Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients

BACKGROUND

Butyrate may inhibit SARS-CoV-2 replication and affect the development of COVID-19. However, there have been no systematic comprehensive analyses of the role of butyrate metabolism-related genes (BMRGs) in COVID-19.

METHODS

We performed differential expression analysis of BMRGs in the brain, liver and pancreas of COVID-19 patients and controls in GSE157852 and GSE151803. The differentially expressed genes (DEGs) and module genes between COVID-19 patients and healthy controls in GSE171110 were screened through 'limma' and 'WGANA' R package, respectively, followed by an intersection with BMRGs via 'ggvenn' R package. Six machine learning algorithms were employed to determine the best model for identifying biomarkers, and receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic value of the biomarkers in COVID-19. Moreover, the differences in immune-infiltrating cells between the COVID-19 and control groups were compared using CIBERSORT. The differences in immune cells and expression levels of biomarkers in immune cells among different tissues were analysed using GSE171668.

RESULTS

The BMRGs were the most different in the brain between the COVID-19 and control groups, including 21 upregulated and 16 downregulated genes. Five important common BMRGs were screened as biomarkers for COVID-19 using XGBoost, namely CCNB1, CCNA2, BRCA1, HBB and HSPA5, with increased diagnostic performance. Enrichment analysis revealed that these five genes were related to the cell cycle, cell proliferation and cell senescence. The infiltrating abundance of 12 immune cells was different between the COVID-19 and control groups. Finally, the expression levels of HSPA5, BRCA1 and HBB were higher in annotated cells than in CCNB1 and CCNA2, and there were four different types of immune cells in the liver, heart, lungs and kidneys.

CONCLUSIONS

These five genes may be potential biomarkers of butyrate metabolism in COVID-19 patients. These findings provide a direction for further studies on the molecular mechanisms underlying COVID-19.

modelSSE: An R Package for Characterizing Infectious Disease Superspreading from Contact Tracing Data

Infectious disease superspreading is a phenomenon where few primary cases generate unexpectedly large numbers of secondary cases. Superspreading, is frequently documented in epidemiology literature, and is considered a consequence of heterogeneity in transmission. Since understanding the risks of superspreading became a rising concern from both statistical modelling and public health aspects, the R package modelSSE provides comprehensive analytical tools to characterize transmission heterogeneity. The package modelSSE integrates recent advances in statistical methods, such as decomposition of reproduction number, for modelling infectious disease superspreading using various types and sources of contact tracing data that allow models to be grounded in real-world observations. This study provided an overview of the theoretical background and implementation of modelSSE, designed to facilitate learning infectious disease transmission, and explore novel research questions for transmission risks and superspreading potentials. Detailed examples of classic, historical infectious disease datasets are given for demonstration and model extensions.

A simple model of coupled individual behavior and its impact on epidemic dynamics

Containing infectious disease outbreaks is a complex challenge that usually requires the deployment of multiple intervention strategies. While mathematical modeling of infectious diseases is a widely accepted tool to evaluate intervention strategies, most models and studies overlook the interdependence between individuals' reactions to simultaneously implemented interventions. Intervention modeling efforts typically assume that individual adherence decisions are independent of each other. However, in the real world, individuals who are willing to comply with certain interventions may be more or less likely to comply with another intervention. The combined effect of interventions may depend on the correlation between adherence decisions. In this study, we consider vaccination and non-pharmaceutical interventions, and study how the correlation between individuals' behaviors towards these two interventions strategies affects the epidemiological outcomes. Furthermore, we integrate disease surveillance in our model to study the effects of interventions triggered by surveillance events. This allows us to model a realistic operational context where surveillance informs the timing of interventions deployment, thereby influencing disease dynamics. Our results demonstrate the diverse effects of coupled individual behavior and highlight the importance of robust surveillance systems. Our study yields the following insights: (i) there exists a correlation level that minimizes the initial prevalence peak size; (ii) the optimal correlation level depends on the disease's basic reproduction number; (iii) disease surveillance modulates the impact of interventions on reducing the epidemic burden.

Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The mutations that we infer to have the largest effects on transmission are strongly supported by experimental evidence from prior studies. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when they comprised only around 1-2% of sample sequences. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data.

HealthPass: a contactless check-in and adaptive access control system for lowering cluster infection risk in public health crisis

Introduction

Ensuring effective measures against the spread of the virus is paramount for educational institutions and workplaces as they resume operations amidst the ongoing public health crisis. A touchless and privacy-conscious check-in procedure for visitor assessment is critical to safeguarding venues against potential virus transmission.

Methods

In our study, we developed an interaction-free entry system featuring anonymous visitors who voluntarily provide data. This system introduces an adaptable venue entry management mechanism that accounts for both visitors' potential risk and the venue's capacity, aiming to curb the risk of localized infections. We assess visitors' liability based on their voluntarily provided data through radar map analysis. Additionally, we evaluate the venue's situation by quantifying its risk from multiple dimensions. A queuing model is then employed to control visitor access adaptively based on visitors' liability and the venue's availability.

Results

Since May, our university campus has been the operational site for the implemented system, catering to the needs of visitors across distinct venues. Using real-world implementation, we conduct a series of simulation experiments and case studies to verify the effectiveness of the HealthPass system in lowering infection risks.

Discussion

The system has demonstrated its capacity to reduce infection risks by adapting visitor entry procedures based on individual risk factors and venue conditions. Our results suggest that the integration of a dynamic queuing model and real-time data analysis can effectively manage the flow of visitors while ensuring public health safety.

On optimal control at the onset of a new viral outbreak

We propose a versatile model with a flexible choice of control for an early-pandemic outbreak prevention when vaccine/drug is not yet available. At that stage, control is often limited to non-medical interventions like social distancing and other behavioral changes. For the SIR optimal control problem, we show that the running cost of control satisfying mild, practically justified conditions generates an optimal strategy, u(t), t ∈ [0, T], that is sustainable up until some moment τ ∈ [0, T). However, for any t ∈ [τ, T], the function u(t) will decline as t approaches T, which may cause the number of newly infected people to increase. So, the window from 0 to τ is the time for public health officials to prepare alternative mitigation measures, such as vaccines, testing, antiviral medications, and others. In addition to theoretical study, we develop a fast and stable computational method for solving the proposed optimal control problem. The efficiency of the new method is illustrated with numerical examples of optimal control trajectories for various cost functions and weights. Simulation results provide a comprehensive demonstration of the effects of control on the epidemic spread and mitigation expenses, which can serve as invaluable references for public health officials.

Psychosocial Impact of Quarantines: A Systematic Review with Meta-Analysis

Background: Quarantine is one of the most effective strategies to control outbreaks of communicable diseases. Individuals under isolation or quarantine experience both physical and mental effects. Therefore, given its widespread implementation around the world, it is pertinent to analyze this effect on physical and mental health. The psychosocial impact model, which divides four dimensions into two main points, exposure or protection and positive or negative responses, was used to analyze the psychosocial impact of quarantine. Objectives: The present study aimed to investigate the psychosocial impact of people exposed to or protected from quarantine. Methods: We conducted a search for primary articles in the Web of Science and Scopus databases, and after applying the inclusion and exclusion criteria, we meta-analyzed five of them. Results: Quarantined individuals were more likely to experience anxiety (K = 4; OR = 2.62) and depressive symptoms (K = 6; OR = 1.61) compared to those who did not undergo quarantine. Researchers discuss a twofold increase in the probability of anxiety or depression among those in quarantine. As a result, advancing interventions that reduce this impact is critical for both health and the global economy. Conclusions: In terms of economic variables, the non-moderation of GDP per capita and the moderation of the GINI index stand out, demonstrating that countries must move toward policies that promote the reconstruction of more resilient and inclusive societies.

A novel multivariate time series forecasting dendritic neuron model for COVID-19 pandemic transmission tendency

A novel coronavirus discovered in late 2019 (COVID-19) quickly spread into a global epidemic and, thankfully, was brought under control by 2022. Because of the virus's unknown mutations and the vaccine's waning potency, forecasting is still essential for resurgence prevention and medical resource management. Computational efficiency and long-term accuracy are two bottlenecks for national-level forecasting. This study develops a novel multivariate time series forecasting model, the densely connected highly flexible dendritic neuron model (DFDNM) to predict daily and weekly positive COVID-19 cases. DFDNM's high flexibility mechanism improves its capacity to deal with nonlinear challenges. The dense introduction of shortcut connections alleviates the vanishing and exploding gradient problems, encourages feature reuse, and improves feature extraction. To deal with the rapidly growing parameters, an improved variation of the adaptive moment estimation (AdamW) algorithm is employed as the learning algorithm for the DFDNM because of its strong optimization ability. The experimental results and statistical analysis conducted across three Japanese prefectures confirm the efficacy and feasibility of the DFDNM while outperforming various state-of-the-art machine learning models. To the best of our knowledge, the proposed DFDNM is the first to restructure the dendritic neuron model's neural architecture, demonstrating promising use in multivariate time series prediction. Because of its optimal performance, the DFDNM may serve as an important reference for national and regional government decision-makers aiming to optimize pandemic prevention and medical resource management. We also verify that DFDMN is efficiently applicable not only to COVID-19 transmission prediction, but also to more general multivariate prediction tasks. It leads us to believe that it might be applied as a promising prediction model in other fields.

EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering

Accurately estimating the effective reproduction number (Rt) of a circulating pathogen is a fundamental challenge in the study of infectious disease. The fields of epidemiology and pathogen phylodynamics both share this goal, but to date, methodologies and data employed by each remain largely distinct. Here we present EpiFusion: a joint approach that can be used to harness the complementary strengths of each field to improve estimation of outbreak dynamics for large and poorly sampled epidemics, such as arboviral or respiratory virus outbreaks, and validate it for retrospective analysis. We propose a model of Rt that estimates outbreak trajectories conditional upon both phylodynamic (time-scaled trees estimated from genetic sequences) and epidemiological (case incidence) data. We simulate stochastic outbreak trajectories that are weighted according to epidemiological and phylodynamic observation models and fit using particle Markov Chain Monte Carlo. To assess performance, we test EpiFusion on simulated outbreaks in which transmission and/or surveillance rapidly changes and find that using EpiFusion to combine epidemiological and phylodynamic data maintains accuracy and increases certainty in trajectory and Rt estimates, compared to when each data type is used alone. We benchmark EpiFusion's performance against existing methods to estimate Rt and demonstrate advances in speed and accuracy. Importantly, our approach scales efficiently with dataset size. Finally, we apply our model to estimate Rt during the 2014 Ebola outbreak in Sierra Leone. EpiFusion is designed to accommodate future extensions that will improve its utility, such as explicitly modelling population structure, accommodations for phylogenetic uncertainty, and the ability to weight the contributions of genomic or case incidence to the inference.

Investigating the potential of natural compounds as novel inhibitors of SARS-CoV-2 RdRP using computational approaches

COVID-19 is a highly contagious disease caused by SARS-CoV-2. Currently, no vaccines or antiviral treatments are available to combat this deadly virus; however, precautions and some repurposed medicines are available to contain COVID-19. RNA-dependent RNA polymerase (RdRP) plays an important role in the replication or transcription of viral mechanisms. Approved antiviral drug such as Remdesivir has shown inhibitory activity against SARS-CoV-2 RdRP. The purpose of this study was to carry out a rational screening of natural products against SARS-CoV-2 RdRP, which may serve as a basis to develop a treatment option against COVID-19. For this purpose, a protein and structure conservation analysis of SARS-CoV-2 RdRP was performed to check mutations. A library of 15,000 phytochemicals was developed from literature review, ZINC database, PubChem and MPD3 database; and was used to performed molecular docking and molecular dynamics simulation (MD) analysis. The top-ranked compounds were subjected to pharmacokinetic and pharmacological studies. Among them, top 7 compounds (Spinasaponin A, Monotropane, Neohesperidoe, Posin, Docetaxel, Psychosaponin B2, Daphnodrine M, and Target Remedesvir) were noticed to interact with the active site residues. MD simulation in aqueous solution suggested conformational flexibility of loop regions in the complex to stabilize the docked inhibitors. Our study revealed that the studied compounds have potential to bind to the active site residues of SARS-CoV-2 RdRP. Although, this computational work is not experimentally determined but the structural information and selected compounds might help in the design of antiviral drugs targeting SAR-CoV-2 by inhibiting the activity of SARS-CoV-2 RdRP.

Dual-strain dynamics of COVID-19 variants in India: Modeling, analysis, and implications for pandemic control

This study introduces a detailed compartmental model developed to understand the complex dynamics of COVID-19 transmission, focusing on the Delta and Omicron variants in India. The model tracks disease progression through different population compartments, considering factors like vaccination, time-dependent transmission, economic burden and COVID-19 death rates, loss of vaccine-induced immunity, and the transition of asymptomatic cases to recovery. The model is validated against established epidemiological knowledge and real-world data, emphasizing dynamic parameterization and accurate representation of immunity dynamics. The basic reproduction number for both variants is calculated, and sensitivity analysis for various parameters is conducted. Time-dependent parameters are estimated using the discrete inverse method. The study also explores the economic burden, impact of different types of masks, vaccine efficacy, and vaccine-induced immunity through numerical analysis.

Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

Altered Liver Enzyme Markers in Patients with Asymptomatic, and Mild Omicron Infection: A Retrospective Study

Purpose

The emergence of the SARS-CoV-2 Omicron variant has posed a significant global public health challenge. Elucidating the laboratory profiles of individuals infected with this variant is crucial for assessing organ damage. This study aimed to investigate the variations in liver function tests and their correlation with demographic characteristics and inflammatory markers in patients with early Omicron variant infections.

Patients and Methods

A retrospective cohort study was conducted on 1133 mild or asymptomatic COVID-19 cases at Tianjin First Central Hospital. Data on age, gender, body mass index (BMI), and serum markers were collected and analyzed. Statistical analyses were performed using SPSS software, version 24.0.

Results

Abnormal liver function parameters, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), and total bilirubin (TBIL), were observed in 314 (27.71%) patients. "Hepatocellular type" was identified in 56 (4.94%) patients, "cholestatic type" in 185 (16.33%) patients, and "mixed type" in 73 (6.44%) patients. In the mixed group, we observed a pronounced elevation in the levels of ALT, AST, and GGT. Moreover, the hepatocellular group exhibited a statistically significant increase in AST and ALT concentrations relative to both the normal and cholestatic groups. Notably, the cholestatic group demonstrated a substantial increment in ALP levels. Males had a significantly higher prevalence of "abnormal liver enzyme markers" compared to females. Patients with "abnormal liver enzyme markers" exhibited significantly decreased immunoglobulin G (IgG) levels and elevated levels of inflammatory markers, including procalcitonin (PCT), interleukin-6 (IL6), as well as C-reactive protein (CRP) compared to normal group. Logistic regression analysis revealed that male gender and PCT levels were significantly associated with the risk of abnormal liver enzyme markers. Patients in hepatocellular group were likely accompanied with high CRP levels, whereas those in the cholestatic type were associated with high IL6 levels.

Conclusion

Early Omicron infection might cause liver stress response. Elevated liver enzyme marker levels were correlated with age, gender, inflammatory factors, and IgG.

Data-driven mathematical modeling approaches for COVID-19: A survey

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

Pathogenic mechanisms of cardiovascular damage in COVID-19

BACKGROUND

COVID-19 is a new infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). Since the outbreak in December 2019, it has caused an unprecedented world pandemic, leading to a global human health crisis. Although SARS CoV-2 mainly affects the lungs, causing interstitial pneumonia and severe acute respiratory distress syndrome, a number of patients often have extensive clinical manifestations, such as gastrointestinal symptoms, cardiovascular damage and renal dysfunction.

PURPOSE

This review article discusses the pathogenic mechanisms of cardiovascular damage in COVID-19 patients and provides some useful suggestions for future clinical diagnosis, treatment and prevention.

METHODS

An English-language literature search was conducted in PubMed and Web of Science databases up to 12th April, 2024 for the terms "COVID-19", "SARS CoV-2", "cardiovascular damage", "myocardial injury", "myocarditis", "hypertension", "arrhythmia", "heart failure" and "coronary heart disease", especially update articles in 2023 and 2024. Salient medical literatures regarding the cardiovascular damage of COVID-19 were selected, extracted and synthesized.

RESULTS

The most common cardiovascular damage was myocarditis and pericarditis, hypertension, arrhythmia, myocardial injury and heart failure, coronary heart disease, stress cardiomyopathy, ischemic stroke, blood coagulation abnormalities, and dyslipidemia. Two important pathogenic mechanisms of the cardiovascular damage may be direct viral cytotoxicity as well as indirect hyperimmune responses of the body to SARS CoV-2 infection.

CONCLUSIONS

Cardiovascular damage in COVID-19 patients is common and portends a worse prognosis. Although the underlying pathophysiological mechanisms of cardiovascular damage related to COVID-19 are not completely clear, two important pathogenic mechanisms of cardiovascular damage may be the direct damage of the SARSCoV-2 infection and the indirect hyperimmune responses.

Assessment of Covid-19 vaccine confidence among healthcare personnel in the safety-net sector in the United States and Puerto Rico

BACKGROUND

This study aimed to assess COVID-19 vaccine confidence among healthcare personnel in the safety net sector of the United States and Puerto Rico. This study aimed to examine the extent to which increased knowledge and positive attitudes toward COVID-19 vaccine safety and efficacy were associated with healthcare workers' COVID-19 vaccination status and their recommendation of the vaccine to all patients.

METHODS

Online survey data were collected from health care workers working in Free and Charitable Clinics across the United States and Federally Qualified Health Centers in Puerto Rico. The survey consisted of 62 questions covering various demographic measures and constructs related to healthcare workers' vaccination status, beliefs, and recommendations for COVID-19 vaccination. Statistical analyses, including multivariate analysis, were conducted to identify the factors associated with the COVID-19 vaccine status and recommendations among healthcare personnel.

RESULTS

Among the 2273 respondents, 93% reported being vaccinated against COVID-19. The analysis revealed that respondents who believed that COVID-19 vaccines were efficacious and safe were three times more likely to be vaccinated and twice as likely to recommend them to all their patients. Respondents who believed they had received adequate information about COVID-19 vaccination were 10 times more likely to be vaccinated and four times more likely to recommend it to all their patients.

CONCLUSIONS

The study results indicate that healthcare workers' confidence in COVID-19 vaccines is closely tied to their level of knowledge, positive beliefs, and attitudes about vaccine safety and efficacy. The study emphasizes the significance of healthcare workers feeling well informed and confident in their knowledge to recommend the vaccine to their patients. These findings have important implications for the development of strategies to boost COVID-19 vaccine confidence among healthcare workers and increase vaccine uptake among patients.

Residents' KABP and Social Support Statuses Under Normalized COVID-19 Pandemic Prevention and Control: A Cross-Sectional Study

OBJECTIVES

To investigate the status quo of residents' knowledge, attitude, belief, and practice (KABP) and social support and the correlation of KABP with social support under normalized coronavirus disease (COVID-19) pandemic prevention and control.

METHODS

A questionnaire was designed based on the KABP model, and an online survey was conducted among residents in September 2022. SPSS software (version 25.0) was used to analyze the data. Two independent sample t-tests, one-way analysis of variance (ANOVA), multivariate linear regression analysis, and Pearson's correlation analysis were conducted.

RESULTS

In total, 326 valid questionnaires were obtained. The scoring rates of residents' KABP and social support were 68.1%, 92.2%, 89.3%, 75.3%, and 62.6%, respectively. Main factors influencing residents' knowledge included gender, nationality, education level, practice, and social support; those influencing attitude were belief and practice; those influencing belief were place of residence, attitude, and practice; those influencing practice were knowledge, attitude, belief, and social support; and those influencing social support were marital status, place of residence, knowledge, and practice. Social support was positively correlated with knowledge and practice.

CONCLUSIONS

This study provides a scientific foundation for the current normalized prevention and control of COVID-19 and is conducive to health managers to better carry out prevention and control related health education for specific groups.

Belief, knowledge, attitude and practices towards COVID-19 amongst residents of Abuja, Nigeria: implications for pandemic preparedness

Introduction

coronavirus disease, (COVID-19), was a pandemic with high global morbidity and mortality, partly due to a lack of preparedness. People´s knowledge, belief, attitude, and perception of disease outbreaks may affect their response, and this may impact their health-related behavior. This study was designed to determine the pattern of belief, knowledge, attitude, and practices (BKAP) of residents of Abuja, Nigeria, towards the COVID-19 pandemic. The outcome of the study may help to make informed decisions on future pandemic preparedness.

Methods

a cross-sectional study with data collected online about the local perceptions and common concerns, beliefs, misconceptions, attitudes, and conspiracy theories amongst residents of the FCT. A self-reported validated e-questionnaire prepared on Google Forms was used. The obtained data was downloaded on Excel sheet and then exported to SPSS for analysis.

Results

there were one thousand eight hundred and seventy-three (1,873) respondents, 1017 (54.3%) females and 856 (45.7%) males. Participants were majorly knowledgeable, the majority (31.2%) were in the 41-50 years age group. Surprisingly, about 17% did not know that wearing a face mask could prevent COVID-19. About 25% still met in crowded places, and slightly more than 33% did not wear outdoor masks. The highest knowledge of COVID-19 was found among people in the age range 41-50 years, females, University graduates, married people, and healthcare personnel, particularly doctors.

Conclusion

our study concludes that the overall population of Abuja had good knowledge and, a positive attitude, with pockets of poor attitudes and bad practices born out of misconceptions and infodemics.

Voice Quality and Vocal Tract Discomfort Symptoms in Patients With COVID-19

INTRODUCTION

Dysphonia and laryngeal problems are some of the manifestations of the COVID-19 pandemic due to respiratory disease as a primary effect of COVID-19. The aim of the present study was to investigate voice quality and vocal tract discomfort symptoms in patients with COVID-19.

MATERIALS AND METHODS

Forty-four COVID-19 patients with a mean age of 49.61 ± 16.48 years and 44 healthy subjects with a mean age of 48.52 ± 13.8 years participated in the study. The voice quality of the participants was evaluated using auditory-perceptual evaluation with the Grade, Roughness, Breathiness, Asthenia, and Strain (GRBAS) scale. The vocal tract discomfort symptoms of the participants were assessed using the Persian version of the VTD scale.

RESULTS

Patients with COVID-19 had higher scores in all items of the GRBAS, including grade, roughness, breathiness, asthenia, and strain, than healthy subjects, and these differences were statistically significant (P < 0.05). Among the GRBAS parameters, grade had the highest effect size and asthenia had the lowest effect size in both speech tasks. The COVID-19 patients had a greater frequency of vocal tract discomfort symptoms than healthy subjects in all items of the VTDp scale and these differences were statistically significant (P < 0.05) in the following items: burning, tight, dry, pain, sore, irritable, and lump in the throat. The most and the least effect size in frequency of the vocal tract discomfort symptoms were related to dry (d = 1.502) and tickling (d = 0.157), respectively. Also, COVID-19 patients had more significant severity in all items of the VTDp scale except tight and tickling. The most and the least effect size in severity of the vocal tract discomfort symptoms was related to dry (d = 1.416) and tickling (d = 0.152), respectively.

CONCLUSION

The present study suggests that COVID-19 patients have more deviations in voice quality than healthy subjects. Moreover, mild vocal tract discomfort is prevalent in patients with COVID-19, and patients have more frequent and severe physical discomforts of the vocal tract than healthy subjects.

Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings

BACKGROUND

Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection.

METHODS

Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results.

RESULTS

The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results.

CONCLUSION

These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

Depression, anxiety, lower sleep quality and social support in square cabin hospitals during Shanghai's COVID-19 lockdown, China

Objectives

To investigate and compare the associated factors of depression, anxiety, and other psychological differences between patients with Corona Virus Disease 2019 quarantined in square cabin hospitals (SCH) and isolation wards (IW) in China.

Methods

Cluster sampling method was performed during Shanghai's Two-Month Lockdown in 2022. Hospital Anxiety and Depression Scale Depression subscale (HADS-D), 7-tiem Generalized Anxiety Disorder Scale (GAD-7), Pittsburgh sleep quality index (PSQI), and Perceived Social Support Scale (PSSS) were used to investigate psychological differences.

Results

The HADS-D and GAD-7 scores of SCH patients were significantly higher than those in IW (p < 0.001; p = 0.0295). Sleep latency (SCH-IW = -3.76, p < 0.001), sleep duration (SCH-IW = -2.22, p < 0.05), habitual sleep efficiency (SCH-IW = -4.11, p < 0.001), sleep disturbance (SCH-IW = -3.59, p < 0.001) and use of sleep medication (SCH-IW = -5.18, p < 0.001) of SCH patients were significantly worse. Depression was the main emotional problem of quarantined patients. Patients in SCH had lower social support. Sleep disorders and the lowest oxygen saturation ≤ 93% were risk factors for depression, while social support and child status were protective factors. Myalgia and constipation were risk factors for anxiety, while marital status was the protective factor.

Conclusion

Patients quarantined in SCH had higher risks of depression and anxiety, lower sleep quality and social support. Somatic discomfort and sleep disorders exacerbated depression and anxiety, which could be ameliorated by social support and taken into consideration in future SCH construction.

Antifibrotic Drugs against Idiopathic Pulmonary Fibrosis and Pulmonary Fibrosis Induced by COVID-19: Therapeutic Approaches and Potential Diagnostic Biomarkers

The COVID-19 pandemic has had a significant impact on the health and economy of the global population. Even after recovery from the disease, post-COVID-19 symptoms, such as pulmonary fibrosis, continue to be a concern. This narrative review aims to address pulmonary fibrosis (PF) from various perspectives, including the fibrotic mechanisms involved in idiopathic and COVID-19-induced pulmonary fibrosis. On the other hand, we also discuss the current therapeutic drugs in use, as well as those undergoing clinical or preclinical evaluation. Additionally, this article will address various biomarkers with usefulness for PF prediction, diagnosis, treatment, prognosis, and severity assessment in order to provide better treatment strategies for patients with this disease.

How will Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and lead (Pb) exposure interact in the cardiovascular system?

The combined effects of lead exposure and the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and how they cause cardiovascular dysfunction are examined in this viewpoint manuscript. Lead was picked as a representative environmental contaminant because it has been studied extensively for more than 40 years, while SARS-CoV-2 illustrates the unpredictability of a new infectious agent that can quickly increase the environmental burden on communities. Given that this interaction represents a plausible combined exposure for many people, it is crucial to discuss how it might happen at a mechanistic level. SARS-CoV-2 causes the coronavirus disease 2019 (COVID-19), which can spread by contact and respiratory droplets. One may be exposed to lead and SARS-CoV-2 simultaneously depending on their living situation. Uncertainty exists regarding the consequences of this exposure on cardiovascular disease and its underlying processes. This paper aims to investigate these mechanisms and provide potential causes behind them. The interaction of lead with SARS-CoV-2 and its effects on the cardiovascular system are likely caused by direct damage, the promotion of reactive oxygen species (ROS) production, oxidative stress, and inflammation. In particular, the stimulation of nuclear factor kappa B (NF-B), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF) were all identified as potentially important pathways. Blood clotting, cytokine storm cycle development, and increased hypoxia may also be the results of this. In summary, the effects on the immune system, coagulation, the renin-angiotensin-aldosterone system, and cardiac contractility were the four primary areas identified.

Photodynamic Action of Curcumin and Methylene Blue against Bacteria and SARS-CoV-2-A Review

Coronavirus disease 19 (COVID-19) has occurred for more than four years, and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 is a strain of coronavirus, which presents high rates of morbidity around the world. Up to the present date, there are no therapeutics that can avert this form of illness, and photodynamic therapy (PDT) may be an alternative approach against SARS-CoV-2. Curcumin and methylene blue have been approved and used in clinical practices as a photosensitizer in PDT for a long time with their anti-viral properties and for disinfection through photo-inactivated SARS-CoV-2. Previously, curcumin and methylene blue with antibacterial properties have been used against Gram-positive bacteria, Staphylococcus aureus (S. aureus), and Gram-negative bacteria, Escherichia coli (E. coli), Enterococcus faecalis (E. faecalis), and Pseudomonas aeruginosa (P. aeruginosa).

METHODS

To conduct a literature review, nine electronic databases were researched, such as WanFang Data, PubMed, Science Direct, Scopus, Web of Science, Springer Link, SciFinder, and China National Knowledge Infrastructure (CNKI), without any regard to language constraints. In vitro and in vivo studies were included that evaluated the effect of PDT mediated via curcumin or methylene blue to combat bacteria and SARS-CoV-2. All eligible studies were analyzed and summarized in this review.

RESULTS

Curcumin and methylene blue inhibited the replication of SARS-CoV-2. The reactive oxygen species (ROS) are generated during the treatment of PDT with curcumin and methylene blue to prevent the attachment of SARS-CoV-2 on the ACE2 receptor and damage to the nucleic acids either DNA or RNA. It also modulates pro-inflammatory cytokines and attenuates the clotting effects of the host response.

CONCLUSION

The photodynamic action of curcumin and methylene blue provides a possible approach against bacteria and SARS-CoV-2 infection because they act as non-toxic photosensitizers in PDT with an antibacterial effect, anti-viral properties, and disinfection functions.

Assessment of implicit COVID-19 attitudes using affective priming for pro-vaccine and vaccine-hesitant individuals

The COVID-19 pandemic has resulted in the introduction of pharmaceutical and non-pharmaceutical interventions such as precautionary behaviours. The current study used affective priming to evaluate COVID-19 attitudes in vaccine-hesitant and pro-vaccine participants. Explicitly, both groups rated their overall perception of risk associated with contracting COVID-19 significantly lower compared to their perception of necessary precautions and overall adherence to public health measures. Pro-vaccine participants rated their perception of necessary precautions higher compared to vaccine-hesitant participants. During baseline measures, both groups classified COVID-19 affiliated words as unpleasant. Affective priming was observed for congruent prime-target pleasant and unpleasant word pairs but was not observed for COVID-19 related word pairs. Differences between groups in the perception of necessary public health precautions points to different underlying pathways for reduced perceived risk and lack of affective priming. These results refine previous findings indicating that implicit attitudes towards COVID-19 can be measured using the affective priming paradigm.

Beliefs About the Use of Herbs Before and After COVID-19: A Cross-Sectional Study in Saudi Arabia

BACKGROUND

The COVID-19 pandemic was declared a public health emergency of international concern by the World Health Organization on March 12, 2020. Natural products and herbal medicine have been used since ancient times to relieve and treat disorders and infections, as well as increase immunity. Despite the beneficial effects of herbal medications, there are many side effects or interactions with other medications or foods that might occur.

AIM

This study aims to explore the beliefs of Saudi people towards the use of herbal medicine for COVID-19 infection.

METHOD

A cross-sectional study using an online survey was conducted in Saudi Arabia between January 2021 and January 2023. This survey was generated based on a deep review of the literature on COVID-19 as well as the use of medicine and herbal medicine to treat this infection. SPSS software was used to analyze the data, with a significance level of p < 0.05.

RESULTS

A total of 1,230 individuals participated in this study. More than half (67.6%, n = 831) were females. Around 32.4% (n = 399) of the participants were aged 20-40 years. The majority were married (77.2%, n = 947), Saudi (96.5%, n = 1186), and living in central provinces (62.5%, n = 768) of the Kingdom. More than half of them (70.0%, n = 861) were bachelor's degree holders; 42.3% and 2.4% (n = 29) reported that they had been or were currently infected with COVID-19. Around 33.0% (n = 405) of the participants reported that they had used herbal products or nutritional supplements during the pandemic period to protect themselves from the disease.

CONCLUSION

Our study showed that Saudi Arabia's public takes dietary supplements or herbal products to fight against the illness. We recommend that the Ministry of Health conduct more educational efforts to raise public awareness about disease transmission pathways and preventive actions. Furthermore, to guarantee patient safety, the use of herbal products should be supported by a professional counselor.

Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore

Background

The COVID-19 pandemic, which began in late 2019, has resulted in the devastating collapse of the social economy and more than 10 million deaths worldwide. A recent study suggests that the pattern of COVID-19 cases will resemble a mini-wave rather than a seasonal surge. In general, COVID-19 has more severe impacts on cities than on rural areas, especially in cities with high population density.

Methods

In this study, the background situation of COVID-19 transmission is discussed, including the population number and population density. Moreover, a widely used time series autoregressive integrated moving average (ARIMA) model is applied to simulate and forecast the COVID-19 variations in the six cities. We comprehensively analyze the dynamic variations in COVID-19 in the four first-tier cities of mainland China (BJ: Beijing, SH: Shanghai, GZ: Guangzhou and SZ: Shenzhen), Hong Kong (HK), China and Singapore (SG) from 2020 to 2022.

Results

The major results show that the six cities have their own temporal characteristics, which are determined by the different control and prevention measures. The four first-tier cities of mainland China (i.e., BJ, SH, GZ, and SZ) have similar variations with one wave because of their identical "Dynamic COVID-19 Zero" strategy and strict Non-Pharmaceutical Interventions (NPIs). HK and SG have multiple waves primarily caused by the input cases. The ARIMA model has the ability to provide an accurate forecast of the COVID-19 pandemic trend for the six cities, which could provide a useful approach for predicting the short-term variations in infectious diseases.Accurate forecasting has significant value for implementing reasonable control and prevention measures.

Conclusions

Our main conclusions show that control and prevention measures should be dynamically adjusted and organically integrated for the COVID-19 pandemic. Moreover, the mathematical models are proven again to provide an important scientific basis for disease control.

Measuring the worldwide spread of COVID-19 using a comprehensive modeling method

BACKGROUND

With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models.

METHODS

We collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model.

RESULTS

All 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9-12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10-20% of the countries' populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner.

CONCLUSIONS

We demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management.

Optimal age-specific vaccination control for COVID-19: An Irish case study

The outbreak of a novel coronavirus causing severe acute respiratory syndrome in December 2019 has escalated into a worldwide pandemic. In this work, we propose a compartmental model to describe the dynamics of transmission of infection and use it to obtain the optimal vaccination control. The model accounts for the various stages of the vaccination, and the optimisation is focused on minimising the infections to protect the population and relieve the healthcare system. As a case study, we selected the Republic of Ireland. We use data provided by Ireland's COVID-19 Data-Hub and simulate the evolution of the pandemic with and without the vaccination in place for two different scenarios, one representative of a national lockdown situation and the other indicating looser restrictions in place. One of the main findings of our work is that the optimal approach would involve a vaccination programme where the older population is vaccinated in larger numbers earlier while simultaneously part of the younger population also gets vaccinated to lower the risk of transmission between groups. We compare our simulated results with those of the vaccination policy taken by the Irish government to explore the advantages of our optimisation method. Our comparison suggests that a similar reduction in cases may have been possible even with a reduced set of vaccinations available for use.

Estimating age-stratified transmission and reproduction numbers during the early exponential phase of an epidemic: A case study with COVID-19 data

In a pending pandemic, early knowledge of age-specific disease parameters, e.g., susceptibility, infectivity, and the clinical fraction (the fraction of infections coming to clinical attention), supports targeted public health responses like school closures or sequestration of the elderly. The earlier the knowledge, the more useful it is, so the present article examines an early phase of many epidemics, exponential growth. Using age-stratified COVID-19 case counts collected in Canada, China, Israel, Italy, the Netherlands, and the United Kingdom before April 23, 2020, we present a linear analysis of the exponential phase that attempts to estimate the age-specific disease parameters given above. Some combinations of the parameters can be estimated by requiring that they change smoothly with age. The estimation yielded: (1) the case susceptibility, defined for each age-group as the product of susceptibility to infection and the clinical fraction; (2) the mean number of transmissions of infection per contact within each age-group; and (3) the reproduction number of infection within each age-group, i.e., the diagonal of the age-stratified next-generation matrix. Our restriction to data from the exponential phase indicates the combinations of epidemic parameters that are intrinsically easiest to estimate with early age-stratified case counts. For example, conclusions concerning the age-dependence of case susceptibility appeared more robust than corresponding conclusions about infectivity. Generally, the analysis produced some results consistent with conclusions confirmed much later in the COVID-19 pandemic. Notably, our analysis showed that in some countries, the reproduction number of infection within the half-decade 70-75 was unusually large compared to other half-decades. Our analysis therefore could have anticipated that without countermeasures, COVID-19 would spread rapidly once seeded in homes for the elderly.

Complex network-based classification of radiographic images for COVID-19 diagnosis

In this work, we present a network-based technique for chest X-ray image classification to help the diagnosis and prognosis of patients with COVID-19. From visual inspection, we perceive that healthy and COVID-19 chest radiographic images present different levels of geometric complexity. Therefore, we apply fractal dimension and quadtree as feature extractors to characterize such differences. Moreover, real-world datasets often present complex patterns, which are hardly handled by only the physical features of the data (such as similarity, distance, or distribution). This issue is addressed by complex networks, which are suitable tools for characterizing data patterns and capturing spatial, topological, and functional relationships in data. Specifically, we propose a new approach combining complexity measures and complex networks to provide a modified high-level classification technique to be applied to COVID-19 chest radiographic image classification. The computational results on the Kaggle COVID-19 Radiography Database show that the proposed method can obtain high classification accuracy on X-ray images, being competitive with state-of-the-art classification techniques. Lastly, a set of network measures is evaluated according to their potential in distinguishing the network classes, which resulted in the choice of communicability measure. We expect that the present work will make significant contributions to machine learning at the semantic level and to combat COVID-19.

The effect of the Covid-19 pandemic on Tuberculosis (TB) case notification in Ogun State, Nigeria

Introduction

COVID-19 pandemic has resulted in disruptions in delivery of Tuberculosis services especially, in resource-limited settings. Provisional data by the WHO from 84 countries indicates that about 1.4 million fewer people received care for tuberculosis in 2020 than in 2019. This study assessed the effect of COVID-19 pandemic on tuberculosis case notification rates in Ogun state, Nigeria.

Methods

A retrospective review of presumptive TB and diagnosed TB cases that were notified in 2019 and 2020. Analysis was done using Epi-info version 7.2.3.1. Level of statistical significance was p < 0.05.

Results

A total of 3102 and 3326 confirmed cases were reported in 2019 and 2020 respectively with an increase of 7.2%. There was significant decline in total number of cases notified in Q2, 2020 compared to 2019 (p=0.001) with a significant increase in proportion of TB cases notified by private facilities from 11.65% in 2019 to 20.27% in 2020.

Conclusion

Total TB cases notified in Ogun state increased during the covid-19 pandemic. There was significant decline in TB cases during the lockdown but an increase in proportion of TB cases notified by private facilities demonstrating that private facilities can withstand disruptions to TB case notifications due to the Covid-19 pandemic.

The In Vitro, In Vivo, and PBPK Evaluation of a Novel Lung-Targeted Cardiac-Safe Hydroxychloroquine Inhalation Aerogel

Hydroxychloroquine (HCQ) was repurposed for COVID-19 treatment. Subtherapeutic HCQ lung levels and cardiac toxicity of oral HCQ were overcome by intratracheal (IT) administration of lower HCQ doses. The crosslinker-free supercritical fluid technology (SFT) produces aerogels and impregnates them with drugs in their amorphous form with efficient controlled release. Mechanistic physiologically based pharmacokinetic (PBPK) modeling can predict the lung's epithelial lining fluid (ELF) drug levels. This study aimed to develop a novel HCQ SFT formulation for IT administration to achieve maximal ELF levels and minimal cardiac toxicity. HCQ SFT formulation was prepared and evaluated for physicochemical, in vitro release, pharmacokinetics, and cardiac toxicity. Finally, the rat HCQ ELF concentrations were predicted using PBPK modeling. HCQ was amorphous after loading into the chitosan-alginate nanoporous microparticles (22.7±7.6 μm). The formulation showed a zero-order release, with only 40% released over 30 min compared to 94% for raw HCQ. The formulation had a tapped density of 0.28 g/cm3 and a loading efficiency of 35.3±1.3%. The IT administration of SFT HCQ at 1 mg/kg resulted in 23.7-fold higher bioavailability, fourfold longer MRT, and eightfold faster absorption but lower CK-MB and LDH levels than oral raw HCQ at 4 mg/kg. The PBPK model predicted 6 h of therapeutic ELF levels for IT SFT HCQ and a 100-fold higher ELF-to-heart concentration ratio than oral HCQ. Our findings support the feasibility of lung-targeted and more effective SFT HCQ IT administration for COVID-19 compared to oral HCQ with less cardiac toxicity. Graphical abstract.

A data-driven Markov process for infectious disease transmission

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.

The Relationship between Socio-Demographic Factors, Preventive Health Behaviors and Acceptance of COVID-19 Vaccine among Israeli Pregnant Women during the Coronavirus Pandemic

BACKGROUND

The outbreak of the Coronavirus disease led the World Health Organization to publish recommendations regarding preventive health behaviors (PHB). Pregnant women are at a higher risk of severe COVID-19 infection and adherence to these recommendations is critical. There are little data regarding PHB among pregnant women. The current study aims to evaluate the contribution of socio-demographic factors and COVID-19 vaccinations in predicting PHB among pregnant women.

METHOD

202 pregnant Israeli women (mean age = 30.8 years) participated in an online survey in 2021.

RESULTS

88% of the women were vaccinated and few had been infected. Of the women, 75.2% reported wearing face masks in closed spaces, while 12.4% reported wearing masks outdoors; 63.9% of the women did not travel abroad for fear of infection by the virus and 51% avoided crowded events. A simultaneous regression analysis to predict PHB indicated that pregnancy week and Coronavirus vaccination significantly and positively predicted PHB, but religious status was a negative predictor. Age, number of children, and level of education were not associated with PHB.

CONCLUSIONS

These findings can be helpful as a preliminary evidence base for policy-making at present and for future epidemics regarding guidelines on PHB adjusted for pregnant women.

Recent Advances in Quantum Dot-Based Lateral Flow Immunoassays for the Rapid, Point-of-Care Diagnosis of COVID-19

The COVID-19 pandemic has spurred demand for efficient and rapid diagnostic tools that can be deployed at point of care to quickly identify infected individuals. Existing detection methods are time consuming and they lack sensitivity. Point-of-care testing (POCT) has emerged as a promising alternative due to its user-friendliness, rapidity, and high specificity and sensitivity. Such tests can be conveniently conducted at the patient's bedside. Immunodiagnostic methods that offer the rapid identification of positive cases are urgently required. Quantum dots (QDs), known for their multimodal properties, have shown potential in terms of combating or inhibiting the COVID-19 virus. When coupled with specific antibodies, QDs enable the highly sensitive detection of viral antigens in patient samples. Conventional lateral flow immunoassays (LFAs) have been widely used for diagnostic testing due to their simplicity, low cost, and portability. However, they often lack the sensitivity required to accurately detect low viral loads. Quantum dot (QD)-based lateral flow immunoassays have emerged as a promising alternative, offering significant advancements in sensitivity and specificity. Moreover, the lateral flow immunoassay (LFIA) method, which fulfils POCT standards, has gained popularity in diagnosing COVID-19. This review focuses on recent advancements in QD-based LFIA for rapid POCT COVID-19 diagnosis. Strategies to enhance sensitivity using QDs are explored, and the underlying principles of LFIA are elucidated. The benefits of using the QD-based LFIA as a POCT method are highlighted, and its published performance in COVID-19 diagnostics is examined. Overall, the integration of quantum dots with LFIA holds immense promise in terms of revolutionizing COVID-19 detection, treatment, and prevention, offering a convenient and effective approach to combat the pandemic.

A nonlinear mathematical model on the Covid-19 transmission pattern among diabetic and non-diabetic population

In this paper, a three tier mathematical model describing the interactions between susceptible population, Covid-19 infected, diabetic population and Covid-19 infected, non diabetic population is proposed. Basic properties of such a dynamic model, namely, non negativity, boundedness of solutions, existence of disease-free and disease equilibria are studied and sufficient conditions are obtained. Basic reproduction number for the system is derived. Sufficient conditions on functionals and parameters of the system are obtained for the local as well as global stability of equilibria, thus, establishing the conditions for eventual prevalence of disease free or disease environment, as the case may be. The stability aspects are discussed in the context of basic reproduction number and vice versa. An important contribution of this article is that a novel technique is presented to estimate some key, influencing parameters of the system so that a pre-specified, assumed equilibrium state is approached eventually. This enables the society to prepare itself with the help of these key, influencing parameters so estimated. Several examples are provided to illustrate the results established and simulations are provided to visualize the examples.

Identify hidden spreaders of pandemic over contact tracing networks

The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Due to the continuous emergence of new virus variants, even if vaccines have been widely used, the detection of asymptomatic infected persons is still important in the epidemic control. Based on the unique characteristics of COVID-19 spreading dynamics, here we propose a theoretical framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy, even with incomplete information of the contract-tracing networks. Furthermore, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading.

Drawing Parallels between SARS, MERS, and COVID-19: A Comparative Overview of Epidemiology, Pathogenesis, and Pathological Features

Background

Since November 2019, when the novel coronavirus arose in Wuhan City, over 188 million people worldwide have been infected with COVID-19. It is the third coronavirus outbreak in the twenty-first century. Until now, practically all coronavirus epidemics have occurred due to zoonotic spread from an animal or transitional host or through the consumption of their products. Coronaviruses can infect humans and cause severe illness and even death.

Material and Methods

This review was designed to help us recognize and harmonize the similarities and differences between these three coronaviridae family members.

Result

Measures aimed at containing the epidemic should be emphasized in this circumstance. Prioritizing and planning these activities require an understanding of the particulars of these three viruses. Given the pandemic's enormous death toll and rapid spread, we should be cognizant of the parallels and differences between these three viruses. Additionally, this pandemic warns us to be cautious against the possibility of a future pandemic.

Conclusion

We highlight the fundamental characteristics of coronaviruses that are critical for recognizing coronavirus epidemiology, pathogenesis, and pathological features that reveal numerous significant pathological attributes and evolutionary patterns in the viral genome that aid in better understanding and anticipating future epidemics.

One Pandemic, Two Solutions: Comparing the U.S.-China Response and Health Priorities to COVID-19 from the Perspective of "Two Types of Control"

After three years of global rampage, the COVID-19 epidemic, the most serious infectious disease to occur worldwide since the 1918 influenza pandemic, is nearing its end. From the global experience, medical control and social control are the two main dimensions in the prevention and control of COVID-19. From the perspective of "two types of control", namely medical control and social control, this paper finds that the political system, economic structure, and cultural values of the United States greatly limit the government's ability to impose social control, forcing it to adopt medical control to fight the virus in a single dimension. In contrast, China's political system, economic structure, and cultural values allow its government to adopt stringent, extensive, and frequent social control, as well as medical control to fight the virus. This approach departs from the traditional pathway of fighting the epidemic, i.e., "infection-treatment-immunization", thereby outpacing the evolution of the virus and controlling its spread more rapidly. This finding helps explain why the Chinese government adopted a strict "zeroing" and "dynamic zeroing" policy during the first three years, at the cost of enormous economic, social, and even political legitimacy. It was not until late 2022, when the Omicron variant with the waning virulence became prevalent, that China chose to "coexist" with the virus, thus avoiding a massive epidemic-related death. While the United States adopted a pulsed-style strategy at the beginning of the epidemic, i.e., "relaxation-suppression-relaxation-suppression", and began to "coexist" with the virus in just one year, resulting in a large number of excess deaths associated with the epidemic. The study contributes to explaining the difference in the interplay between public health priorities and COVID-19 response strategies in China and the United States, based on the specific public health context and the perspective of "medical control" and "social control".

Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis

Background

On September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot.

Methods

In this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis.

Results

Of the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30-59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population.

Conclusion

Our model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus.

Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model

In this paper, we develop a new mathematical model for an in-depth understanding of COVID-19 (Omicron variant). The mathematical study of an omicron variant of the corona virus is discussed. In this new Omicron model, we used idea of dividing infected compartment further into more classes i.e asymptomatic, symptomatic and Omicron infected compartment. Model is asymptotically locally stable whenever R0<1 and when R0≤1 at disease free equilibrium the system is globally asymptotically stable. Local stability is investigated with Jacobian matrix and with Lyapunov function global stability is analyzed. Moreover basic reduction number is calculated through next generation matrix and numerical analysis will be used to verify the model with real data. We consider also the this model under fractional order derivative. We use Grunwald-Letnikov concept to establish a numerical scheme. We use nonstandard finite difference (NSFD) scheme to simulate the results. Graphical presentations are given corresponding to classical and fractional order derivative. According to our graphical results for the model with numerical parameters, the population's risk of infection can be reduced by adhering to the WHO's suggestions, which include keeping social distances, wearing facemasks, washing one's hands, avoiding crowds, etc.

Understanding the challenges to COVID-19 vaccines and treatment options, herd immunity and probability of reinfection

Unlike pandemics in the past, the outbreak of coronavirus disease 2019 (COVID-19), which rapidly spread worldwide, was met with a different approach to control and measures implemented across affected countries. The lack of understanding of the fundamental nature of the outbreak continues to make COVID-19 challenging to manage for both healthcare practitioners and the scientific community. Challenges to vaccine development and evaluation, current therapeutic options, convalescent plasma therapy, herd immunity, and the emergence of reinfection and new variants remain the major obstacles to combating COVID-19. This review discusses these challenges in the management of COVID-19 at length and highlights the mechanisms needed to provide better understanding of this pandemic.

Engineering Extracellular Vesicles as Delivery Systems in Therapeutic Applications

Extracellular vesicles (EVs) are transport vesicles secreted by living cells and released into the extracellular environment. Recent studies have shown that EVs serve as "messengers" in intercellular and inter-organismal communication, in both normal and pathological processes. EVs, as natural nanocarriers, can deliver bioactivators in therapy with their endogenous transport properties. This review article describes the engineering EVs of sources, isolation method, cargo loading, boosting approach, and adjustable targeting of EVs. Furthermore, the review summarizes the recent progress made in EV-based delivery systems applications, including cancer, cardiovascular diseases, liver, kidney, nervous system diseases, and COVID-19 and emphasizes the obstacles and challenges of EV-based therapies and possible strategies.

A point-of-care biosensor for rapid detection and differentiation of COVID-19 virus (SARS-CoV-2) and influenza virus using subwavelength grating micro-ring resonator

In the context of continued spread of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and the emergence of new variants, the demand for rapid, accurate, and frequent detection is increasing. Moreover, the new predominant strain, Omicron variant, manifests more similar clinical features to those of other common respiratory infections. The concurrent detection of multiple potential pathogens helps distinguish SARS-CoV-2 infection from other diseases with overlapping symptoms, which is significant for providing tailored treatment to patients and containing the outbreak. Here, we report a lab-on-a-chip biosensing platform for SARS-CoV-2 detection based on the subwavelength grating micro-ring resonator. The sensing surface is functionalized by specific antibody against SARS-CoV-2 spike protein, which could produce redshifts of resonant peaks by antigen-antibody combination, thus achieving quantitative detection. Additionally, the sensor chip is integrated with a microfluidic chip featuring an anti-backflow Y-shaped structure that enables the concurrent detection of two analytes. In this study, we realized the detection and differentiation of COVID-19 and influenza A H1N1. Experimental results indicate that the limit of detection of our device reaches 100 fg/ml (1.31 fM) within 15 min detecting time, and cross-reactivity tests manifest the specificity of the optical diagnostic assay. Furthermore, the integrated packaging and streamlined workflow facilitate its use for clinical applications. Thus, the biosensing platform presents a promising approach for attaining highly sensitive, selective, multiplexed, and quantitative point-of-care diagnosis and distinction between COVID-19 and influenza.

Dynamic optimal allocation of medical resources: a case study of face masks during the first COVID-19 epidemic wave in the United States

In this paper, we propose a two-group SIR epidemic model to simulate the outcome of the stay-at-home policy and the imposed face mask policy during the first COVID-19 epidemic wave in the United States. Then, we use a dynamic optimal control approach (with the objective of minimizing total deaths) to find the optimal dynamical distribution of face masks between healthcare workers and the general public. It is not surprising that all face masks should be solely reserved for healthcare workers if the supply is short. However, when the supply is indeed sufficient, our numerical study indicates that the general public should share a large portion of face masks at the beginning of the epidemic wave to dramatically reduce the death toll. This interesting result partially contradicts the guideline advised by the US Surgeon General and the Centers for Disease Control and Prevention (CDC) in March 2020. The optimality of this sounding CDC guideline highly depends on the supply level of face masks, which changes frequently; hence, it should be adjusted according to the supply of face masks.

Effective Drug Candidates against Global Pandemic of Novel Corona Virus (nCoV-2019): A Probability Check through Computational Approach for Public Health Emergency

The infection of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started form Wuhan, Chinais a devastating and the incidence rate has increased worldwide. Due to the lack of effective treatment against SARS-CoV-2, various strategies are being tested in China and throughout the world, including drug repurposing. To identify the potent clinical antiretroviral drug candidate against pandemic nCov-19 through computational tools. In this study, we used molecular modelling tool (molecular modelling and molecular dynamics) to identify commercially available drugs that could act on protease proteins of SARS-CoV-2. The result showed that Saquinavir, an antiretroviral medication can be used as a first line agent to treat SARS-CoV-2 infection. Saquinavir showed promising binding to the protease active site compared to other possible antiviral agents such as Nelfinavir and Lopinavir. Structural flexibility is one of the important physical properties that affect protein conformation and function and taking this account we performed molecular dynamics studies. Molecular dynamics studies and free energy calculations suggest that Saquinavir binds better to the COVID-19 protease compared to other known antiretrovirals. Our studies clearly propose repurposing of known protease inhibitors for the treatment of COVID-19 infection. Previously ritonavir and lopinavir were proved an important analogues for SARS and MERS in supressing these viruses. In this study it was found that saquinavir has exhibited good G-score and E-model score compared to other analogues. So saquinavir would be prescribe to cure for nCov-2019 either single drug or maybe in combination with ritonavir.

Relationship Between Vocal Fatigue Index and Acoustic Voice Scales in Patients With Coronavirus Infection

OBJECTIVES

The voice quality of patients with Coronavirus Disease 2019 (COVID-19) seems to be affected due to lower and upper respiratory involvement. Patient-based voice assessment scales are important clinical measures to diagnose voice disorders and monitor treatment outcomes in COVID-19 patients. This study compared vocal fatigue between COVID-19 patients and those with normal voices. Furthermore, the relationship between vocal fatigue and acoustic voice parameters of COVID-19 patients was evaluated.

METHODS

This cross-sectional study enrolled 30 laboratory-confirmed patients with COVID-19 (18 males and 12 females) and 30 healthy individuals with normal voices (14 males and 16 females) to compare their respiratory or phonatory parameters. The Persian versions of the Consensus Auditory Perceptual Evaluation of Voice (CAPE-V) and the vocal fatigue index (VFI) were conducted before and after reading the text. The Jitter, shimmer, maximum phonation time, and harmonic-to-noise ratio (HNR) were analyzed by Praat software based on the recorded voices of CAPE-V tasks. The acoustic assessment and VFI questionnaire results were compared between COVID-19 patients and the control group.

RESULTS

There were significant differences between COVID-19 patients and their healthy counterparts in all VFI subscales (P < 0.001). Moreover, after reading the text, we found significant differences between the two groups regarding Jitter, shimmer, and HNR of /a/ and /i/ vowels (P < 0.05). Our findings also indicated a significant correlation between symptom improvement with rest and acoustic parameters in all tasks, except the Jitter of /a/ before reading the text.

CONCLUSION

Patients with COVID-19 showed significantly more vocal fatigue than people with normal voices after reading the text. Moreover, there was a significant relationship between Jitter, shimmer, and HNR and the tiredness of voice and physical discomfort subscales of VFI.

Comparative Study of the Myocardium of Patients from Four COVID-19 Waves

BACKGROUND

Few studies have compared COVID-19 patients from different waves. This study aims to conduct a clinical and morphological analysis of patients who died from COVID-19 during four waves.

METHODS

The study involved 276 patients who died from COVID-19 during four waves, including 77 patients in the first wave, 119 patients in the second wave, and 78 patients in the third wave. We performed a histological examination of myocardium samples from autopsies and additionally analyzed the samples by PCR. We conducted immunohistochemistry of the myocardium for 21 samples using antibodies against CD3, CD45, CD8, CD68, CD34, Ang1, VWF, VEGF, HLA-DR, MHC1, C1q, enteroviral VP1, and SARS-CoV-2 spike protein. We also did immunofluorescent staining of three myocardial specimens using VP1/SARS-CoV-2 antibody cocktails. Further, we ran RT-ddPCR analysis for 14 RNA samples extracted from paraffin-embedded myocardium. Electron microscopic studies of the myocardium were also performed for two samples from the fourth wave.

RESULTS

Among the 276 cases, active myocarditis was diagnosed in 5% (15/276). Of these cases, 86% of samples expressed VP1, and individual cells contained SARS-CoV-2 spike protein in 22%. Immunofluorescence confirmed the co-localization of VP1 and SARS-CoV-2 spike proteins. ddPCR did not confidently detect SARS-CoV-2 RNA in the myocardium in any myocarditis cases. However, the myocardium sample from wave IV detected a sub-threshold signal of SARS-CoV-2 by qPCR, but myocarditis in this patient was not confirmed. Electron microscopy showed several single particles similar to SARS-CoV-2 virions on the surface of the endothelium of myocardial vessels. A comparison of the cardiovascular complication incidence between three waves revealed that the incidence of hemorrhage (48 vs. 24 vs. 17%), myocardial necrosis (18 vs. 11 vs. 4%), blood clots in the intramural arteries (12 vs. 7 vs. 0%), and myocarditis (19 vs. 1 vs. 6%) decreased over time, and CD8-T-killers appeared. Immunohistochemistry confirmed the presence of endotheliitis in all 21 studied cases.

CONCLUSIONS

This study compared myocardial damage in patients who died during three COVID-19 waves and showed a decrease in the incidence of endotheliitis complications (thrombosis, hemorrhage, necrosis) and myocarditis over time. However, the connection between myocarditis and SARS-CoV-2 infection remains unproven.

The impact of corona populism: Empirical evidence from Austria and theory

I study the co-evolution between public opinion and party policy in situations of crises by investigating a policy U-turn of a major Austrian right-wing party (FPÖ) during the Covid-19 pandemic. My analysis suggests the existence of both i) a "Downsian" effect, which causes voters to adapt their party preferences based on policy congruence and ii) a "party identification" effect, which causes partisans to realign their policy preferences based on "their" party's platform. Specifically, I use individual-level panel data to show that i) "corona skeptical" voters who did not vote for the FPÖ in the pre-Covid-19 elections of 2019 were more likely to vote for the party after it embraced "corona populism", and ii) beliefs of respondents who declared that they voted for the FPÖ in 2019 diverged from the rest of the population in three out of four health-related dimensions only after the turn, causing them to underestimate the threat posed by Covid-19 compared to the rest of the population. Using aggregate-level panel data, I study whether the turn has produced significant behavioral differences which could be observed in terms of reported cases and deaths per capita. Paradoxically, after the turn the FPÖ vote share is significantly positively correlated with deaths per capita, but not with the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing, which causes a correlation between the number of "corona skeptics" and the share of unreported cases after the turn. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study that involves information about participants' true vs. reported infection status. I finally study a simple heterogeneous mixing epidemiological model and show that a testing bias can indeed explain the apparent paradox of an increase in deaths without an increase in reported cases. My results can, among others, be used to enrich formal analyses regarding the co-evolution between voter and party behavior.