groups. Our results suggest a considerable proportion of secondary transmission occurred prior to symptom onset. And the current practice of 14-day quarantine period in many regions is reasonable.

The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

BACKGROUND

A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities.

OBJECTIVE

To estimate the length of the incubation period of COVID-19 and describe its public health implications.

DESIGN

Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.

SETTING

News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.

PARTICIPANTS

Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.

MEASUREMENTS

Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.

RESULTS

There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.

LIMITATION

Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.

CONCLUSION

This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.

PRIMARY FUNDING SOURCE

U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

BACKGROUND

A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities.

OBJECTIVE

To estimate the length of the incubation period of COVID-19 and describe its public health implications.

DESIGN

Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.

SETTING

News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.

PARTICIPANTS

Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.

MEASUREMENTS

Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.

RESULTS

There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.

LIMITATION

Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.

CONCLUSION

This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.

PRIMARY FUNDING SOURCE

U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

BACKGROUND

A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities.

OBJECTIVE

To estimate the length of the incubation period of COVID-19 and describe its public health implications.

DESIGN

Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.

SETTING

News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.

PARTICIPANTS

Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.

MEASUREMENTS

Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.

RESULTS

There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.

LIMITATION

Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.

CONCLUSION

This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.

PRIMARY FUNDING SOURCE

U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

Coronavirus Infections in Children Including COVID-19: An Overview of the Epidemiology, Clinical Features, Diagnosis, Treatment and Prevention Options in Children

Coronaviruses (CoVs) are a large family of enveloped, single-stranded, zoonotic RNA viruses. Four CoVs commonly circulate among humans: HCoV2-229E, -HKU1, -NL63 and -OC43. However, CoVs can rapidly mutate and recombine leading to novel CoVs that can spread from animals to humans. The novel CoVs severe acute respiratory syndrome coronavirus (SARS-CoV) emerged in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012. The 2019 novel coronavirus (SARS-CoV-2) is currently causing a severe outbreak of disease (termed COVID-19) in China and multiple other countries, threatening to cause a global pandemic. In humans, CoVs mostly cause respiratory and gastrointestinal symptoms. Clinical manifestations range from a common cold to more severe disease such as bronchitis, pneumonia, severe acute respiratory distress syndrome, multi-organ failure and even death. SARS-CoV, MERS-CoV and SARS-CoV-2 seem to less commonly affect children and to cause fewer symptoms and less severe disease in this age group compared with adults, and are associated with much lower case-fatality rates. Preliminary evidence suggests children are just as likely as adults to become infected with SARS-CoV-2 but are less likely to be symptomatic or develop severe symptoms. However, the importance of children in transmitting the virus remains uncertain. Children more often have gastrointestinal symptoms compared with adults. Most children with SARS-CoV present with fever, but this is not the case for the other novel CoVs. Many children affected by MERS-CoV are asymptomatic. The majority of children infected by novel CoVs have a documented household contact, often showing symptoms before them. In contrast, adults more often have a nosocomial exposure. In this review, we summarize epidemiologic, clinical and diagnostic findings, as well as treatment and prevention options for common circulating and novel CoVs infections in humans with a focus on infections in children.

Coronavirus Infections in Children Including COVID-19: An Overview of the Epidemiology, Clinical Features, Diagnosis, Treatment and Prevention Options in Children

Coronaviruses (CoVs) are a large family of enveloped, single-stranded, zoonotic RNA viruses. Four CoVs commonly circulate among humans: HCoV2-229E, -HKU1, -NL63 and -OC43. However, CoVs can rapidly mutate and recombine leading to novel CoVs that can spread from animals to humans. The novel CoVs severe acute respiratory syndrome coronavirus (SARS-CoV) emerged in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012. The 2019 novel coronavirus (SARS-CoV-2) is currently causing a severe outbreak of disease (termed COVID-19) in China and multiple other countries, threatening to cause a global pandemic. In humans, CoVs mostly cause respiratory and gastrointestinal symptoms. Clinical manifestations range from a common cold to more severe disease such as bronchitis, pneumonia, severe acute respiratory distress syndrome, multi-organ failure and even death. SARS-CoV, MERS-CoV and SARS-CoV-2 seem to less commonly affect children and to cause fewer symptoms and less severe disease in this age group compared with adults, and are associated with much lower case-fatality rates. Preliminary evidence suggests children are just as likely as adults to become infected with SARS-CoV-2 but are less likely to be symptomatic or develop severe symptoms. However, the importance of children in transmitting the virus remains uncertain. Children more often have gastrointestinal symptoms compared with adults. Most children with SARS-CoV present with fever, but this is not the case for the other novel CoVs. Many children affected by MERS-CoV are asymptomatic. The majority of children infected by novel CoVs have a documented household contact, often showing symptoms before them. In contrast, adults more often have a nosocomial exposure. In this review, we summarize epidemiologic, clinical and diagnostic findings, as well as treatment and prevention options for common circulating and novel CoVs infections in humans with a focus on infections in children.

On a Statistical Transmission Model in Analysis of the Early Phase of COVID-19 Outbreak

Since December 2019, a disease caused by a novel strain of coronavirus (COVID-19) had infected many people and the cumulative confirmed cases have reached almost 180,000 as of 17, March 2020. The COVID-19 outbreak was believed to have emerged from a seafood market in Wuhan, a metropolis city of more than 11 million population in Hubei province, China. We introduced a statistical disease transmission model using case symptom onset data to estimate the transmissibility of the early-phase outbreak in China, and provided sensitivity analyses with various assumptions of disease natural history of the COVID-19. We fitted the transmission model to several publicly available sources of the outbreak data until 11, February 2020, and estimated lock down intervention efficacy of Wuhan city. The estimated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}R0 was between 2.7 and 4.2 from plausible distribution assumptions of the incubation period and relative infectivity over the infectious period. 95% confidence interval of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}R0 were also reported. Potential issues such as data quality concerns and comparison of different modelling approaches were discussed.

Estimation of incubation period distribution of COVID-19 using disease onset forward time: a novel cross-sectional and forward follow-up study

BACKGROUND

The current outbreak of coronavirus disease 2019 (COVID-19) has quickly spread across countries and become a global crisis. However, one of the most important clinical characteristics in epidemiology, the distribution of the incubation period, remains unclear. Different estimates of the incubation period of COVID-19 were reported in recent published studies, but all have their own limitations. In this study, we propose a novel low-cost and accurate method to estimate the incubation distribution.

METHODS

We have conducted a cross-sectional and forward follow-up study by identifying those asymptomatic individuals at their time of departure from Wuhan and then following them until their symptoms developed. The renewal process is hence adopted by considering the incubation period as a renewal and the duration between departure and symptom onset as a forward recurrence time. Under mild assumptions, the observations of selected forward times can be used to consistently estimate the parameters in the distribution of the incubation period. Such a method enhances the accuracy of estimation by reducing recall bias and utilizing the abundant and readily available forward time data.

FINDINGS

The estimated distribution of forward time fits the observations in the collected data well. The estimated median of incubation period is 8·13 days (95% confidence interval [CI]: 7·37-8·91), the mean is 8·62 days (95% CI: 8·02-9·28), the 90th percentile is 14·65 days (95% CI: 14·00-15·26), and the 99th percentile is 20·59 days (95% CI: 19·47, 21·62). Compared with results in other studies, the incubation period estimated in this study is longer.

INTERPRETATION

Based on the estimated incubation distribution in this study, about 10% of patients with COVID-19 would not develop symptoms until 14 days after infection. Further study of the incubation distribution is warranted to directly estimate the proportion with long incubation periods.

Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks

Coronaviruses (CoVs) are RNA viruses that have become a major public health concern since the Severe Acute Respiratory Syndrome-CoV (SARS-CoV) outbreak in 2002. The continuous evolution of coronaviruses was further highlighted with the emergence of the Middle East Respiratory Syndrome-CoV (MERS-CoV) outbreak in 2012. Currently, the world is concerned about the 2019 novel CoV (SARS-CoV-2) that was initially identified in the city of Wuhan, China in December 2019. Patients presented with severe viral pneumonia and respiratory illness. The number of cases has been mounting since then. As of late February 2020, tens of thousands of cases and several thousand deaths have been reported in China alone, in addition to thousands of cases in other countries. Although the fatality rate of SARS-CoV-2 is currently lower than SARS-CoV, the virus seems to be highly contagious based on the number of infected cases to date. In this review, we discuss structure, genome organization, entry of CoVs into target cells, and provide insights into past and present outbreaks. The future of human CoV outbreaks will not only depend on how the viruses will evolve, but will also depend on how we develop efficient prevention and treatment strategies to deal with this continuous threat.

Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study

BACKGROUND

Since Dec 31, 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV). Cases have been exported to other Chinese cities, as well as internationally, threatening to trigger a global outbreak. Here, we provide an estimate of the size of the epidemic in Wuhan on the basis of the number of cases exported from Wuhan to cities outside mainland China and forecast the extent of the domestic and global public health risks of epidemics, accounting for social and non-pharmaceutical prevention interventions.

METHODS

We used data from Dec 31, 2019, to Jan 28, 2020, on the number of cases exported from Wuhan internationally (known days of symptom onset from Dec 25, 2019, to Jan 19, 2020) to infer the number of infections in Wuhan from Dec 1, 2019, to Jan 25, 2020. Cases exported domestically were then estimated. We forecasted the national and global spread of 2019-nCoV, accounting for the effect of the metropolitan-wide quarantine of Wuhan and surrounding cities, which began Jan 23-24, 2020. We used data on monthly flight bookings from the Official Aviation Guide and data on human mobility across more than 300 prefecture-level cities in mainland China from the Tencent database. Data on confirmed cases were obtained from the reports published by the Chinese Center for Disease Control and Prevention. Serial interval estimates were based on previous studies of severe acute respiratory syndrome coronavirus (SARS-CoV). A susceptible-exposed-infectious-recovered metapopulation model was used to simulate the epidemics across all major cities in China. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credibile interval (CrI).

FINDINGS

In our baseline scenario, we estimated that the basic reproductive number for 2019-nCoV was 2·68 (95% CrI 2·47-2·86) and that 75 815 individuals (95% CrI 37 304-130 330) have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6·4 days (95% CrI 5·8-7·1). We estimated that in the baseline scenario, Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461 (95% CrI 227-805), 113 (57-193), 98 (49-168), 111 (56-191), and 80 (40-139) infections from Wuhan, respectively. If the transmissibility of 2019-nCoV were similar everywhere domestically and over time, we inferred that epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan outbreak of about 1-2 weeks.

INTERPRETATION

Given that 2019-nCoV is no longer contained within Wuhan, other major Chinese cities are probably sustaining localised outbreaks. Large cities overseas with close transport links to China could also become outbreak epicentres, unless substantial public health interventions at both the population and personal levels are implemented immediately. Independent self-sustaining outbreaks in major cities globally could become inevitable because of substantial exportation of presymptomatic cases and in the absence of large-scale public health interventions. Preparedness plans and mitigation interventions should be readied for quick deployment globally.

FUNDING

Health and Medical Research Fund (Hong Kong, China).

Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020

A novel coronavirus (2019-nCoV) is causing an outbreak of viral pneumonia that started in Wuhan, China. Using the travel history and symptom onset of 88 confirmed cases that were detected outside Wuhan in the early outbreak phase, we estimate the mean incubation period to be 6.4 days (95% credible interval: 5.6-7.7), ranging from 2.1 to 11.1 days (2.5th to 97.5th percentile). These values should help inform 2019-nCoV case definitions and appropriate quarantine durations.

The Middle East Respiratory Syndrome Coronavirus: An Emerging Virus of Global Threat

Middle East respiratory syndrome (MERS) is a viral respiratory illness caused by a coronavirus (CoV), first identified in Saudi Arabia in 2012. Since then, almost 2000 cases have been reported from 27 countries, with Saudi Arabia being the epicenter. This newly emerging virus is highly pathogenic and has a case mortality rate of 35%. It is similar to the CoV causing severe acute respiratory syndrome CoV (SARS-CoV) in that both belong to the genus beta CoVs that are of zoonotic origin and cause lower respiratory infection. The natural reservoir for MERS-CoV remains unknown. Serological studies indicate that most dromedary camels in the Middle East have been infected with this virus, and they maybe the potential intermediate host. However, the mode of transmission from camels to humans is poorly understood. The majority of confirmed human cases have resulted from human-to-human transmission, most probably via respiratory route. Patients most at risk of developing severe MERS-CoV infection appear to be those with underlying conditions such as diabetes, hypertension, obesity, cardiac diseases, chronic respiratory diseases, and cancer. Unlike SARS-CoV, MERS-CoV is considered an ongoing public health problem, particularly for the Middle East region. In this chapter, we outline the prevailing information regarding the emergence and epidemiology of this virus, its mode of transmission and pathogenicity, its clinical features, and the potential strategies for prevention.

COVID-19: data-driven dynamics, statistical and distributed delay models, and observations

COVID-19 was declared as a pandemic by the World Health Organization on March 11, 2020. Here, the dynamics of this epidemic is studied by using a generalized logistic function model and extended compartmental models with and without delays. For a chosen population, it is shown as to how forecasting may be done on the spreading of the infection by using a generalized logistic function model, which can be interpreted as a basic compartmental model. In an extended compartmental model, which is a modified form of the SEIQR model, the population is divided into susceptible, exposed, infectious, quarantined, and removed (recovered or dead) compartments, and a set of delay integral equations is used to describe the system dynamics. Time-varying infection rates are allowed in the model to capture the responses to control measures taken, and distributed delay distributions are used to capture variability in individual responses to an infection. The constructed extended compartmental model is a nonlinear dynamical system with distributed delays and time-varying parameters. The critical role of data is elucidated, and it is discussed as to how the compartmental model can be used to capture responses to various measures including quarantining. Data for different parts of the world are considered, and comparisons are also made in terms of the reproductive number. The obtained results can be useful for furthering the understanding of disease dynamics as well as for planning purposes.

A Comparative Analysis of Factors Influencing Two Outbreaks of Middle Eastern Respiratory Syndrome (MERS) in Saudi Arabia and South Korea

In 2012, an emerging viral infection was identified in Saudi Arabia that subsequently spread to 27 additional countries globally, though cases may have occurred elsewhere. The virus was ultimately named Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV), and has been endemic in Saudi Arabia since 2012. As of September 2019, 2468 laboratory-confirmed cases with 851 associated deaths have occurred with a case fatality rate of 34.4%, according to the World Health Organization. An imported case of MERS occurred in South Korea in 2015, stimulating a multi-month outbreak. Several distinguishing factors emerge upon epidemiological and sociological analysis of the two outbreaks including public awareness of the MERS outbreak, and transmission and synchronization of governing healthcare bodies. South Korea implemented a stringent healthcare model that protected patients and healthcare workers alike through prevention and high levels of public information. In addition, many details about MERS-CoV virology, transmission, pathological progression, and even the reservoir, remain unknown. This paper aims to delineate the key differences between the two regional outbreaks from both a healthcare and personal perspective including differing hospital practices, information and public knowledge, cultural practices, and reservoirs, among others. Further details about differing emergency outbreak responses, public information, and guidelines put in place to protect hospitals and citizens could improve the outcome of future MERS outbreaks.

MERS-CoV as an emerging respiratory illness: A review of prevention methods

INTRODUCTION

Middle East Respiratory Coronavirus Virus (MERS-CoV) first emerged from Saudi Arabia in 2012 and has since been recognized as a significant human respiratory pathogen on a global level.

METHODS

In this narrative review, we focus on the prevention of MERS-CoV. We searched PubMed, Embase, Cochrane, Scopus, and Google Scholar, using the following terms: 'MERS', 'MERS-CoV', 'Middle East respiratory syndrome' in combination with 'prevention' or 'infection control'. We also reviewed the references of each article to further include other studies or reports not identified by the search.

RESULTS

As of Nov 2019, a total of 2468 laboratory-confirmed cases of MERS-CoV were diagnosed mostly from Middle Eastern regions with a mortality rate of at least 35%. A major outbreak that occurred outside the Middle East (in South Korea) and infections reported from 27 countries. MERS-CoV has gained recognition as a pathogen of global significance. Prevention of MERS-CoV infection is a global public health priority. Healthcare facility transmission and by extension community transmission, the main amplifier of persistent outbreaks, can be prevented through early identification and isolation of infected humans. While MERS-CoV vaccine studies were initially hindered by multiple challenges, recent vaccine development for MERS-CoV is showing promise.

CONCLUSIONS

The main factors leading to sustainability of MERS-CoV infection in high risk courtiers is healthcare facility transmission. MERS-CoV transmission in healthcare facility mainly results from laps in infection control measures and late isolation of suspected cases. Preventive measures for MERS-CoV include disease control in camels, prevention of camel to human transmission.

A Smartphone Magnetometer-Based Diagnostic Test for Automatic Contact Tracing in Infectious Disease Epidemics

Smartphone magnetometer readings exhibit high linear correlation when two phones coexist within a short distance. Thus, the detected coexistence can serve as a proxy for close human contact events, and one can conceive using it as a possible automatic tool to modernize the contact tracing in infectious disease epidemics. This paper investigates how good a diagnostic test it would be, by evaluating the discriminative and predictive power of the smartphone magnetometer-based contact detection in multiple measures. Based on the sensitivity, specificity, likelihood ratios, and diagnostic odds ratios, we find that the decision made by the smartphone magnetometer-based test can be accurate in telling contacts from no contacts. Furthermore, through the evaluation process, we determine the appropriate range of compared trace segment sizes and the correlation cutoff values that we should use in such diagnostic tests.

High-resolution epidemic simulation using within-host infection and contact data

BACKGROUND

Recent epidemics have entailed global discussions on revamping epidemic control and prevention approaches. A general consensus is that all sources of data should be embraced to improve epidemic preparedness. As a disease transmission is inherently governed by individual-level responses, pathogen dynamics within infected hosts posit high potentials to inform population-level phenomena. We propose a multiscale approach showing that individual dynamics were able to reproduce population-level observations.

METHODS

Using experimental data, we formulated mathematical models of pathogen infection dynamics from which we simulated mechanistically its transmission parameters. The models were then embedded in our implementation of an age-specific contact network that allows to express individual differences relevant to the transmission processes. This approach is illustrated with an example of Ebola virus (EBOV).

RESULTS

The results showed that a within-host infection model can reproduce EBOV's transmission parameters obtained from population data. At the same time, population age-structure, contact distribution and patterns can be expressed using network generating algorithm. This framework opens a vast opportunity to investigate individual roles of factors involved in the epidemic processes. Estimating EBOV's reproduction number revealed a heterogeneous pattern among age-groups, prompting cautions on estimates unadjusted for contact pattern. Assessments of mass vaccination strategies showed that vaccination conducted in a time window from five months before to one week after the start of an epidemic appeared to strongly reduce epidemic size. Noticeably, compared to a non-intervention scenario, a low critical vaccination coverage of 33% cannot ensure epidemic extinction but could reduce the number of cases by ten to hundred times as well as lessen the case-fatality rate.

CONCLUSIONS

Experimental data on the within-host infection have been able to capture upfront key transmission parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. The population of interest in epidemic assessments could be modelled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios to explore multilevel aspects in infectious diseases epidemics are underway.

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.

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.