Five reasons COVID-19 is less severe in younger age-groups

Technical efficiency and its determinants in regulating adolescents' coronavirus infection across Asian countries

The coronavirus pandemic, besides generating health distress, influences the socio-economic conditions of humankind. Every adolescent's lifestyle is affected by the virus. Healthy adolescents are not only key contributors to the forthcoming workforce but also a source of a country's human capital. The purpose of the article is to examine the efficacy of various Asian countries in regulating the spread of the coronavirus among adolescents. In addition to that, the paper also attempts to pinpoint the prime causes of the inefficiency of a country in regulating the same. The paper also examines the behavioural changes among adolescents across Asian countries in pre-and-post pandemic times. In this context, the study identifies the impact of adolescents' tobacco consumption, female political leadership, and accreditation on a country's efficacy to regulate adolescents' coronavirus infection. The study's empirical analysis covers twenty-one Asian countries. By using the Panel Stochastic Production Frontier, the study concludes that Kazakhstan is the most efficient country and Afghanistan is the least efficient country on the list. The inefficiency effects estimates conclude that adolescents' tobacco consumption decreases and good governance practices increase the efficiency of a country in regulating the spread of adolescent coronavirus infection. Additionally, the paper finds no significant behavioural changes among adolescents in pre-and-post pandemic times across Asian countries. The paper concludes with appropriate policy recommendations supported by empirical evidence. The paper also identifies its shortcomings and suggests potential future lines of inquiry.

Vulnerability Predictors of Post-Vaccine SARS-CoV-2 Infection and Disease-Empirical Evidence from a Large Population-Based Italian Platform

We aimed to identify individual features associated with increased risk of post-vaccine SARS-CoV-2 infection and severe COVID-19 illness. We performed a nested case–control study based on 5,350,295 citizens from Lombardy, Italy, aged ≥ 12 years who received a complete anti-COVID-19 vaccination from 17 January 2021 to 31 July 2021, and followed from 14 days after vaccine completion to 11 November 2021. Overall, 17,996 infections and 3023 severe illness cases occurred. For each case, controls were 1:1 (infection cases) or 1:10 (severe illness cases) matched for municipality of residence and date of vaccination completion. The association between selected predictors (sex, age, previous occurrence of SARS-CoV-2 infection, type of vaccine received, number of previous contacts with the Regional Health Service (RHS), and the presence of 59 diseases) and outcomes was assessed by using multivariable conditional logistic regression models. Sex, age, previous SARS-CoV-2 infection, type of vaccine and number of contacts with the RHS were associated with the risk of infection and severe illness. Moreover, higher odds of infection and severe illness were significantly associated with 14 and 34 diseases, respectively, among those investigated. These results can be helpful to clinicians and policy makers for prioritizing interventions.

Potential Application of SARS-CoV-2 Rapid Antigen Diagnostic Tests for the Detection of Infectious Individuals Attending Mass Gatherings - A Simulation Study

Rapid Antigen Diagnostic Tests (RADTs) for the detection of SARS-CoV-2 offer advantages in that they are cheaper and faster than currently used PCR tests but have reduced sensitivity and specificity. One potential application of RADTs is to facilitate gatherings of individuals, through testing of attendees at the point of, or immediately prior to entry at a venue. Understanding the baseline risk in the tested population is of particular importance when evaluating the utility of applying diagnostic tests for screening purposes. We used incidence data from January and from July-August 2021, periods of relatively high and low levels of infection, to estimate the prevalence of infectious individuals in the community at particular time points and simulated mass gatherings by sampling from a series of age cohorts. Nine different illustrative scenarios were simulated, small (n = 100), medium (n = 1,000) and large (n = 10,000) gatherings each with 3 possible age constructs: mostly younger, mostly older or a gathering with equal numbers from each age cohort. For each scenario, we estimated the prevalence of infectious attendees, then simulated the likely number of positive and negative test results, the proportion of cases detected and the corresponding positive and negative predictive values, and the cost per case identified. Our findings suggest that for each reported case on a given day, there are likely to be 13.8 additional infectious individuals also present in the community. Prevalence ranged from 0.26% for "mostly older" events in July-August, to 2.6% for "mostly younger" events in January. For small events (100 attendees) the expected number of infectious attendees ranged from <1 across all age constructs of attendees in July-August, to 2.6 for "mostly younger" events in January. For large events (10,000 attendees) the expected number of infectious attendees ranged from 27 (95% confidence intervals 12 to 45) for mostly older events in July-August, to 267 (95% confidence intervals 134 to 436) infectious attendees for mostly younger attendees in January. Given rapid changes in SARS-CoV-2 incidence over time, we developed an RShiny app to allow users to run updated simulations for specific events.

Classification of COVID-19 and Influenza Patients Using Deep Learning

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019–22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.

Evaluation of Three Multiplex Real-time Reverse Transcription PCR Assays for Simultaneous Detection of SARS-CoV-2, Influenza A/B, and Respiratory Syncytial Virus in Nasopharyngeal Swabs

BACKGROUND

In the coronavirus disease 2019 (COVID-19) pandemic era, the simultaneous detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza virus (Flu), and respiratory syncytial virus (RSV) is important in the rapid differential diagnosis in patients with respiratory symptoms. Three multiplex real-time reverse transcription polymerase chain reaction (rRT-PCR) assays have been recently developed commercially in Korea: PowerChek™ SARS-CoV-2, Influenza A&B Multiplex Real-time PCR Kit (PowerChek; KogeneBiotech); STANDARD™ M Flu/SARS-CoV-2 Real-time Detection Kit (STANDARD M; SD BioSensor); and Allplex™ SARS-CoV-2/FluA/FluB/RSV Assay (Allplex; Seegene). We evaluated the analytical and clinical performances of these kits.

METHODS

A limit of detection tests were performed and cross-reactivity analysis was executed using clinical respiratory samples. Ninety-seven SARS-CoV-2-positive, 201 SARS-CoV-2-negative, 71 influenza A-positive, 50 influenza B-positive, 78 RSV-positive, and 207 other respiratory virus-positive nasopharyngeal swabs were tested using the three assays. The AdvanSure™ respiratory viruses rRT-PCR assay (AdvanSure; LG Life Sciences) was used as a comparator assay for RSV.

RESULTS

Except in influenza B, in SARS-CoV-2 and influenza A, there were no significant differences in detecting specific genes of the viruses among the three assays. All three kits did not cross-react with common respiratory viruses. All three kits had greater than 92% positive percent agreement and negative percent agreement and ≥ 0.95 kappa value in the detection of SARS-CoV-2 and flu A/B. Allplex detected RSV more sensitively than AdvanSure.

CONCLUSION

The overall performance of three multiplex rRT-PCR assays for the concurrent detection of SARS-CoV-2, influenza A/B, and RSV was comparable. These kits will promote prompt differential diagnosis of COVID-19, influenza, and RSV infection in the COVID-19 pandemic era.

One Year of the COVID-19 Pandemic in Galicia: A Global View of Age-Group Statistics during Three Waves

In this work we look at the past in order to analyze four key variables after one year of the COVID-19 pandemic in Galicia (NW Spain): new infected, hospital admissions, intensive care unit admissions and deceased. The analysis is presented by age group, comparing at each stage the percentage of the corresponding group with its representation in the society. The time period analyzed covers 1 March 2020 to 1 April 2021, and includes the influence of the B.1.1.7 lineage of COVID-19 which in April 2021 was behind 90% of new cases in Galicia. It is numerically shown how the pandemic affects the age groups 80+, 70+ and 60+, and therefore we give information about how the vaccination process could be scheduled and hints at why the pandemic had different effects in different territories.