Mathematical modeling for Delta and Omicron variant of SARS-CoV-2 transmission dynamics in Greece

Masks and respirators for prevention of respiratory infections: a state of the science review

SUMMARYThis narrative review and meta-analysis summarizes a broad evidence base on the benefits-and also the practicalities, disbenefits, harms and personal, sociocultural and environmental impacts-of masks and masking. Our synthesis of evidence from over 100 published reviews and selected primary studies, including re-analyzing contested meta-analyses of key clinical trials, produced seven key findings. First, there is strong and consistent evidence for airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory pathogens. Second, masks are, if correctly and consistently worn, effective in reducing transmission of respiratory diseases and show a dose-response effect. Third, respirators are significantly more effective than medical or cloth masks. Fourth, mask mandates are, overall, effective in reducing community transmission of respiratory pathogens. Fifth, masks are important sociocultural symbols; non-adherence to masking is sometimes linked to political and ideological beliefs and to widely circulated mis- or disinformation. Sixth, while there is much evidence that masks are not generally harmful to the general population, masking may be relatively contraindicated in individuals with certain medical conditions, who may require exemption. Furthermore, certain groups (notably D/deaf people) are disadvantaged when others are masked. Finally, there are risks to the environment from single-use masks and respirators. We propose an agenda for future research, including improved characterization of the situations in which masking should be recommended or mandated; attention to comfort and acceptability; generalized and disability-focused communication support in settings where masks are worn; and development and testing of novel materials and designs for improved filtration, breathability, and environmental impact.

Impacts of Vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 Variants Alpha and Delta on Coronavirus Disease 2019 Transmission Dynamics in Four Metropolitan Areas of the United States

To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x  to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.

Omicron variant dominance and anti-SARS-CoV-2 vaccination are key determinants for a milder course of COVID-19 in patients with systemic autoimmune rheumatic diseases

INTRODUCTION

This study aimed to determine whether the introduction of anti-SARS-CoV-2 vaccines and the dominance of the omicron variant had a significant impact on the outcome of COVID-19 in patients with systemic autoimmune rheumatic diseases (SAIRDs).

METHODS

Using data entered to the Greek Rheumatology Society COVID-19 registry, we investigated the incidence of hospitalization and death due to COVID-19, during the successive periods of the pandemic according to the prevalent strain (wild-type, Alpha, Delta, Omicron) in vaccinated and unvaccinated patients. Variables independently associated with hospitalization and death were explored using multivariate regression analyses, while Kaplan-Meier curves were used to depict survival data.

RESULTS

From August 2020 until June 30, 2022, 456 cases (70.2% females) of COVID-19 with a mean age (± SD) of 51.4 ± 14.0 years were reported. In unvaccinated patients, the proportions of hospitalization and death were 24.5% and 4%, compared to 12.5% and 0.8% in the vaccinated group (p < 0.001 for both comparisons). The rates of hospitalization for the wild-type, Alpha, Delta, and Omicron periods were 24.7%, 31.3%, 25.9%, and 8.1% respectively (p < 0.0001), while the case fatality rates were 2.7%, 4%, 7%, and 0%, respectively (p = 0.001). Using multivariable regression analysis, factors independently associated with hospitalization were infection by a non-Omicron variant, being non-vaccinated, exposure to rituximab, older age, and respiratory and cardiovascular disease. Independent predictors for death were contracting COVID-19 during the Alpha or Delta period, pulmonary disease, and older age, while being vaccinated was protective.

CONCLUSIONS

In this 2-year analysis, the rates of hospitalization and death among patients with SAIRDs have declined significantly. Vaccination and the dominance of the Omicron variant appear to be the major determinants for this shift. Key points • During the late phase of the pandemic, the proportion of severe COVID-19 cases, defined as requiring hospitalization or resulting in death, in patients with systemic autoimmune rheumatic diseases has declined. • Anti-SARS-CoV-2 vaccination and the dominance of the Omicron strain are the key factors that have independently contributed to this shift.

Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium

Introduction

COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension.

Methods

We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January - 31 December 2021 using canonical correlation analysis.

Results

Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition.

Conclusion

It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.