Asymptomatic COVID-19 transmission: the importance of avoiding official miscommunication

Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease

In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning (ML)-based classification workflow that is intended as a template for researchers seeking to apply ML approaches for developing host-based infectious disease biomarker classifiers. As an example, we built a classification model that could accurately distinguish between three disease etiology classes: bacterial, viral, and normal in human sera using host protein biomarkers of known diagnostic utility. After collecting protein data from known disease samples, we trained a series of increasingly complex Auto-ML models until arriving at an optimized classifier that could differentiate viral, bacterial, and non-disease samples. Even when limited to a relatively small training set size, the model had robust diagnostic characteristics and performed well when faced with a blinded sample set. We present here a flexible approach for applying an Auto-ML-based workflow for the identification of host biomarker classifiers with diagnostic utility for infectious disease, and which can readily be adapted for multiple biomarker classes and disease states.

Crisis communication strategies for health officials

Background

Mixed messaging among health officials are prevalent amid COVID-19. Crisis communication strategies have the potential to help health officials effectively address issues such as mixed messages and improve their crisis communication efficacy. However, there is a dearth of insights in the literature. Therefore, to bridge the research gap, this study aims to examine practical strategies health officials can utilize to improve their crisis communication efficacy.

Methods

A literature review on effective crisis communication strategies amid COVID-19 was conducted in PubMed, Scopus, and PsycINFO, with a focus on scholarly literature published in English.

Results

The findings of the study identified the following strategies that health officials can utilize to improve their crisis communication capabilities: (1) develop fact-based, transparent, and accountable messaging, (2) utilize people-centered and empathetic persuasive strategies, and (3) leverage international collaboration for consistent messaging and comprehensive crisis communication.

Conclusion

COVID-19 has challenged health officials with unprecedented crisis communication duties and responsibilities. In this study, we underscored the importance of effective crisis communication amid global health emergencies like COVID-19, and identified communication strategies health officials could adopt or adapt to improve their crisis communication efficacy. Future research could explore strategies health officials can use to better communicate with government officials and media professionals to further help health officials improve their crisis communication capabilities, their abilities to avoid preventable miscommunication or mixed messaging, and in turn, society's collective strengthen in curbing and controlling the pandemic.

Diagnostic performance of the fully automated Roche Elecsys SARS-CoV-2 antigen electrochemiluminescence immunoassay: a pooled analysis

OBJECTIVES

Among the diagnostic tests that have recently become commercially available for diagnosing coronavirus disease 2019 (COVID-19), the fully-automated Roche Elecsys severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen electrochemiluminescence immunoassay (ECLIA) is one of the most widespread for its adaptability within a system of laboratory automation, rapidity and high-throughput. This article is aimed to provide the results of the first pooled analysis of its accuracy for diagnosing SARS-CoV-2 infections.

CONTENT

We carried out an electronic search in Scopus and Medline, without language or date restrictions (i.e., up to January 18, 2022), to identify articles where the diagnostic performance of Roche Elecsys SARS-CoV-2 antigen ECLIA was compared with that of reference molecular diagnostic techniques.

SUMMARY

Overall, 11 studies were identified, 10 of which (n=6,095 swabs) provided necessary data for inclusion in a pooled analysis. The pooled diagnostic sensitivity, specificity and area under the curve (AUC) in nasopharyngeal samples were 0.68 (95%CI, 0.66-0.70), 0.99 (95%CI, 0.99-0.99) and 0.958 (95%CI, 0.936-0.980), respectively. The cumulative observed agreement with reference molecular assays was 89.5% and the kappa statistic was 0.735 (95%CI, 0.716-0.754). The pooled diagnostic sensitivity in samples with high viral load (i.e., cycle threshold values <28-30) was 0.95 (95%CI, 0.92-0.97).

OUTLOOK

The results of this pooled analysis confirm that the fully-automated Roche Elecsys SARS-CoV-2 antigen ECLIA has high diagnostic specificity and optimal diagnostic sensitivity for identifying nasopharyngeal samples with higher viral load, thus making it a reliable technique for mass screening and for supporting strategies based on shorten isolation and/or quarantine.

Analysis of COVID-19 pandemics in Kazakhstan

Background: This study aimed to analyze the demographic and epidemiological features of identified COVID-19 cases in Kazakhstan.

Study design: A cross-sectional study.

Methods: This cross-sectional study aimed to analyze COVID-19 cases (n=5116) collected from March 13 to June 6, 2020, in Kazakhstan. The data were obtained from a state official medical electronic database. The study investigated the geographic and demographic data of patients as well as the association of COVID-19 cases with gender and age. The prevalence of symptoms, the presence of comorbidities, complications, and COVID-19 mortality were determined for all patients.

Results: The mean ±SD age of the patients in this study was 34.8 ±17.6 years, and the majority (55.7%) of COVID-19 cases were male and residents of cities (79.6%). In total, 80% of the cases had the asymptomatic/mild form of the disease. Cough (20.8 %) and sore throat (17.1%) were the most common symptoms among patients, and pneumonia was diagnosed in 1 out of 5 cases. Acute respiratory distress syndrome (ARDS) was recorded in 1.2% of the patients. The fatality rate was 1% in the study population and lethality was 2.6 times higher in males compared to females. Each additional year in age increased the probability of COVID-19 infection by 1.06 times. The presence of cardiovascular, diabetes, respiratory, and kidney diseases affected the rate of mortality (P<0.05).

Conclusions: The results demonstrated a high proportion (40%) of the asymptomatic type of coronavirus infection in the Kazakhstan population. The severity of COVID-19 symptoms and lethality were directly related to the age of patients and the presence of comorbidities.

Making sense of rapid antigen testing in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostics

Although the most effective strategy for preventing or containing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks relies on early diagnosis, the paramount and unprecedented number of tests needed to fully achieve this target is overwhelming worldwide testing supply and capacity. Molecular detection of SARS-CoV-2 RNA in nasopharyngeal swabs is still considered the reference diagnostic approach. Nonetheless, identification of SARS-CoV-2 proteins in upper respiratory tract specimens and/or saliva by means of rapid (antigen) immunoassays is emerging as a promising screening approach. These tests have some advantages compared to molecular analysis, such as point of care availability, no need of skilled personnel and dedicated instrumentation, lower costs and short turnaround time. However, these advantages are counterbalanced by lower diagnostic sensitivity compared to molecular testing, which would only enable to identifying patients with higher SARS-CoV-2 viral load. The evidence accumulated to-date has hence persuaded us to develop a tentative algorithm, which would magnify the potential benefits of rapid antigen testing in SARS-CoV-2 diagnostics.

SARS-CoV-2 recurrent RNA positivity after recovering from coronavirus disease 2019 (COVID-19): a meta-analysis

BACKGROUND AND AIM

Isolation of subjects with active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a pivotal preventive measure in the ongoing coronavirus disease 2019 (COVID-19) pandemic. A growing number of studies reported cases of recurrent SARS-CoV-2 RNA positivity following disease recovery, which were identified with a critical literature search and then meta-analyzed in this article.

MATERIALS AND METHODS

A digital search was performed in Medline and Web of Science, using the keywords "coronavirus disease 2019" OR "COVID-19" OR "severe acute respiratory disease 2" OR "SARS-CoV-2" AND "recurrence" OR "repositivization" OR "retesting", without date or language restrictions. Recovery was defined as resolution of symptoms, with at least two consecutive negative molecular tests.

RESULTS

A total number of 17 studies, with 5,182 COVID-19 patients, were included. SARS-CoV-2 recurrent RNA positivity in recovered COVID-19 patients ranged between 7-23% across the studies, with follow-up testing between 1-60 days. The estimated cumulative rate of SARS-CoV-2 recurrent RNA positivity was 12% (95% confidence interval, 12-13%; I2, 74%).

CONCLUSIONS

Repeated molecular testing on respiratory tracts specimens at 1 and 2 months after recovery from COVID-19 is strongly advisable for early identification, isolation and clinical management of subjects with SARS-CoV-2 recurrent RNA positivity.