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Background: The COVID-19 pandemic significantly affected high school students. Little is known about the mediators of student perceptions of infection prevention and public health entities. We piloted a survey to evaluate the relationship between student perceptions of COVID-19 topics and satisfaction with their most recent health class., Methods: Students from one private high school in southeast Michigan completed a survey in early 2022. The primary outcomes were 4 domains: vaccination knowledge, intervention effectiveness, intervention impact, and willingness to readopt an intervention. We assessed the associations between health class satisfaction and these outcomes using multiple linear regression., Results: One-hundred ninety students reported their health class satisfaction and were eligible for analysis. Students reported high confidence in vaccines (93%) but limited knowledge of COVID-19 vaccination (45%). Students perceived COVID-19 interventions as highly effective (range, 72% [hand hygiene]-93% [vaccination]) and reported a willingness to readopt them (range, 73% [stay-at-home orders]-96% [vaccination]). Health class satisfaction (54%) was positively associated with composite scores on vaccination knowledge and intervention effectiveness., Discussion: Assessing students' intrapandemic perspectives on infection prevention illuminated areas of strength (ie, intervention confidence) and areas for improvement (ie, intervention knowledge and institutional confidence)., Conclusions: Students reported favorable perceptions regarding common infection prevention interventions. Future work should investigate the role of educational satisfaction in mediating confidence in public health interventions and institutions., (Copyright © 2024 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

In late 2019, the emergence of COVID-19 in Wuhan, China, led to the implementation of stringent measures forming the zero-COVID policy aimed at eliminating transmission. Zero-COVID policy basically aimed at completely eliminating the transmission of COVID-19. However, the relaxation of this policy in late 2022 reportedly resulted in a rapid surge of COVID-19 cases. The aim of this work is to investigate the factors contributing to this outbreak using a new SEIR-type epidemic model with time-dependent level of immunity. Our model incorporates a time-dependent level of immunity considering vaccine doses administered and time-post-vaccination dependent vaccine efficacy. We find that vaccine efficacy plays a significant role in determining the outbreak size and maximum number of daily infected. Additionally, our model considers under-reporting in daily cases and deaths, revealing their combined effects on the outbreak magnitude. We also introduce a novel Physics Informed Neural Networks (PINNs) approach which is extremely useful in estimating critical parameters and helps in evaluating the predictive capability of our model., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ghosh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

We propose an epidemiological model with distributed recovery and death rates. It represents an integrodifferential system of equations for susceptible, exposed, infectious, recovered and dead compartments. This model can be reduced to the conventional ODE model under the assumption that recovery and death rates are uniformly distributed in time during disease duration. Another limiting case, where recovery and death rates are given by the delta-function, leads to a new point-wise delay model with two time delays corresponding to the infectivity period and disease duration. Existence and positiveness of solutions for the distributed delay model and point-wise delay model are proved. The basic reproduction number and the final size of the epidemic are determined. Both, the ODE model and the delay models are used to describe COVID-19 epidemic progression. The delay model gives a better approximation of the Omicron data than the conventional ODE model from the point of view of parameter estimation.

The work is devoted to a new immuno-epidemiological model with distributed recovery and death rates considered as functions of time after the infection onset. Disease transmission rate depends on the intra-subject viral load determined from the immunological submodel. The age-dependent model includes the viral load, recovery and death rates as functions of age considered as a continuous variable. Equations for susceptible, infected, recovered and dead compartments are expressed in terms of the number of newly infected cases. The analysis of the model includes the proof of the existence and uniqueness of solution. Furthermore, it is shown how the model can be reduced to age-dependent SIR or delay model under certain assumptions on recovery and death distributions. Basic reproduction number and final size of epidemic are determined for the reduced models. The model is validated with a COVID-19 case data. Modelling results show that proportion of young age groups can influence the epidemic progression since disease transmission rate for them is higher than for other age groups., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The virus that causes severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) belongs to the genus Beta coronavirus and the family Coronaviridae. The SARS-CoV-2 virus is a positive sense, non-segmented single-strand RNA virus that causes coronavirus disease 2019 (COVID-19), which was first reported in December 2019 in Wuhan, China. COVID-19 is now a worldwide pandemic. Globally, several newer variants have been identified; however, only a few of them are of concern (VOCs). VOCs differ in terms of infectivity, transmissibility, disease severity, drug efficacy, and neutralization efficacy by monoclonal antibodies, convalescent sera, or vaccines. VOCs reported from various parts of the world include B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617/B.1.617.2 (Delta), P.1 (Gamma), and B.1.1.529 (Omicron). These VOCs are the result of mutations, with some based on spike proteins. Mutations may also cause molecular diagnostic tests to fail to detect the few VOCs, leading to a delayed diagnosis, increased community spread, and delayed treatment. We searched PubMed, EMBASE, Covariant, Stanford variants database, and CINAHL from December 2019 to February 2022 using the following search terms: Variant of Concern, SARS-CoV-2, Omicron, etc. All types of research were chosen. All research methods were considered. This review discusses the various VOCs, as well as their mutations, infectivity, transmissibility, and neutralization efficacy.

During the early wave of the COVID-19 pandemic, the Scientific Registry of Transplant Recipients (SRTR) designated a "black out" period between March 12, 2020, and June 12, 2020, for transplant outcomes reporting. We discuss the implications and potential bias it has introduced as it may selectively favor the outcomes for certain regions and harm other regions due to varied effects of different waves of COVID-19 infections across the United States., (© 2022 The American Society of Transplantation and the American Society of Transplant Surgeons.)

A compartmental epidemiological model with distributed recovery and death rates is proposed. In some particular cases, the model can be reduced to the conventional SIR model. However, in general, the dynamics of epidemic progression in this model is different. Distributed recovery and death rates are evaluated from COVID-19 data. The model is validated by the epidemiological data for different countries, and it shows better agreement with the data than the SIR model. The time-dependent disease transmission rate is estimated., (© 2022. The Author(s), under exclusive licence to Society for Mathematical Biology.)

We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.

The study is the first attempt to assess the role of climatic predictors in the rise of COVID-19 intensity in the Russian climatic region. The study used the Random Forest algorithm to understand the underlying associations and monthly scenarios. The results show that temperature seasonality (29.2 ± 0.9%) has the highest contribution for COVID-19 transmission in the humid continental region. In comparison, the diurnal temperature range (26.8 ± 0.4%) and temperature seasonality (14.6 ± 0.8%) had the highest impacts in the sub-arctic region. Our results also show that September and October have favorable climatic conditions for the COVID-19 spread in the sub-arctic and humid continental regions, respectively. From June to August, the high favorable zone for the spread of the disease will shift towards the sub-arctic region from the humid continental region. The study suggests that the government should implement strict measures for these months to prevent the second wave of COVID-19 outbreak in Russia.

The SARS-CoV-2 is a new coronavirus responsible for the COVID-19 disease and has caused the pandemic worldwide. A large number of cases have overwhelmed the healthcare system worldwide. The COVID-19 infection has been associated with a heightened risk of thromboembolic complications. Various mechanisms are leading to the high thrombotic risk in COVID-19 patients such as inflammation, endotheliitis, hyperviscosity, and hypercoagulability. We searched PubMed, EMBASE, and CINAHL from January 2020 to December 2020. We used the following search terms: COVID-19, coagulopathy, and thrombosis. We reviewed the epidemiology, clinical features, mechanisms, and treatment of COVID-19-associated coagulopathy., (© 2021 John Wiley & Sons Ltd.)

The sudden surge in demand to use plastic products due to COVID-19 pandemic has increased plastic pollution. It has resulted into degradation of a broad range of habitats and ecosystems by destroying natural functions, water quality, and environmental sustainability. However, the government agencies, scientific communities, and the public, have started to give attention to this issue. So, in the present study, we used the correlation methods to check the relationship between COVID-19 affected population with the medical plastic waste (MPW) that has developed a conceptual model of the inter-linkages between the preventive measures of COVID-19 pandemic problems and the reduction challenges of plastic waste during and after pandemic scenarios. Emerging issues in the waste management during and after the COVID-19 are established by reviewing the literature, reports, policy briefs, and information from the website concerning COVID-19. Considering MPW management issues, we selected India as a case study to analyse the plastic waste footprint (PWF) due to COVID-19 pandemic. The correlation results showed COVID-19 affected population and MPW; COVID-19 affected population and PWF have a significant relationship (R 2 = 0.60; Area under ROC curve 81.4%). It suggests an urgent need for plastic waste management initiatives. Moreover, substantial plastic products, human awareness, strict government regulations, and inclusive research can check plastic waste footprints in India and worldwide. Then discuss the specific pathways through which the immediate and long-term impacts operate and highlight the issues of hampering the sustainable development goals (SDGs) progress in India and beyond. Finally, call for coordinated assessment, support and appropriate short- and long-term mitigation and the policy measures of plastic waste problems during and after the COVID-19 pandemic., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Coronavirus disease 2019 infection produce a prothrombotic state. This is initiated through multiple pathways and is finally aggravated by cross talks with cytokine storm and neutrophil, platelet, complement activation. All these combine towards the second week of illness to produce thrombosis in the lung capillaries surrounding the alveolus producing characteristic pulmonary dysfunction (PaO2/FiO2 > 300, normal or minimally increased lung compliance and very high d-dimer levels) and a high rate of peripheral venous thrombosis. International and many national guidelines have approached this state in different ways but all emphasized the need for management and prevention of widespread thrombosis. It is felt more aggressive and graded thrombosis prevention and management should be initiated early in the treatment. d-Dimer, neutrophil count, SaO2, fibrinogen levels should be used to control the hypercoagulability. Drugs like statins which have anti-inflammatory action as well as ability to reduce fibrinogen and other clotting factors should be used in the beginning along with antiplatelet drugs and progressively complement activation and neutrophil extracellular traps inhibitors, oral mucopolysaccharides, full-scale anticoagulation along with judicial use of fibrinolysis supporting drugs should be added. In the present review, we have evaluated the various studies and argued the rationality that the anticoagulation in this condition should be initiated early during the infection and should be increased in a graded manner depending on clinical and laboratory progression of the condition until a strong specific antiviral drug for coronavirus disease 2019 infection is available., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

In this study, we trace the COVID-19 pandemic's footprint across India's districts. We identify its primary epicentres and the outbreak's imprint in India's hinterlands in four separate time-steps, signifying the different lockdown stages. We also identify hotspots and predict areas where the pandemic may spread next. Significant clusters in the country's western and northern parts pose risk, along with the threat of rising numbers in the east. We also perform epidemiological and socioeconomic susceptibility and vulnerability analyses, identifying resident populations that may be physiologically weaker, leading to a high incidence of cases and pinpoint regions that may report high fatalities due to ambient poor demographic and health-related factors. Districts with a high share of urban population and high population density face elevated COVID-19 risks. Aspirational districts have a higher magnitude of transmission and fatality. Discerning such locations can allow targeted resource allocation to combat the pandemic's next phase in India., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020. Published by Elsevier Ltd.)

An extended SEIQR type model is considered in order to model the COVID-19 epidemic. It contains the classes of susceptible individuals, exposed, infected symptomatic and asymptomatic, quarantined, hospitalized and recovered. The basic reproduction number and the final size of epidemic are determined. The model is used to fit available data for some European countries. A more detailed model with two different subclasses of susceptible individuals is introduced in order to study the influence of social interaction on the disease progression. The coefficient of social interaction K characterizes the level of social contacts in comparison with complete lockdown (K=0) and the absence of lockdown (K=1). The fitting of data shows that the actual level of this coefficient in some European countries is about 0.1, characterizing a slow disease progression. A slight increase of this value in the autumn can lead to a strong epidemic burst.