Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). METHODS: Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. RESULTS: The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. CONCLUSION: We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

A prediction model for major adverse cardiovascular events (MACE) in patients with coronavirus disease 2019 (COVID-19).

BACKGROUND: Emerging evidence shows that cardiovascular injuries and events in coronavirus disease 2019 (COVID-19) should be considered. The current study was conducted to develop an early prediction model for major adverse cardiovascular events (MACE) during hospitalizations of COVID-19 patients. METHODS: This was a retrospective, multicenter, observational study. Hospitalized COVID-19 patients from Wuhan city, Hubei Province and Sichuan Province, China, between January 14 and March 9, 2020, were randomly divided into a training set (70% of patients) and a testing set (30%). All baseline data were recorded at admission or within 24 h after admission to hospitals. The primary outcome was MACE during hospitalization, including nonfatal myocardial infarction, nonfatal stroke and cardiovascular death. The risk factors were selected by LASSO regression and multivariate logistic regression analysis. The nomogram was assessed by calibration curve and decision curve analysis (DCA). RESULTS: Ultimately, 1206 adult COVID-19 patients were included. In the training set, 48 (5.7%) patients eventually developed MACE. Six factors associated with MACE were included in the nomogram: age, PaO2/FiO2 under 300, unconsciousness, lymphocyte counts, neutrophil counts and blood urea nitrogen. The C indices were 0.93 (95% CI 0.90, 0.97) in the training set and 0.81 (95% CI 0.70, 0.93) in the testing set. The calibration curve and DCA demonstrated the good performance of the nomogram. CONCLUSIONS: We developed and validated a nomogram to predict the development of MACE in hospitalized COVID-19 patients. More prospective multicenter studies are needed to confirm our results.

DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays

COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest X-ray images have become an important basis for screening and monitoring the disease, and deep learning has shown great potential for this task. Many studies have proposed deep learning methods for automated diagnosis of COVID-19. Although these methods have achieved excellent performance in terms of detection, most have been evaluated using limited datasets and typically use a single deep learning network to extract features. To this end, the dual asymmetric feature learning network (DAFLNet) is proposed, which is divided into two modules, DAFFM and WDFM. DAFFM mainly comprises the backbone networks EfficientNetV2 and DenseNet for feature fusion. WDFM is mainly for weighted decision-level fusion and features a new pretrained network selection algorithm (PNSA) for determination of the optimal weights. Experiments on a large dataset were conducted using two schemes, DAFLNet-1 and DAFLNet-2, and both schemes outperformed eight state-of-the-art classification techniques in terms of classification performance. DAFLNet-1 achieved an average accuracy of up to 98.56% for the triple classification of COVID-19, pneumonia, and healthy images.

Successful Application of Argatroban During VV-ECMO in a Pregnant Patient Complicated With ARDS due to Severe Tuberculosis: A Case Report and Literature Review.

Severe tuberculosis during pregnancy may progress to acute respiratory distress syndrome (ARDS), and venovenous (VV) extracorporeal membrane oxygenation (ECMO) should be considered if conventional lung-protective mechanical ventilation fails. However, thrombocytopenia often occurs with ECMO, and there are limited reports of alternative anticoagulant therapies for pregnant patients with thrombocytopenia during ECMO. This report describes the first case of a pregnant patient who received argatroban during ECMO and recovered. Furthermore, we summarized the existing literature on VV-ECMO and argatroban in pregnant patients. A 31-year-old woman at 17 weeks of gestation was transferred to our hospital with ARDS secondary to severe tuberculosis. We initiated VV-ECMO after implementing a protective ventilation strategy and other conventional therapies. Initially, we selected unfractionated heparin anticoagulant therapy. However, on ECMO day 3, the patient's platelet count and antithrombin III (AT-III) level declined to 27 × 103 cells/µL and 26.9%, respectively. Thus, we started the patient on a 0.06 µg/kg/min argatroban infusion. The argatroban infusion maintenance dose ranged between 0.9 and 1.2 µg/kg/min. The actual activated partial thromboplastin clotting time and activated clotting time ranged from 43 to 58 s and 220-260 s, respectively, without clinically significant bleeding and thrombosis. On day 27, the patient was weaned off VV-ECMO and eventually discharged. VV-ECMO may benefit pregnant women with refractory ARDS, and argatroban may be an alternative anticoagulant for pregnant patients with thrombocytopenia and AT-III deficiency during ECMO.

Clinical Analysis of Kidney Injury in Elderly Patients with COVID-19

Objective: The aim of the study was to analyze the clinical features of elderly patients with coronavirus disease 2019 (COVID-19) and to explore the relationship between COVID-19 patients and kidney injury. Methods: A total of 188 elderly patients with confirmed COVID-19 enrolled in this study were hospitalized for at least 1 week in the Central Theater Command General Hospital of Chinese People's Liberation Army from January 3, 2020 to March 14, 2020. The recorded information included clinical data and results of kidney-related laboratory tests. Retrospective analysis was performed. Results: The median age of the patients was 69 years (interquartile range 65–78, range: 60–97 years);31.4% were 60–74 years old, and 68.6% were over 75 years old. A total of 12.8% and 18.6% of the patients were in critical and severe stages of COVID-19, respectively. The proportions of patients using mechanical ventilators and deaths were 9.5% and 8.5%, respectively. A total of 26.1% and 8.5% of the patients showed mild elevation of blood urea nitrogen (BUN) and serum creatinine (SCr) levels at admission. A total of 18.6% and 5.9% of the patients had elevated BUN and SCr 1 week after admission, respectively. A total of 3.1% of the patients were diagnosed with acute kidney injury, and 75% of those patients had chronic kidney disease before admission. Compared with the patients aged 60–74 years, those over 75 years exhibited significantly increased proportions of elevated BUN levels, critical illness, use of mechanical ventilated, and death. Multivariate logistic regression analysis revealed that an elevated BUN level at admission and 1 week after admission were independent risk factors for death in the elderly patients with COVID-19. Conclusion: There were more critical cases and a high mortality in elderly patients with COVID-19. An increased BUN level was an independent risk factor for death in elderly patients with COVID-19.

Identification of asymptomatic COVID-19 patients on chest CT images using Transformer-or CNN-based deep learning models (preprint)

Backgroud To evaluate the feasibility of deep learning (DL) models in identifying asymptomatic COVID-19 patients, based on chest CT images.Methods In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs)-or Transformer-architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou was randomly split into the training set (n = 2,140) and the internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Models’ performance was assessed by accuracy, recall and specificity and was compared with that of two radiologists.Results A total of 2,700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by EfficientNet model (0.954). The recall and precision of the Swin model were 0.989 and 1.000. In the test dataset, the Swin model still was the best that achieved the highest accuracy (0.980). All the DL models performed remarkable than two experts. Lastly, the time on the test set diagnosis spent by two experts 42min17s (Junior) and 29min43s (Senior), was significantly higher than that of those DL models (all below 2min).Conclusions This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found a Transformer model, the Swin model, performed best.

Identify potential treatments of COVID-19 from Tibetan medicine Hippophae Fructus using a network pharmacological approach

OBJECTIVE Since the coronavirus disease 2019(COVID-19) outbreak in December 2019, the search for a potential treatment for COVID-19 has been a constant focus. Therefore, we identified potential treatments for COVID-19 from Hippophae Fructus, a Tibetan medicine that may act on COVID-19, using a network pharmacology approach.METHODS We collected the chemical constituents and corresponding targets of Hippophae Fructus from traditional Chinese medicine system pharmacology(TCMSP). COVID-19 related genes were predicted in pubmed-Gene, OMIM and GeneCards databases. Then, protein-protein interactions(PPIs) of key genes were analyzed by STRING database.Compound-target-diseases network was constructed using Cytoscape software. The potential pathways were determined by Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analyses. Additionally,molecular docking was used to verify the binding effect between the active component and the target. RESULTS A total of 33 components and 192 corresponding targets in Hippophae Fructus were found. 50 genes were obtained from the intersection of component targets and disease targets. These genes include IL-6, TNF, MAPK8 and PTGS2, which regulate several pathways associated with COVID-19, involving Hepatitis B, Influenza A, TNF signaling pathway and Tuberculosis. More importantly, high-node compounds such as quercetin and beta-sitosterol can well bind to key targets.CONCLUSION Some components in Hippophae Fructus can act on COVID-19 related genes and regulate multiple pathways. Perhaps Hippophae Fructus has the effect in treating COVID-19.

Regional Travel as an Alternative Form of Tourism during the COVID-19 Pandemic: Impacts of a Low-Risk Perception and Perceived Benefits.

Previous COVID-19 tourism research has not considered the positive impact of a low-risk perception and a perception of the benefits of regional travel on taking alternative tourism. This study attempts to fill the research gap and examine the positive effect of these perceptions on tourists' attitudes to regional travel and intentions to undertake regional travel during the COVID-19 pandemic. A survey of 278 respondents confirmed that the perceived benefit positively influences tourists' attitudes and travel intentions, but that a low-risk perception only positively affects their attitudes. This study contributes to tourism risk management research by introducing the concept of a low-risk perception as a positive factor. For tourism recovery, it finds that relaxation, value, and convenience are benefits to drive people to travel.

Blood Urea Nitrogen to Serum Albumin Ratio (BAR) Predicts Critical Illness in Patients with Coronavirus Disease 2019 (COVID-19).

PURPOSE: We sought to explore the prognostic value of blood urea nitrogen (BUN) to serum albumin ratio (BAR) and further develop a prediction model for critical illness in COVID-19 patients. PATIENTS AND METHODS: This was a retrospective, multicenter, observational study on adult hospitalized COVID-19 patients from three provinces in China between January 14 and March 9, 2020. Primary outcome was critical illness, including admission to the intensive care unit (ICU), need for invasive mechanical ventilation (IMV), or death. Clinical data were collected within 24 hours after admission to hospitals. The predictive performance of BAR was tested by multivariate logistic regression analysis and receiver operating characteristic (ROC) curve and then a nomogram was developed. RESULTS: A total of 1370 patients with COVID-19 were included and 113 (8.2%) patients eventually developed critical illness in the study. Baseline age (OR: 1.031, 95% CI: 1.014, 1.049), respiratory rate (OR: 1.063, 95% CI: 1.009, 1.120), unconsciousness (OR: 40.078, 95% CI: 5.992, 268.061), lymphocyte counts (OR: 0.352, 95% CI: 0.204, 0.607), total bilirubin (OR: 1.030, 95% CI: 1.001, 1.060) and BAR (OR: 1.319, 95% CI: 1.183, 1.471) were independent risk factors for critical illness. The predictive AUC of BAR was 0.821 (95% CI: 0.784, 0.858; P<0.01) and the optimal cut-off value of BAR was 3.7887 mg/g (sensitivity: 0.690, specificity: 0.786; positive predictive value: 0.225, negative predictive value: 0.966; positive likelihood ratio: 3.226, negative likelihood ratio: 0.394). The C index of nomogram including above six predictors was 0.9031125 (95% CI: 0.8720542, 0.9341708). CONCLUSION: Elevated BAR at admission is an independent risk factor for critical illness of COVID-19. The novel predictive nomogram including BAR has superior predictive performance.

Efficacy and safety of umifenovir for coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis.

We conducted this systemic review and meta-analysis in an attempt to evaluate the efficacy and safety of umifenovir in coronavirus disease 2019 (COVID-19). We searched PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, and medRxiv database. We included both retrospective and prospective studies. The mean difference (MD) and risk ratio (RR) with 95% confidence intervals (CI) were applied to assess the effectiveness of umifenovir for COVID-19. A total of 12 studies with 1052 patients were included in our final studies. Compared with control group, umifenovir was associated with higher negative rate of PCR on day 14 (RR:1.27; 95% CI: 1.04 to 1.55). However, umifenovir is not related to nucleus acid negative conversion time (MD: 0.09; 95% CI: -1.48 to 1.65), negative rate on day 7 (RR:1.09; 95% CI: 0.91 to 1.31), incidence of composite endpoint (RR:1.20; 95% CI: 0.61 to 2.37), rate of fever alleviation on day 7 (RR:1.00; 95% CI: 0.91 to 1.10), rate of cough alleviation on day 7 (RR:1.00; 95% CI: 0.85 to 1.18), or hospital length of stay (MD: 1.34; 95% CI: -2.08 to 4.76). Additionally, umifenovir was safe in COVID-19 patients (RR for incidence of adverse events: 1.29; 95% CI: 0.57 to 2.92). The results of sensitivity analysis and subgroup analysis were similar to pooled results. There is no evidence to support the use of umifenovir for improving patient-important outcomes in patients with COVID-19.

Diagnostic Value of Hematological and Biochemical Parameters Combinations for Predicting Coronavirus Disease 2019 (COVID-19) in Suspected Patients.

BACKGROUND: The severe epidemiologic situation of COVID-19 due to the limited capacity of healthcare systems makes it necessary to improve the hospital management and early identification and stratification of patients. The aim of the study was to explore hematological and biochemical parameters at admission to the hospital as novel early predictors for diagnosis with coronavirus disease 2019 (COVID-19) among all suspected patients. METHODS: This was a retrospective, multicenter, observational study. The clinical data of all suspected patients were analyzed. The suspected patients with negative RT-PCR results were included as the control group, and compared with confirmed patients. Receiver- operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the hematological indexes. RESULTS: In total, 326 confirmed COVID-19 patients and 116 control patients were included. The predictive ability of combinations of the hematological and biochemical parameters was significantly superior to that of a single parameter. The area under the ROC curve (AUC) of the aspartate aminotransferase (AST) to neutrophil ratio index (ANRI) and the AST to monocyte ratio index (AMRI) were 0.791 and 0.812, respectively. In the multivariate analysis, an ANRI ≥ 6.03(OR: 3.26, 95% CI: 1.02-10.40, P=0.046) and an AMRI ≥ 36.32(OR: 3.64. 95% CI: 1.24-10.68, P=0.02) at admission were independent risk factors related to the occurrence of COVID-19. CONCLUSIONS: We found two novel predictors with promising predictive capacities for COVID-19 among all suspected patients: ANRI and AMRI. Our findings need to be confirmed in further studies.

The Determinants of COVID-19 Morbidity and Mortality across Countries (preprint)

We identify 19 predetermined country-level factors significantly related to weekly COVID-19 morbidity and mortality across 109 countries between January and the end of 2020. Aggravating factors, which increase infections and deaths, include population, the median age of the population, the male-to-female ratio, population density, democracy, political corruption, GDP per capita, income inequality, international tourism activities, and life satisfaction and happiness. Mitigating factors, which reduce morbidity and mortality, include temperature, the education level, religious diversity, media freedom, female leadership, the strength of legal systems, public trust in government, SARS experiences, and healthcare infrastructure. The number of COVID-19 tests is positively (negatively) related to confirmed infection (death) cases. Dominance analysis shows that the top five determinants, which collectively explain approximately 70% of the cross-country variation in morbidity, are population, international tourism activities, media freedom, SARS experiences, and the median age of the population.

Differences in the clinical characteristics and outcomes of COVID-19 patients in the epicenter and peripheral areas of the pandemic from China: a retrospective, large-sample, comparative analysis.

BACKGROUND: There is limited information on the difference in epidemiology, clinical characteristics and outcomes of the initial outbreak of the coronavirus disease (COVID-19) in Wuhan (the epicenter) and Sichuan (the peripheral area) in the early phase of the COVID-19 pandemic. This study was conducted to investigate the differences in the epidemiological and clinical characteristics of patients with COVID-19 between the epicenter and peripheral areas of pandemic and thereby generate information that would be potentially helpful in formulating clinical practice recommendations to tackle the COVID-19 pandemic. METHODS: The Sichuan & Wuhan Collaboration Research Group for COVID-19 established two retrospective cohorts that separately reflect the epicenter and peripheral area during the early pandemic. The epidemiology, clinical characteristics and outcomes of patients in the two groups were compared. Multivariate regression analyses were used to estimate the adjusted odds ratios (aOR) with regard to the outcomes. RESULTS: The Wuhan (epicenter) cohort included 710 randomly selected patients, and the peripheral (Sichuan) cohort included 474 consecutive patients. A higher proportion of patients from the periphery had upper airway symptoms, whereas a lower proportion of patients in the epicenter had lower airway symptoms and comorbidities. Patients in the epicenter had a higher risk of death (aOR=7.64), intensive care unit (ICU) admission (aOR=1.66), delayed time from illness onset to hospital and ICU admission (aOR=6.29 and aOR=8.03, respectively), and prolonged duration of viral shedding (aOR=1.64). CONCLUSIONS: The worse outcomes in the epicenter could be explained by the prolonged time from illness onset to hospital and ICU admission. This could potentially have been associated with elevated systemic inflammation secondary to organ dysfunction and prolonged duration of virus shedding independent of age and comorbidities. Thus, early supportive care could achieve better clinical outcomes.

A novel risk score to predict cardiovascular complications in patients with coronavirus disease 2019 (COVID-19): A retrospective, multicenter, observational study.

BACKGROUND: We conducted this study to explore a novel risk score to predict cardiovascular complications in patients with coronavirus disease 2019 (COVID-19). METHODS: The current study was a retrospective, multicenter, observational study. The clinical data of COVID-19 patients at admission were collected. Patients were randomly divided into training set and testing set (70% vs. 30% of patients). Independent risk factors were identified via logistic regression analysis. RESULTS: Finally, 1207 patients were included. Ten independent risk factors associated with cardiovascular complications were identified in training set: male (odds ratio [OR]: 1.84; 95% confidence interval [CI]: 1.18, 2.85), age ≥ 60 years old (OR: 2.01; 95% CI: 1.3, 3.2), cough (OR: 1.86; 95% CI: 1.16, 3), chronic heart disease (OR: 2.3; 95% CI: 1.19, 4.46), lymphocyte count ≤1.1 × 109 /L at admission (OR: 1.60; 95% CI: 1.03, 2.47), blood urea nitrogen ≥7 mmol/L at admission (OR: 2.14; 95% CI: 1.27, 3.62), estimated glomerular filtration rate ≤90 ml/min/1.73 m2 at admission (OR: 2.08; 95% CI: 1.13, 3.83), activated partial thromboplastin time ≥37 s (OR: 3.07; 95% CI: 1.37, 6.86), D-dimer ≥ 0.5 mg/L (OR: 2.12; 95% CI: 1.33, 3.36) and procalcitonin ≥0.5 µg/L (OR: 3.58; 95% CI: 1.40, 9.14). The area under curve of ROC curve was 0.773 (95% CI: 0.723, 0.822; p < .01). The risk score had robustness and generalizability after validation. Cardiovascular complications were significantly associated with poorer survivals (log-rank test: p < .001). CONCLUSIONS: We developed and validated a novel risk score, which has a promising predictive capacity for cardiovascular complications in COVID-19 patients.

Comparison of acute pneumonia caused by SARS-COV-2 and other respiratory viruses in children: a retrospective multi-center cohort study during COVID-19 outbreak (preprint)

Background: Until July 14, 2020, coronavirus disease-2019 (COVID-19) has infected more than 130 million individuals and has caused a certain degree of panic. Viral pneumonia caused by common viruses such as respiratory syncytial virus, rhinovirus, human metapneumovirus, human bocavirus, and parainfluenza viruses have been more common in children. However, the incidence of COVID-19 in children was significantly lower than that in adults. The purpose of this study was to describe the clinical manifestations, treatment and outcomes of COVID-19 in children compared to those of other sources of viral pneumonia diagnosed during the COVID-19 outbreak. Methods: Children with COVID-19 and viral pneumonia admitted to 20 hospitals were enrolled in this retrospective multi-center cohort study. A total of 64 children with COVID-19 were defined as the COVID-19 cohort, of which 40 children who developed pneumonia were defined as the COVID-19 pneumonia cohort. Another 284 children with pneumonia caused by other viruses were defined as the viral pneumonia cohort. Results: Compared to the viral pneumonia cohort, children in the COVID-19 cohort were mostly exposed to family members confirmed to have COVID-19 (53/64 vs. 23/284), were of older median age (6.3 vs. 3.2 years), and had a higher proportion of ground-glass opacity (GGO) on computed tomography (18/40 vs. 0/38) (all P ＜0.001). Children in the COVID-19 pneumonia cohort had a lower proportion of severe cases (1/40 vs. 38/284, P =0.048), and lower cases with high fever (3/40 vs 167/284, P ＜0.001), requiring intensive care (1/40 vs 32/284, P ＜0.047) and with shorter symptomatic duration (median 5 vs 8 days, P ＜0.001). The proportion of cases with evaluated inflammatory indicators, biochemical indicators related to organ or tissue damage, D-dimer and secondary bacterial infection were lower in the COVID-19 pneumonia cohort than in the viral pneumonia cohort (all P ＜0.05). No statistical differences were found in the duration of positive PCR results from pharyngeal swabs in 25 children with COVID-19 who received antiviral drugs (lopinavir-ritonavir, ribavirin, and arbidol) as compared to duration in 39 children without antiviral therapy [median 10 vs. 9 days, P =0.885]. Conclusion: The symptoms and severity of COVID-19 pneumonia in children were no more severe than those in children with other viral pneumonias. Lopinavir-ritonavir, ribavirin and arbidol do not shorten the duration of positive PCR results from pharyngeal swabs in children with COVID-19. During the COVID-19 outbreak, attention also must be given to children with infection by other pathogens infection.

Prevention and treatment system of novel coronavirus infection in medical and health institutions: experience in West China Hospital of Sichuan University.

The novel coronavirus infection broke out in Wuhan, China, in December 2019, and progressed to a global pandemic. We describe the measures taken by West China Hospital of Sichuan University to address the diagnosis, prevention and treatment of the infection.

Asymptomatic “Exposers” and “Infectors” are Sources of Infection by SARS-CoV-2: Analysis based on Family Clustering (preprint)

Background: Previous studies have documented the clinical characteristics of patients with Coronavirus disease 2019(COVID-19) and presented evidence of person-to-person transmission. Limited data are available for patients with asymptomatic infections. Some asymptomatic carriers, whom we characterize as “exposers” or “infectors”, may be responsible for family clustering of COVID-19.Methods: A questionnaire survey and follow-up survey based on media reports were used to assess familial clustering of SARS-CoV-2 infection induced by asymptomatic exposers/infectors. Individual data were collected for all members of each tracked family. A transmission map was then drawn for each family.Results: Our study of 5 families indicated that individuals with no obvious symptoms of COVID-19, regardless of the PCR results, transmitted the virus to other family members who were community contained at home and had no contact with other infected individuals. There was one death case in Family No.3. Conclusion: Asymptomatic exposers/infectors of SARS-CoV-2 were all middle-aged (average age: 44.4 ± 14.9 years) who had no symptoms but had the ability to disseminate the virus. Medical staff participating in treatment of COVID-19 cases all had a high risk of infection, they should be quarantined so as to protect their families. The morbidity and mortality of Case 3.2 remind us that although these asymptomatic infected people have no symptoms, they are also infectious. It is not ruled out that the subsequent infected people are seriously ill or even die. Therefore, we should not take it lightly.

Children with COVID-19 behaving milder may challenge the public policies: A Systematic Review and Meta-Analysis (preprint)

Background: The emerging virus is rampaging globally. Great efforts are needed to cut down the transmission. Clinical characteristics of infected children have been described previously. No meta-analysis on this subgroup have been published.Methods: A single-arm meta-analysis was conducted. We searched PubMed, Google Scholar, Web of Science, three preprints websites and other Chinese database for studies presenting characteristics of children confirmed with Coronavirus Disease 2019 (COVID-19) from December 1 2019 to March 28 2020. Quality Appraisal of Case Series Studies Checklist was used to assess quality and publication bias was analyzed by Egger’s test. Random-effect model was used to calculate the pooled incidence rate (IR) or mean difference (MD) with 95% confidence intervals (CI), or a fixed model instead when I2<50%. We conducted subgroup analysis according to geographic region. Additionally, we searched United Nations Educational Scientific and Cultural Organization to see how different countries act to the education disruption in COVID-19.Results: 14 studies (two unpublished) with 361 pediatric patients were included. The mean age was 5.5 (95% CI: 0.344–0.765) years old. 23.4% of children were asymptomatic (95%CI: 0.112-0.377). 32.3% (95%CI: 0.163-0.503) showed normal computed tomography imaging, besides, four children were admitted in intensive care units (0, 95%CI: 0.000-0.001) and one death was reported (0, 95%CI: 0.000-0.001). Up to 191 countries have implemented nationwide school closures, affecting over 91% of the world’s students.Conclusion: Children were also susceptible to SARS-CoV-2, while critical cases or death were rare. Characterized by mild presentation, the dilemma that children may become a potential spreader in the pandemic, while strict managements like prolonged school closures, may undermine their well-beings. Thus, the public policies are facing challenge.