Identifying asymptomatic cases during the mass COVID-19 vaccination campaign: insights and implications for policy makers

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID-19

The COVID-19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic. New modes of monitoring and sharing of epidemic data were implemented using Internet resources. We modified the basic SEIR compartmental model to include public health measures, multiwave scenarios, and the variation of viral infectivity and transmissibility reflected by the basic reproduction number R0 of emerging viral variants. SVEIR(MH) model considers the capacity of the medical system, lockdowns, vaccination, and changes in viral reproduction rate on the epidemic spread. The developed model uses daily infection reports for assessing the epidemic dynamics, and daily changes of mobility data from mobile phone networks to assess the lockdown effectiveness. This model was deployed to six European regions Baden-Württemberg (Germany), Belgium, Czechia, Lombardy (Italy), Sweden, and Switzerland for the first 2 years of the pandemic. The correlation coefficients between observed and reported infection data showed good concordance for both years of the pandemic (ρ = 0.84-0.94 for the raw data and ρ = 0.91-0.98 for smoothed 7-day averages). The results show stability across the regions and the different epidemic waves. Optimal control of epidemic waves can be achieved by dynamically adjusting epidemic control measures in real-time. SVEIR(MH) model can simulate different scenarios and inform adjustments to the public health policies to achieve the target outcomes. Because this model is highly representative of actual epidemic situations, it can be used to assess both the public health and socioeconomic effects of the public health measures within the first 7 days of the outbreak.

Disparate viral pandemics from COVID19 to monkeypox and beyond: a simple, effective and universal therapeutic approach hiding in plain sight

The field of antiviral therapeutics is fixated on COVID19 and rightly so as the fatalities at the height of the pandemic in the United States were almost 1,000,000 in a twelve month period spanning parts of 2020/2021. A coronavirus called SARS-CoV2 is the causative virus. Development of a vaccine through molecular biology approaches with mRNA as the inducer of virus spike protein has played a major role in driving down mortality and morbidity. Antivirals have been of marginal value in established infections at the level of hospitalization. Thus, the current focus is on early symptomatic infection of about the first five days. The Pfizer drug paxlovid which is composed of nirmatrelvir, a peptidomimetic protease inhibitor of SARS-CoV2 Mpro enzyme, and ritonavir to retard degradation of nirmatrelvir, is the current FDA recommended treatment of early COVID19. There is no evidence of broad antiviral activity of paxlovid against other diverse viruses such as the influenza virus, poxviruses, as well as a host of respiratory viruses. Although type I interferons (IFNs) are effective against SARS-CoV2 in cell cultures and in early COVID19 infections, they have not been broadly recommended as therapeutics for COVID19. We have developed stable peptidomimetics of both types I and II IFNs based on our noncanonical model of IFN signaling involving the C-terminus of the IFNs. We have also identified two members of intracellular checkpoint inhibitors called suppressors of cytokine signaling (SOCS), SOCS1 and SOCS3 (SOCS1/3), and shown that they are virus induced intrinsic virulence proteins with activity against IFN signaling enzymes JAK2 and TYK2. We developed a peptidomimetic antagonist, based on JAK2 activation loop, against SOCS1/3 and showed that it synergizes with the IFN mimetics for potent broad spectrum antiviral activity without the toxicity of intact IFN molecules. IFN mimetics and the SOCS1/3 antagonist should have an advantage over currently used antivirals in terms of safety and potency against a broad spectrum of viruses.

COVID-19: A state of art on immunological responses, mutations, and treatment modalities in riposte

Over the last few years, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) unleashed a global public health catastrophe that had a substantial influence on human physical and mental health, the global economy, and socio-political dynamics. SARS-CoV-2 is a respiratory pathogen and the cause of ongoing COVID-19 pandemic, which testified how unprepared humans are for pandemics. Scientists and policymakers continue to face challenges in developing ideal therapeutic agents and vaccines, while at the same time deciphering the pathology and immunology of SARS-CoV-2. Challenges in the early part of the pandemic included the rapid development of diagnostic assays, vaccines, and therapeutic agents. The ongoing transmission of COVID-19 is coupled with the emergence of viral variants that differ in their transmission efficiency, virulence, and vaccine susceptibility, thus complicating the spread of the pandemic. Our understanding of how the human immune system responds to these viruses as well as the patient groups (such as the elderly and immunocompromised individuals) who are often more susceptible to serious illness have both been aided by this epidemic. COVID-19 causes different symptoms to occur at different stages of infection, making it difficult to determine distinct treatment regimens employed for the various clinical phases of the disease. Unsurprisingly, determining the efficacy of currently available medications and developing novel therapeutic strategies have been a process of trial and error. The global scientific community collaborated to research and develop vaccines at a neck-breaking speed. This review summarises the overall picture of the COVID-19 pandemic, different mutations in SARS-CoV-2, immune response, and the treatment modalities against SARS-CoV-2.

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Background and Objective

: The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques.

Methods

: The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability.

Results

: Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement.

Conclusion

: Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

Clinical epidemiological applicability of real-time polymerase chain reaction for COVID-19

OBJECTIVES

Real-time polymerase chain reaction is currently used as a confirmatory test for coronavirus disease 2019 (COVID-19). The test results are interpreted as positive, negative, or inconclusive, and are used only for a qualitative classification of patients. However, the test results can be quantitated using threshold count (Ct) values to determine the amount of virus present in the sample. Therefore, this study investigated the diagnostic usefulness of Ct results through various quantitative analyzes, along with an analysis of clinical and epidemiological characteristics.

METHODS

Clinical and epidemiological data from 4,642 COVID-19 patients in April 2021 were analyzed, including the Ct values of the RNA-dependent RNA polymerase (RdRp), envelope (E), and nucleocapsid (N) genes. Clinical and epidemiological data (sex, age, underlying diseases, and early symptoms) were collected through a structured questionnaire. A correlation analysis was used to examine the relationships between variables.

RESULTS

All 3 genes showed statistically significant relationships with symptoms and severity levels. The Ct values of the RdRp gene decreased as the severity of the patients increased. Moreover, statistical significance was observed for the presence of underlying diseases and dyspnea.

CONCLUSION

Ct values were found to be related to patients' clinical and epidemiological characteristics. In particular, since these factors are closely related to symptoms and severity, Ct values can be used as primary data for predicting patients' disease prognosis despite the limitations of this method. Conducting follow-up studies to validate this approach might enable using the data from this study to establish policies for preventing COVID-19 infection and spread.

Risk Factors of Symptomatic COVID-19 in Samtse District, Bhutan

Introduction

All Coronavirus disease 2019 (COVID-19) positive cases in Samtse District, Bhutan were isolated in the isolation facilities managed by the government hospitals. This study aimed to identify the socio-demographic risk factors for developing COVID-19 symptoms amongst these patients.

Methods and Materials

A secondary data of the COVID-19 positive cases from isolation facilities of Samtse District from 5 May to 7 September 2021 was used for this study. Survival analysis was carried out to estimate the cumulative probability of symptom onset time by each risk factor. Kaplan–Meier curves were used to estimate the probabilities for the onset of symptoms at different time points and a log-rank test was employed to assess the differences between covariates.

Results

A total of 449 patients were included, of which 55.2% were males and 73.3% (328) were aged >18 years. The mean age was 42 years with a range of 3 months to 83 years. Forty-seven percent (213) reported at least one symptom. Common symptoms were fever (32.3%, 145), headache (31.6%, 142), and cough (30.1%, 135), respectively. Males were 64% less likely to be symptomatic than females [adjusted hazard ratio (aHR) = 0.36, 95% confidence interval (CI) 0.183–0.917]. Farmers (aHR = 3.17, 95% CI 1.119–8.953), and drivers and loaders (aHR = 3.18, 95% CI 1.029–9.834) were 3 times more likely to be symptomatic compared to housewives. Residents of Samtse sub-districts were 5 times more likely to be symptomatic than those living in other sub-districts (aHR = 5.16, 95% CI 2.362–11.254).

Conclusion

The risk of developing COVID-19 symptoms was being fe male, farmers, drivers and loaders, and residents of the Samtse sub-district. These high-risk groups should be provided additional care when in isolation facilities.

Second Wave of COVID-19 Pandemic in Argentinian Population: Vaccination Is Associated With a Decrease in Depressive Symptoms

BACKGROUND

Since the irruption of the coronavirus disease 2019 (COVID-19) the planet has submerged in a time of concern and uncertainty, with a direct impact on people's mental health. Moreover, the recurrent outbreaks that periodically harry different regions of the world constantly refocus people's concerns to the pandemic. Yet, each new wave heats the diverse countries in different situations, including the advances in their vaccination campaigns. In this research, we studied the levels of the general anxiety disorder (GAD) and depression in the Argentine population across the first and second waves of infections that occurred in our country.

METHODS

We conducted an on-line survey, within each peak of the pandemic. People were asked to self-report GAD and depression symptoms using the GAD-7 and PHQ-9 questioners, inform their vaccination status, the frequency they performed physical activity as well as working condition and modality. Here, we identified the more vulnerable groups and evaluated factors that could mitigate the rise of these mental disorders, focusing on vaccination.

RESULTS

Our data shows that reported GAD and depression levels were higher during the second wave than during the first one. More importantly, vaccinated people were less depressed than non-vaccinated people, while GAD levels remained equivalent in both groups. Other factors directly associated with lower GAD and depression levels were performing frequent physical activity and being employed, regardless of the employment modality. These observations were replicated in different age ranges and genders.

CONCLUSION

This work evidences GAD and depression in different pandemic waves in Argentina, as well the factors that may contribute to reducing the magnitude of these disorders, including vaccination.

Epidemiological and clinical characteristics of vaccinated COVID-19 patients: A meta-analysis and systematic review

Objective: With the global epidemic of coronavirus disease 2019 (COVID-19), vaccination rates are increasing globally. This study evaluated the relevant clinical manifestations of vaccinated COVID-19 patients. Methods: We searched carefully in 11 databases such as PubMed, Embase, Scopus, Cochrane Library, Web of Science, Ovid, China National Knowledge Infrastructure Database, Wan Fang Data, Sinomed, VIP Database, and Reading Showing Database up to 26 March 2022. To search for articles that have described the characteristics of vaccinated patients including epidemiological and clinical symptoms. Statistical analysis of the extracted data using STATA 14.0. Results: A total of 58 articles and 263,708 laboratory-confirmed COVID-19 patients were included. Most of the patients in the vaccinated group had more asymptomatic infection and fewer severe illnesses. There were significant differences in ethnicity, and strain infected with COVID-19, and comorbidities (hyperlipidemia, diabetes, obesity, kidney disease, immunocompromised, cardiovascular disease, and tumor) and symptoms (fever, cough, gastrointestinal symptoms, neurological symptoms, and dysgeusia/anosmia) between vaccinated group and unvaccinated group. Oxygen support, use of steroid, days in hospital, hospital treatment, ICU treatment, death, and poor prognosis were also significantly different. Conclusion: Compared with the vaccinated group, patients in the unvaccinated group had a more severe clinical manifestations. Vaccines are also protective for infected people.