Pay attention to SARS-CoV-2 infection in children

ConvCoroNet: a deep convolutional neural network optimized with iterative thresholding algorithm for Covid-19 detection using chest X-ray images

Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly the infected patients. During the peak of this epidemic, there was an insufficiency of Covid-19 test kits. In addition, this technique takes a considerable time in the diagnosis. Hence the need to find fast, accurate and low-cost method to replace or supplement RT PCR-based methods. Covid-19 is a respiratory disease, chest X-ray images are often used to diagnose pneumonia. From this perspective, these images can play an important role in the Covid-19 detection. In this article, we propose ConvCoroNet, a deep convolutional neural network model optimized with new method based on iterative thresholding algorithm to detect coronavirus automatically from chest X-ray images. ConvCoroNet is trained on a dataset prepared by collecting chest X-ray images of Covid-19, pneumonia and normal cases from publically datasets. The experimental results of our proposed model show a high accuracy of 99.50%, sensitivity of 98.80% and specificity of 99.85% when detecting Covid-19 from chest X-ray images. ConvCoroNet achieves promising results in the automatic detection of Covid-19 from chest X-ray images. It may be able to help radiologists in the Covid-19 detection by reducing the examination time of X-ray images.Communicated by Ramaswamy H. Sarma.

Characteristics of Respiratory Syncytial Virus Infection in Hospitalized Children Before and During the COVID-19 Pandemic in Thailand

OBJECTIVES

This study compared the epidemiological and clinical manifestations of patients hospitalized with respiratory syncytial virus (RSV) infection before and during the coronavirus disease 2019 (COVID-19) pandemic at a tertiary care hospital in Chiang Mai Province, Thailand.

METHODS

This retrospective observational study utilized data from all cases of laboratory-confirmed RSV infection at Maharaj Nakorn Chiang Mai Hospital from January 2016 to December 2021. Differences in the clinical presentation of RSV infection before (2016 to 2019) and during (2020 to 2021) the COVID-19 pandemic were analyzed and compared.

RESULTS

In total, 358 patients hospitalized with RSV infections were reported from January 2016 to December 2021. During the COVID-19 pandemic, only 74 cases of hospitalized RSV infection were reported. Compared to pre-pandemic levels, the clinical presentations of RSV infection showed statistically significant decreases in fever on admission (p=0.004), productive cough (p=0.004), sputum (p=0.003), nausea (p=0.03), cyanosis (p=0.004), pallor (p&lt;0.001), diarrhea (p&lt;0.001), and chest pain (p<0.001). Furthermore, vigilant measures to prevent the spread of COVID-19, including lockdowns, also interrupted the RSV season in Thailand from 2020 to 2021.

CONCLUSIONS

The incidence of RSV infection was affected by the COVID-19 pandemic in Chiang Mai Province, Thailand, which also changed the clinical presentation and seasonal pattern of RSV infection in children.

A deep learning approach for classification of COVID and pneumonia using DenseNet-201

In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.

Parents' Decisions to Vaccinate Children against COVID-19: A Scoping Review

Since 2019, the COVID-19 pandemic has resulted in sickness, hospitalizations, and deaths of the old and young and impacted global social and economy activities. Vaccination is one of the most important and efficient ways to protect against the COVID-19 virus. In a review of the literature on parents’ decisions to vaccinate their children, we found that widespread vaccination was hampered by vaccine hesitancy, especially for children who play an important role in the coronavirus transmission in both family and school. To analyze parent vaccination decision-making for children, our review of the literature on parent attitudes to vaccinating children, identified the objective and subjective influencing factors in their vaccination decision. We found that the median rate of parents vaccinating their children against COVID-19 was 59.3% (IQR 48.60~73.90%). The factors influencing parents’ attitudes towards child vaccination were heterogeneous, reflecting country-specific factors, but also displaying some similar trends across countries, such as the education level of parents. The leading reason in the child vaccination decision was to protect children, family and others; and the fear of side effects and safety was the most important reason in not vaccinating children. Our study informs government and health officials about appropriate vaccination policies and measures to improve the vaccination rate of children and makes specific recommendations on enhancing child vaccinate rates.

CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification

The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score.

A Novel Weighted Consensus Machine Learning Model for COVID-19 Infection Classification Using CT Scan Images

As COVID-19 has spread rapidly, detection of the COVID-19 infection from radiology and radiography images is probably one of the quickest ways to diagnose the patients. Many researchers found the necessity to utilize chest X-ray and chest computed tomography imaging to diagnose COVID-19 infection. In this paper, our objective is to minimize the false negatives and false positives in the detection process. Reduction in the number of false negatives minimizes community spread of the COVID-19 pandemic. Reducing false positives help people avoid mental trauma and wasteful expenses. This paper proposes a novel weighted consensus model to minimize the number of false negatives and false positives without compromising accuracy. In the proposed novel weighted consensus model, the accuracy of individual classification models is normalized. While predicting, different models predict different classes, and the sum of the normalized accuracy for a particular class is then considered based on a predefined threshold value. We used traditional Machine Learning classification algorithms like Linear Regression, Support Vector Machine, k-Nearest Neighbours, Decision Tree, and Random Forest for the weighted consensus experimental evaluation. We predicted the classes, which provided better insights into the condition. The proposed model can perform as well as the existing state-of-the-art technique in terms of accuracy (99.64%) and reduce false negatives and false positives.

A Meta-analysis on the Role of Children in Severe Acute Respiratory Syndrome Coronavirus 2 in Household Transmission Clusters

The role of children in the spread of SARS-CoV-2 remains highly controversial. To address this issue, we performed a meta-analysis of the published literature on household SARS-CoV-2 transmission clusters (n=213 from 12 countries). Only 8 (3.8%) transmission clusters were identified as having a paediatric index case. Asymptomatic index cases were associated with a lower secondary attack in contacts than symptomatic index cases (estimate risk ratio [RR], 0.17; 95% confidence interval [CI], 0.09-0.29). To determine the susceptibility of children to household infections the secondary attack rate (SAR) in paediatric household contacts was assessed. The secondary attack rate in paediatric household contacts was lower than in adult household contacts (RR, 0.62; 95% CI, 0.42-0.91). These data have important implications for the ongoing management of the COVID-19 pandemic, including potential vaccine prioritization strategies.

Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction

Novel corona-virus (nCOV) has been declared as a pandemic that started from the city Wuhan of China. This deadly virus is infecting people rapidly and has targeted 4.93 million people across the world, with 227 K people being infected only in Italy. Cases of nCOV are quickly increasing whereas the number of nCOV test kits available in hospitals are limited. Under these conditions, an automated system for the classification of patients into nCOV positive and negative cases, is a much needed tool against the pandemic, helping in a selective use of the limited number of test kits. In this research, Convolutional Neural Network-based models (one block VGG, two block VGG, three block VGG, four block VGG, LetNet-5, AlexNet, and Resnet-50) have been employed for the detection of Corona-virus and SARS_MERS infected patients, distinguishing them from the healthy subjects, using lung X-ray scans, which has proven to be a challenging task, due to overlapping characteristics of different corona virus types. Furthermore, LSTM model has been used for time series forecasting of nCOV cases, in the following 10 days, in Italy. The evaluation results obtained, proved that the VGG1 model distinguishes the three classes at an accuracy of almost 91%, as compared to other models, whereas the approach based on the LSTM predicts the number of nCOV cases with 99% accuracy.

Why COVID-19 is less frequent and severe in children: a narrative review

BACKGROUND

Despite the streaks of severity, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection is, in general, less frequent and severe in children than in adults. We searched for causal evidence of this mystery.

DATA SOURCES

An extensive search strategy was designed to identify papers on coronavirus disease 2019 (COVID-19). We searched Ovid MEDLINE, PubMed, EMBASE databases, and Cochrane library and carried out a review on the causes of this dilemma.

RESULTS

Our searches produced 81 relevant articles. The review showed that children accounted for a lower percentage of reported cases, and they also experienced less severe illness courses. Some potential explanations, including the tendency to engage the upper airway, the different expression in both receptors of angiotensin-converting enzyme and renin-angiotensin system, a less vigorous immune response, the lower levels of interleukin (IL)-6, IL-10, myeloperoxidase, and P-selectin and a higher intracellular adhesion molecule-1, a potential protective role of lymphocytes, and also lung infiltrations might have protective roles in the immune system-respiratory tract interactions. Finally, what have shed light on this under representation comes from two studies that revealed high-titer immunoglobulin-G antibodies against respiratory syncytial virus and mycoplasma pneumonia, may carry out cross-protection against SARS-CoV-2 infection, just like what suggested about the vaccines.

CONCLUSIONS

These results require an in-depth look. Properties of the immune system including a less vigorous adaptive system beside a preliminary potent innate response and a trained immunity alongside a healthier respiratory system, and their interactions, might protect children against SARS-CoV-2 infection. However, further studies are needed to explore other possible causes of this enigma.

Age-Specific Differences in the Severity of COVID-19 Between Children and Adults: Reality and Reasons

In severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, children experience mild symptoms compared to adults. However, the precise explanations for this disparity are not clear. Thus, we attempted to identify rational explanations about age-related differences as reported in different studies. Given the incomplete data on SARS-CoV-2, some information has been gathered from other studies of earlier coronavirus or influenza outbreaks. Age-related differences in disease severity are important with regard to diagnosis, prognosis, and treatment of SARS-CoV-2 infections. In addition, these differences impact social distancing needs, since pediatric patients with mild or asymptomatic are likely to play a significant role in disease transmission.

Seroprevalence of SARS-CoV-2 infections among children visiting a hospital

IMPORTANCE

In this study, we retrospectively investigated the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies within serum samples from children in Beijing, China. These findings provide preliminary guidance regarding population susceptibility to SARS-CoV-2, which will aid in establishing policy toward coronavirus disease 2019 (COVID-19) prevention and control.

OBJECTIVE

To understand the seropositivity of anti-SARS-CoV-2 IgM/IgG antibodies among children in Beijing, China, evaluate the susceptibility of children in Beijing to SARS-CoV-2, and provide prima facie evidence to guide SARS-CoV-2 prevention and control.

METHODS

IgM/IgG antibody kits (colloidal gold) were used to conduct preliminary screening of SARS-CoV-2 IgM/IgG antibodies in serum samples of children who presented to Beijing Children's Hospital, Capital Medical University, having fever or requiring hospitalization, from March 2020 to August 2020. Statistical analysis of anti-SARS-CoV-2 antibody seropositivity was performed according to the children's general demographic characteristics, timing of admission to hospital, presence of pneumonia, and viral nucleic acid test results.

RESULTS

The study included 19 797 children with both IgM and IgG antibody results. Twenty-four children had anti-SARS-CoV-2 IgM-positive results (positive rate of 1.2‰), twelve children had anti-SARS-CoV-2 IgG-positive results (positive rate of 0.6‰). Viral nucleic acid test results were negative for the above-mentioned children with positive antibody findings; during the study, two children exhibited positive viral nucleic acid test results, but their anti-SARS-CoV-2 IgM/IgG antibody results were negative. Anti-SARS-CoV-2 IgM antibody seropositivity was higher in the <1-year-old group than in the ≥6-year-old group. The rates of anti-SARS-CoV-2 IgM seropositivity was highest in August from March to August; IgG results did not significantly differ over time. The rates of anti-SARS-CoV-2 IgM or IgG seropositivity among children with and without suspected pneumonia did not significantly differ between groups.

INTERPRETATION

During the study period, the rates of anti-SARS-CoV-2 IgM/IgG antibody seropositivity were low among children who presented to Beijing Children's Hospital, Capital Medical University. The findings suggest that children in Beijing are generally susceptible to SARS-CoV-2 infection; COVID-19 prevention and control measures should be strengthened to prevent disease in children.

Cross-sectional study on the role of public awareness in preventing the spread of COVID-19 outbreak in India

BACKGROUND

WHO has recommended personal hygiene (respiratory hygiene, using face masks, washing hands with warm water and soap, use of alcohol-based hand sanitizers, avoid touching mouth, eyes & nose, cleanliness), social distancing and careful handling of purchased products as an effective preventive measure for COVID-19 disease. The growing pandemic of COVID-19 disease requires social distancing and personal hygiene measures to protect public health. But this message is not clear and well understood among people. The aim of this study is to determine the awareness, knowledge and attitude about COVID-19 and relate the behaviour of Indian society, especially when the country is restarting all its economic activities, after the complete lockdown.

METHOD

The present paper is based on an extensive survey among 21 406 adult participants of various sections of Indian society with different age groups between 18 and 80 years to introspect the level of public awareness with respect to cause, spread, prevention and treatment of disease caused by spread of COVID-19 viral outbreak, which will be automatically reflected in the societal behavioural response of rigorous precautionary measures.

CONCLUSIONS

There is a need to extend the knowledge base among individuals to enhance their active participation in the prevention mechanisms with respect to the spread of the pandemic. There is a need to elaborate the Indian socio-cultural aspects, so that society starts appreciating and voluntarily following social distancing. This should improve the adaptability of people with livelihood resilience to let them protect themselves not only from the present pandemic but also from all other unforeseen infections, and to provide care to patients.

Novel Coronavirus Infection in Febrile Infants Aged 60 Days and Younger

In this case series, we describe the clinical course and outcomes of 7 febrile infants aged ≤60 days with confirmed severe acute respiratory syndrome coronavirus 2 infection. No infant had severe outcomes, including the need for mechanical ventilation or ICU level of care. Two infants had concurrent urinary tract infections, which were treated with antibiotics. Although a small sample, our data suggest that febrile infants with severe acute respiratory syndrome coronavirus 2 infection often have mild illness.

SARS-CoV-2: Repurposed Drugs and Novel Therapeutic Approaches-Insights into Chemical Structure-Biological Activity and Toxicological Screening

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic represents the primary public health concern nowadays, and great efforts are made worldwide for efficient management of this crisis. Considerable scientific progress was recorded regarding SARS-CoV-2 infection in terms of genomic structure, diagnostic tools, viral transmission, mechanism of viral infection, symptomatology, clinical impact, and complications, but these data evolve constantly. Up to date, neither an effective vaccine nor SARS-CoV-2 specific antiviral agents have been approved, but significant advances were enlisted in this direction by investigating repurposed approved drugs (ongoing clinical trials) or developing innovative antiviral drugs (preclinical and clinical studies). This review presents a thorough analysis of repurposed drug admitted for compassionate use from a chemical structure-biological activity perspective highlighting the ADME (absorption, distribution, metabolism, and excretion) properties and the toxicophore groups linked to potential adverse effects. A detailed pharmacological description of the novel potential anti-COVID-19 therapeutics was also included. In addition, a comprehensible overview of SARS-CoV-2 infection in terms of general description and structure, mechanism of viral infection, and clinical impact was portrayed.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

Lessons from SARS-CoV-2 Pandemic: Evolution, Disease Dynamics and Future

The COVID-19 pandemic is rising at an unprecedented rate. The surging number of deaths every day, global lockdown and travel restrictions have resulted in huge losses to society. The impact is massive and will leave a historical footprint. The Spanish Flu of 1918, which was the last pandemic that had a similar impact, was shadowed under the consequences of World War I. All the brilliance, strength and economies of countries worldwide are aimed at fighting the COVID-19 pandemic. The knowledge about coronavirus dynamics, its nature and epidemiology are expanding every day. The present review aims to summarize the structure, epidemiology, symptoms, statistical status of the disease status, intervention strategies and deliberates the lessons learnt during the pandemic. The intervention approaches, antiviral drug repurposing and vaccine trials are intensified now. Statistical interpretations of disease dynamics and their projections may help the decision-makers.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.