Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing,China

Previous studies have showed clinical characteristics of patients with the 2019 novel coronavirus disease(COVID-19) and the evidence of person-to-person transmission. Limited data are available for asymptomatic infections. This study aims to present the clinical characteristics of 24 cases with asymptomatic infection screened from close contacts and to show the transmission potential of asymptomatic COVID-19 virus carriers. Epidemiological investigations were conducted among all close contacts of COVID-19 patients(or suspected patients) in Nanjing, Jiangsu Province, China, from Jan 28 to Feb 9, 2020, both in clinic and in community. Asymptomatic carriers were laboratory-confirmed positive for the COVID-19 virus by testing the nucleic acid of the pharyngeal swab samples. Their clinical records, laboratory assessments, and chest CT scans were reviewed. As a result, none of the 24 asymptomatic cases presented any obvious symptoms while nucleic acid screening. Five cases(20.8%) developed symptoms(fever, cough, fatigue, etc.) during hospitalization. Twelve(50.0%) cases showed typical CT images of ground-glass chest and 5(20.8%) presented stripe shadowing in the lungs. The remaining 7(29.2%) cases showed normal CT image and had no symptoms during hospitalization. These 7 cases were younger(median age: 14.0 years;P=0.012) than the rest. None of the 24 cases developed severe COVID-19 pneumonia or died. The median communicable period, defined as the interval from the first day of positive nucleic acid tests to the first day of continuous negative tests, was 9.5 days(up to 21 days among the 24 asymptomatic cases). Through epidemiological investigation, we observed a typical asymptomatic transmission to the cohabiting family members, which even caused severe COVID-19 pneumonia. Overall, the asymptomatic carriers identified from close contacts were prone to be mildly ill during hospitalization. However, the communicable period could be up to three weeks and the communicated patients could develop severe illness. These results highlighted the importance of close contact tracing and longitudinally surveillance via virus nucleic acid tests. Further isolation recommendation and continuous nucleic acid tests may also be recommended to the patients discharged.     

Linking Time-Varying Symptomatology and Intensity of Infectiousness to Patterns of Norovirus Transmission

Background

Norovirus (NoV) transmission may be impacted by changes in symptom intensity. Sudden onset of vomiting, which may cause an initial period of hyper-infectiousness, often marks the beginning of symptoms. This is often followed by: a 1–3 day period of milder symptoms, environmental contamination following vomiting, and post-symptomatic shedding that may result in transmission at progressively lower rates. Existing models have not included time-varying infectiousness, though representing these features could add utility to models of NoV transmission.

Methods

We address this by comparing the fit of three models (Models 1–3) of NoV infection to household transmission data from a 2009 point-source outbreak of GII.12 norovirus in North Carolina. Model 1 is an SEIR compartmental model, modified to allow Gamma-distributed sojourn times in the latent and infectious classes, where symptomatic cases are uniformly infectious over time. Model 2 assumes infectiousness decays exponentially as a function of time since onset, while Model 3 is discontinuous, with a spike concentrating 50% of transmissibility at onset. We use Bayesian data augmentation techniques to estimate transmission parameters for each model, and compare their goodness of fit using qualitative and quantitative model comparison. We also assess the robustness of our findings to asymptomatic infections.

Results

We find that Model 3 (initial spike in shedding) best explains the household transmission data, using both quantitative and qualitative model comparisons. We also show that these results are robust to the presence of asymptomatic infections.

Conclusions

Explicitly representing explosive NoV infectiousness at onset should be considered when developing models and interventions to interrupt and prevent outbreaks of norovirus in the community. The methods presented here are generally applicable to the transmission of pathogens that exhibit large variation in transmissibility over an infection.     

The outlook for diagnostic purposes of the 2019-novel coronavirus disease

At the end of December 2019, a novel acute respiratory syndrome coronavirus 2 (SARS-CoV2) appeared as the third unheard of outbreak of human coronavirus infection in the 21st century. First, in Wuhan, China, the novel SARS-CoV2 was named by the World Health Organization (WHO), as 2019-nCOV (COVID-19), and spread extremely all over the world. SARS-CoV2 is transmitted to individuals by human-to-human transmission leading to severe viral pneumonia and respiratory system injury. SARS-CoV2 elicits infections from the common cold to severe conditions accompanied by lung injury, acute respiratory distress syndrome, and other organ destruction. There is a possibility of virus transmission from asymptomatic cases as active carriers, in addition to symptomatic ones, which is a crucial crisis of COVID-19 that should be considered. Hence, paying more attention to the accurate and immediate diagnosis of suspected and infected cases can be a great help in preventing the rapid spread of the virus, improving the disease prognosis, and controlling the pandemic. In this review, we provide a comprehensive and up-to-date overview of the different types of Clinical and Para-clinical diagnostic methods and their practical features, which can help understand better the applications and capacities of various diagnostic approaches for COVID-19 infected cases.     

Covasim: An agent-based model of COVID-19 dynamics and interventions

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.     

Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance,virologic testing,and mortality data: Four complementary approaches

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.     

Seroprevalence of COVID-19 in Riyadh city during the early increase of COVID-19 infections in Saudi Arabia,June 2020

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) emerged in December 2019 and caused a global pandemic of the Coronavirus Disease 2019 (COVID-19). More than 170 million cases have been reported worldwide with mortality rate of 1–3%. The detection of SARS-CoV-2 by molecular testing is limited to acute infections, therefore serological studies provide a better estimation of the virus spread in a population. This study aims to evaluate the seroprevalence of SARS-CoV-2 in the major city of Riyadh, Saudi Arabia during the sharp increase of the pandemic, in June 2020. Serum samples from non-COVID patients (n = 432), patients visiting hospitals for other complications and confirmed negative for COVID-19, and healthy blood donors (n = 350) were collected and evaluated using an in-house enzyme-linked immunosorbent assay (ELISA). The overall percentage of positive samples was 7.80% in the combined two populations (n = 782). The seroprevalence was lower in the blood donors (6%) than non-COVID-19 patients (9.25%), p = 0.0004. This seroprevalence rate is higher than the documented cases, indicating asymptomatic or mild unreported COVID-19 infections in these two populations. This warrants further national sero-surveys and highlights the importance of real-time serological surveillance during pandemics.     

Prevalence and associated factors of asymptomatic leishmaniasis: a systematic review and meta-analysis

Asymptomatic leishmaniasis is believed to play important role in maintaining the transmission of Leishmania spp. within endemic communities. Therefore, the efforts to eliminate leishmaniasis are daunting if we cannot manage asymptomatic leishmaniasis well. To clarify the global prevalence and factors associated with the asymptomatic Leishmania infection, we assessed the prevalence of asymptomatic leishmaniasis by a systematic review followed by meta-analyses. In addition, factors associated with the asymptomatic leishmaniasis versus symptomatic were also analyzed. We included all of the original articles alluding to the human asymptomatic leishmaniasis that was confirmed by at least one laboratory diagnosis method regardless of age, sex, race, and ethnicity of the patients, study design, publication date or languages. In total, 111 original articles were chosen for the data extraction. Based on our meta-analyses of the original articles reporting asymptomatic leishmaniasis mostly in endemic areas, the prevalence of asymptomatic leishmaniasis was 11.2% [95% confidence interval (CI) 8.6%‐14.4%] in general population, 36.7% [95% CI 27.6%‐46.8%] in inhabitants living in the same or neighboring household to the symptomatic patients, and 11.8% [95% CI 7.1%-19%] in HIV infected patients. Among individuals with leishmaniasis, 64.9% [95% CI 54.7%-73.9%] were asymptomatic and males were more susceptible to develop symptoms, with OR=1.88, 95% CI 1.19-2.99, P=0.007. Meta-regression analysis showed no significant change in the prevalence of asymptomatic leishmaniasis during the last 40 years.     

Immune response pattern across the asymptomatic,symptomatic and convalescent periods of COVID-19

We present an integrated analysis of urine and serum proteomics and clinical measurements in asymptomatic, mild/moderate, severe and convalescent cases of COVID-19. We identify the pattern of immune response during COVID-19 infection. The immune response is activated in asymptomatic infection, but is dysregulated in mild and severe COVID-19 patients. Our data suggest that the turning point depends on the function of myeloid cells and neutrophils. In addition, immune defects persist into the recovery stage, until 12 months after diagnosis. Moreover, disorders of cholesterol metabolism span the entire progression of the disease, starting from asymptomatic infection and lasting to recovery. Our data suggest that prolonged dysregulation of the immune response and cholesterol metabolism might be the pivotal causative agent of other potential sequelae. Our study provides a comprehensive understanding of COVID-19 immunopathogenesis, which is instructive for the development of early intervention strategies to ameliorate complex disease sequelae.     

Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients

Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational model to quantitatively understand the heterogeneous progression of COVID-19 patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). The model consists of intracellular viral dynamics, multicellular infection process, and immune responses, and was formulated using a combination of differential equations and stochastic modeling. By integrating multi-source clinical data with model analysis, we quantified individual heterogeneity using two indexes, i.e., the ratio of infected cells and incubation period. Specifically, our simulations revealed that increasing the host antiviral state or virus induced type I interferon (IFN) production rate can prolong the incubation period and postpone the transition from asymptomatic to symptomatic outcomes. We further identified the threshold dynamics of T cell exhaustion in the transition between mild-moderate and severe symptoms, and that patients with severe symptoms exhibited a lack of naïve T cells at a late stage. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. Simulations results suggested that single antiviral therapy is sufficient for moderate patients, while combination therapies and prevention of T cell exhaustion are needed for severe patients. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression. Our study reveals a quantitative relationship underpinning the heterogeneity of transition stage during COVID-19 progression and can provide a potential guidance for personalized therapy in COVID-19 patients.     

Association of glycosylated haemoglobin HbA1c levels with outcome in patients with COVID-19: A Retrospective Study

Patients with hyperglycemia tend to be susceptible to Coronavirus disease 2019 (COVID-19). However, the association of HbA1c level with outcome of COVID-19 patients was unclear. We performed a retrospective study of 2880 cases of COVID-19 admitted in Tongji Hospital, Wuhan, China, among which 922 had detected the HbA1c levels. We found that COVID-19 patients with either lower levels of HbAlc (3%-4.9%) or higher levels of HbAlc (≥6%) were associated with elevated all-cause mortality. Meanwhile, we observed that HbAlc levels were highly correlated with haemoglobin (Hb) and total cholesterol (TC) (P < .0001), moderately correlated with albumin (ALB) and high-sensitive C reaction protein (hs-CRP) (0.0001 < P<.001), and relatively low correlated with low-density lipoprotein cholesterol (LDL-C) (.001 < P<.01). These associated cofactors might together contribute to the clinical outcome of COVID-19 patients. Furthermore, the mortality was higher in COVID-19 patients with newly diagnosed diabetes mellitus (DM) compared with COVID-19 patients with history of DM. Moreover, in patients with history of DM, the mortality was decreased in patients treated with anti-hyperglycaemic drugs. In summary, our data showed that the in-hospital mortality was increased in COVID-19 patients with lower or higher levels of HbAlc. Meanwhile, initiation of appropriate anti-hyperglycaemic treatment might improve the clinical outcome in COVID-19 patients.     

Prolonged humoral and cellular immunity in COVID-19-recovered patients

By the beginning of 2021, the battle against coronavirus disease 2019 (COVID-19) remains ongoing. Investigating the adaptive immune response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, in patients who have recovered from this disease could contribute to our understanding of the natural host immune response. We enrolled 38 participants in this study. 7 healthy participants and 31 COVID-19 patients who had recovered from COVID-19 and categorized them into 3 groups according to their previous clinical presentations: 10 moderate, 9 mild, and 12 asymptomatic. Flow cytometry analysis of peripheral lymphocyte counts in recovered patients showed significantly increased levels of CD4⁺ T cells in patients with a history of mild and moderate COVID-19 symptoms compared with those healthy individuals (p < 0.05 and p < 0.0001 respectively). whereas no significant difference was observed in the CD8⁺ T cell percentage in COVID-19-recovered patients compared with healthy individuals. Our study demonstrated that antibodies against the SARS-CoV-2 spike protein (anti-S) IgG antibody production could be observed in all recovered COVID-19 patients, regardless of whether they were asymptomatic (p < 0.05)or presented with mild (p < 0.0001) or moderate symptoms (p < 0.01). Anti-S IgG antibodies could be detected in participants up to 90 days post-infection. In conclusion, the lymphocyte levels in recovered patients were associated with the clinical presentation of the disease, and further analysis is required to investigate relationships between different clinical presentations and lymphocyte activation and function.     

Transmission and containment of the SARS-CoV-2 Delta variant of concern in Guangzhou,China: A population-based study

BackgroundThe first community transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC) in Guangzhou, China occurred between May and June 2021. Herein, we describe the epidemiological characteristics of this outbreak and evaluate the implemented containment measures against this outbreak.Methodology/Principal findingsGuangzhou Center for Disease Control and Prevention provided the data on SARS-CoV-2 infections reported between 21 May and 24 June 2021. We estimated the incubation period distribution by fitting a gamma distribution to the data, while the serial interval distribution was estimated by fitting a normal distribution. The instantaneous effective reproductive number (R_t) was estimated to reflect the transmissibility of SARS-CoV-2. Clinical severity was compared for cases with different vaccination statuses using an ordinal regression model after controlling for age. Of the reported local cases, 7/153 (4.6%) were asymptomatic. The median incubation period was 6.02 (95% confidence interval [CI]: 5.42–6.71) days and the means of serial intervals decreased from 5.19 (95% CI: 4.29–6.11) to 3.78 (95% CI: 2.74–4.81) days. The incubation period increased with age (P<0.001). A hierarchical prevention and control strategy against COVID-19 was implemented in Guangzhou, with R_t decreasing from 6.83 (95% credible interval [CrI]: 3.98–10.44) for the 7-day time window ending on 27 May 2021 to below 1 for the time window ending on 8 June and thereafter. Individuals with partial or full vaccination schedules with BBIBP-CorV or CoronaVac accounted for 15.3% of the COVID-19 cases. Clinical symptoms were milder in partially or fully vaccinated cases than in unvaccinated cases (odds ratio [OR] = 0.26 [95% CI: 0.07–0.94]).Conclusions/SignificanceThe hierarchical prevention and control strategy against COVID-19 in Guangzhou was timely and effective. Authorised inactivated vaccines are likely to contribute to reducing the probability of developing severe disease. Our findings have important implications for the containment of COVID-19.     

Coronavirus Disease (Covid-19) Associated Mucormycosis (CAM): Case Report and Systematic Review of Literature

Severe coronavirus disease (COVID-19) is currently managed with systemic glucocorticoids. Opportunistic fungal infections are of concern in such patients. While COVID-19 associated pulmonary aspergillosis is increasingly recognized, mucormycosis is rare. We describe a case of probable pulmonary mucormycosis in a 55-year-old man with diabetes, end-stage kidney disease, and COVID-19. The index case was diagnosed with pulmonary mucormycosis 21 days following admission for severe COVID-19. He received 5 g of liposomal amphotericin B and was discharged after 54 days from the hospital. We also performed a systematic review of the literature and identified seven additional cases of COVID-19 associated mucormycosis (CAM). Of the eight cases included in our review, diabetes mellitus was the most common risk factor. Three subjects had no risk factor other than glucocorticoids for COVID-19. Mucormycosis usually developed 10–14 days after hospitalization. All except the index case died. In two subjects, CAM was diagnosed postmortem. Mucormycosis is an uncommon but serious infection that complicates the course of severe COVID-19. Subjects with diabetes mellitus and multiple risk factors may be at a higher risk for developing mucormycosis. Concurrent glucocorticoid therapy probably heightens the risk of mucormycosis. A high index of suspicion and aggressive management is required to improve outcomes.

     

Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies

Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.     

COVID-19 in schools: Mitigating classroom clusters in the context of variable transmission

Widespread school closures occurred during the COVID-19 pandemic. Because closures are costly and damaging, many jurisdictions have since reopened schools with control measures in place. Early evidence indicated that schools were low risk and children were unlikely to be very infectious, but it is becoming clear that children and youth can acquire and transmit COVID-19 in school settings and that transmission clusters and outbreaks can be large. We describe the contrasting literature on school transmission, and argue that the apparent discrepancy can be reconciled by heterogeneity, or “overdispersion” in transmission, with many exposures yielding little to no risk of onward transmission, but some unfortunate exposures causing sizeable onward transmission. In addition, respiratory viral loads are as high in children and youth as in adults, pre- and asymptomatic transmission occur, and the possibility of aerosol transmission has been established. We use a stochastic individual-based model to find the implications of these combined observations for cluster sizes and control measures. We consider both individual and environment/activity contributions to the transmission rate, as both are known to contribute to variability in transmission. We find that even small heterogeneities in these contributions result in highly variable transmission cluster sizes in the classroom setting, with clusters ranging from 1 to 20 individuals in a class of 25. None of the mitigation protocols we modeled, initiated by a positive test in a symptomatic individual, are able to prevent large transmission clusters unless the transmission rate is low (in which case large clusters do not occur in any case). Among the measures we modeled, only rapid universal monitoring (for example by regular, onsite, pooled testing) accomplished this prevention. We suggest approaches and the rationale for mitigating these larger clusters, even if they are expected to be rare.     

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.     

A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic

The effective reproduction number R_eff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate R_eff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its R_eff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).     

Assessment of global asymptomatic SARS-CoV-2 infection and management practices from China

With ongoing research, it was found that asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was widespread in coronavirus disease 2019 (COVID-19) populations. Studies have confirmed asymptomatic patients with COVID-19 have potential infectivity, and most of the transmission occurred before symptoms appear. Asymptomatic infection rates varied widely in different countries and regions. Identifying the asymptomatic infected persons and cutting off the infection source is an effective way to prevent the spread of this disease. However, asymptomatic patients have hidden clinical symptoms, and screening based only on the clinical symptoms of COVID-19 can easily lead to a missed diagnosis. Therefore, determining asymptomatic infection patients by SARS-CoV-2 nucleic acid testing is the gold standard. A series of prevention and control measures adopted by the Chinese government, especially the “Four Early” policy, have achieved outstanding achievements, which are worth learning from by other countries.     

Current scenario of COVID-19 in pediatric age group and physiology of immune and thymus response

COVID-19 pandemic caused by SARS-CoV-2, continues to manifest with severe acute respiratory syndrome among the adults, however, it offers a convincing indication of less severity and fatality in pediatric age group (0–18 years). The current trend suggests that children may get infected but are less symptomatic with less fatality, which is concordant to earlier epidemic outbreaks of SARS-CoV and MERS-CoV, in 2002 and 2012, respectively. According to the available data, children appear to be at lower risk for COVID-19, as adults constitute for maximum number of the confirmed cases (308,592) and deaths (13,069) as on 22nd March (https://www.worldometers.info/coronavirus). However, rapid publications and information of the adult patients with COVID-19 is in progress and published, on the contrary, almost no comprehensive data or discussion about the COVID-19 in children is available. Therefore, in this review, we outline the epidemiology, clinical symptoms, diagnosis, treatment, prevention, possible immune response and role of thymus in children to combat the COVID-19 outbreak.     

Osteopontin as a biomarker for COVID-19 severity and multisystem inflammatory syndrome in children: A pilot study

This study sought to evaluate the candidacy of plasma osteopontin (OPN) as a biomarker of COVID-19 severity and multisystem inflammatory condition in children (MIS-C) in children. A retrospective analysis of 26 children (0–21 years of age) admitted to Children’s Healthcare of Atlanta with a diagnosis of COVID-19 between March 17 and May 26, 2020 was undertaken. The patients were classified into three categories based on COVID-19 severity levels: asymptomatic or minimally symptomatic (control population, admitted for other non-COVID-19 conditions), mild/moderate, and severe COVID-19. A fourth category of children met the Centers for Disease Control and Prevention''s case definition for MIS-C. Residual blood samples were analyzed for OPN, a marker of inflammation using commercial ELISA kits (R&D), and results were correlated with clinical data. This study demonstrates that OPN levels are significantly elevated in children hospitalized with moderate and severe COVID-19 and MIS-C compared to OPN levels in mild/asymptomatic children. Further, OPN differentiated among clinical levels of severity in COVID-19, while other inflammatory markers including maximum erythrocyte sedimentation rate, C-reactive protein and ferritin, minimum lymphocyte and platelet counts, soluble interleukin-2R, and interleukin-6 did not. We conclude OPN is a potential biomarker of COVID-19 severity and MIS-C in children that may have future clinical utility. The specificity and positive predictive value of this marker for COVID-19 and MIS-C are areas for future larger prospective research studies.