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

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. Subject Classification; Primary 92B05, Secondary 62P10.

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.

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.

Automatic Identification of Lung Opacities Due to COVID-19 from Chest X-ray Images-Focussing Attention on the Lungs

Due to the primary affection of the respiratory system, COVID-19 leaves traces that are visible in plain chest X-ray images. This is why this imaging technique is typically used in the clinic for an initial evaluation of the patient's degree of affection. However, individually studying every patient's radiograph is time-consuming and requires highly skilled personnel. This is why automatic decision support systems capable of identifying those lesions due to COVID-19 are of practical interest, not only for alleviating the workload in the clinic environment but also for potentially detecting non-evident lung lesions. This article proposes an alternative approach to identify lung lesions associated with COVID-19 from plain chest X-ray images using deep learning techniques. The novelty of the method is based on an alternative pre-processing of the images that focuses attention on a certain region of interest by cropping the original image to the area of the lungs. The process simplifies training by removing irrelevant information, improving model precision, and making the decision more understandable. Using the FISABIO-RSNA COVID-19 Detection open data set, results report that the opacities due to COVID-19 can be detected with a Mean Average Precision with an IoU > 0.5 (mAP@50) of 0.59 following a semi-supervised training procedure and an ensemble of two architectures: RetinaNet and Cascade R-CNN. The results also suggest that cropping to the rectangular area occupied by the lungs improves the detection of existing lesions. A main methodological conclusion is also presented, suggesting the need to resize the available bounding boxes used to delineate the opacities. This process removes inaccuracies during the labelling procedure, leading to more accurate results. This procedure can be easily performed automatically after the cropping stage.

The effect of suture techniques on the outcome of tracheal reconstruction: An observational study and review of literature

BACKGROUND

Tracheal resection and anastomosis surgery is a safe operation and is used to treat various benign and malignant diseases of the trachea. However, tracheal stenosis is among the main anastomotic complications following this procedure. Surgeons use both the continuous and the interrupted suture techniques for tracheal anastomosis, but contradicting results in each technique's complications have been reported in various studies. In this study, we aimed to compare the outcome of these two different suture techniques and a relevant literature review.

METHODS

Surgical records during a period of 15 years (2005-2019) were screened for tracheal reconstruction surgery in affiliated hospitals of Shiraz University of Medical Sciences, Shiraz, Iran. A total of 82 patients were evaluated based on surgical and suture features, along with their follow-up bronchoscopy for anastomotic complications.

RESULTS

Post-operational subclinical restenosis occurred in 8 (15.3%) out of 52 and 10 (33.3%) of 30 patients who underwent continuous and interrupted suturing techniques, respectively. Also, 6 (20%) patients in the interrupted group developed symptomatic restenosis, while in the continuous group, only one patient was clinically symptomatic. The patients with continuous suture technique had a shorter surgery time than patients whose interrupted technique was used (P < 0.001).

CONCLUSIONS

Based on our results, we conclude that restenosis is significantly reduced when the continuous technique is applied for tracheal anastomosis; However, the results are contradicting in relevant literature and due to the retrospective nature of our study, further human studies and clinical trials are warranted.

Clinical Characterization and Outcomes of Patients with Hypercreatinemia Affected by COVID-19

The present study evaluated the clinical presentation and outcome of COVID-19 patients with underlying hypercreatinemia at the time of hospitalization. A retrospective observational study was conducted from the 23rd of March 2020 to the 15th of April 2021 in 1668 patients confirmed positive for COVID-19 in the Chest Disease Hospital in Srinagar, India. The results of the present study revealed that out of 1668 patients, 339 with hypercreatinemia had significantly higher rates of admission to the intensive care unit (ICU), severe manifestations of the disease, need for mechanical ventilation, and all-cause mortality. Multivariable analysis revealed that age, elevated creatinine concentrations, IL-1, D-Dimer, and Hs-Crp were independent risk factors for in-hospital mortality. After adjusted analysis, the association of creatinine levels remained strongly predictive of all-cause, in-hospital mortality (HR-5.34; CI-4.89-8.17; p ≤ 0.001). The amelioration of kidney function may be an effective method for achieving creatinemic targets and, henceforth, might be beneficial for improving outcomes in patients with COVID-19.

A study of COVID anxiety, spiritual well-being and resilience levels in patients with cancer undergoing chemotherapy during the COVID-19 pandemic: a cross-sectional study in the south of Iran

BACKGROUND

Patients with cancer are at higher risk of contracting COVID-19 with poor prognosis. Therefore, the present study was conducted to investigate anxiety, spiritual well-being, and resilience levels in patients with cancer undergoing chemotherapy during the COVID-19 pandemic in the south of Fars Province, Iran.

METHODS

This is a descriptive study with a cross-sectional design conducted on the patients undergoing chemotherapy at Dr. Ali Shariati Hospital in Fasa from November 2021 to February 2022. Cancer patients undergoing chemotherapy were included in the study by census method. Out of 210 patients, 155 participated in the study. Data were collected electronically using the standard instruments of Ellison's Spiritual Well-being Scale, COVID-19 anxiety questionnaire, and Connor-Davidson resilience scale. The collected data were analyzed in SPSS 22 using descriptive statistics, Pearson correlation tests, T-test, ANOVA and multivariate linear regression at a level of significance of P < 0.05.

RESULTS

The participants' resilience mean score was 46.35 ± 26.51 and their spiritual well-being mean score was 69.58 ± 9.32. In addition, their COVID anxiety mean, score was found to be 16.85 ± 10.51. The results showed a significant direct correlation between the patients' spiritual well-being and resilience (r = 0.47, P < 0.001) and a significant inverse correlation between the patients' spiritual well-being and COVID-19-related anxiety (r = - 0.275, P < 0.001). In addition, there was a significant inverse correlation between the variables of resilience and COVID-19-related anxiety (r = - 0.637, P < 0.001). Based on multivariate linear regression, the most common predictors in resilience were age and history of infection with COVID-19, and in spiritual health and anxiety, was a history of infection with COVID-19.

CONCLUSION

Enhancement of spiritual well-being and resilience in patients should be an integral part of care as these qualities are valuable resources in fighting cancer and lowering patients' anxiety, especially during the COVID-19 pandemic.

Using dashboards to verify coronavirus (COVID-19) vaccinations can reduce fatality rates in countries/regions: Development and usability study

BACKGROUND

The new coronavirus disease 2019 (COVID-19) pandemic is raging worldwide. The administered vaccination has become a significant vehicle against the virus. Three hypotheses were made and required for validation: the number of vaccines administered is related to the country gross domestic product (GDP), vaccines can reduce the fatality rate (FR), and dashboards can present more meaningful information than traditionally static visualizations. Research data were downloaded from the GitHub website. The aims of this study are to verify that the number of vaccination uptakes is related to the country GDP, that vaccines can reduce FR, and that dashboards can provide more meaningful information than traditionally static visualizations.

METHODS

The COVID-19 cumulative number of confirmed cases (CNCCs) and deaths were downloaded from the GitHub website for countries/regions on November 6, 2021. Four variables between January 1, 2021, and November 6, 2021, were collected, including CNCCs and deaths, GDP per capita, and vaccine doses administered per 100 people (VD100) in countries/regions. We applied the Kano model, forest plot, and choropleth map to demonstrate and verify the 3 hypotheses using correlation coefficients (CC) between vaccination and FRs. Dashboards used to display the vaccination effects were on Google Maps.

RESULTS

We observed that the higher the GDP, the more vaccines are administered (Association = 0.68, t = 13.14, P < .001) in countries, the FR can be reduced by administering vaccinations that are proven except for the 4 groups of Asia, Low income, Lower middle income, and South America, as well as the application (app) with dashboard-type choropleth map can be used to show the comparison of vaccination rates for countries/regions using line charts.

CONCLUSION

This research uses the Kano map, forest plot, and choropleth map to verify the 3 hypotheses and provides insights into the vaccination effect against the FR for relevant epidemic studies in the future.

Optimal timing of non-pharmaceutical interventions during an epidemic

In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.

Compartmental mathematical modelling of dynamic transmission of COVID-19 in Rwanda

OBJECTIVES

Mathematical modelling is of interest to study the dynamics of coronavirus disease 2019 (COVID-19), and models such as SEIR (Susceptible-Exposed-Infected-Recovered) have been considered. This article describes the development of a compartmental transmission network model - Susceptible-Exposed-Quarantine-Infectious-Infectious, undetected-Infectious, home-based care-Hospitalized-Vaccinated-Recovered-Dead - to simulate the dynamics of COVID-19 in order to account for specific measures put into place by the Government of Rwanda to prevent further spread of the disease.

METHODS

The compartments of this model are connected by parameters, some of which are known from the literature, and others are estimated from available data using the least squares method. For the stability of the model, equilibrium points were determined and the basic reproduction number R 0 was studied; R ₀ is an indicator for contagiousness.

RESULTS

The model showed that secondary infections are generated from the exposed group, the asymptomatic group, the infected (symptomatic) group, the infected (undetected) group, the infected (home-based care) group and the hospitalized group. The formulated model was reliable and fit the data. Furthermore, the estimated R 0 of 2.16 shows that COVID-19 will persist without the application of control measures.

CONCLUSIONS

This article presents results regarding predicted spread of COVID-19 in Rwanda.

The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling

OBJECTIVES

Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations.

METHODS

The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated.

RESULTS

Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization.

CONCLUSIONS

Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

A statistical framework for tracking the time-varying superspreading potential of COVID-19 epidemic

Timely detection of an evolving event of an infectious disease with superspreading potential is imperative for territory-wide disease control as well as preventing future outbreaks. While the reproduction number (R) is a commonly-adopted metric for disease transmissibility, the transmission heterogeneity quantified by dispersion parameter k, a metric for superspreading potential is seldom tracked. In this study, we developed an estimation framework to track the time-varying risk of superspreading events (SSEs) and demonstrated the method using the three epidemic waves of COVID-19 in Hong Kong. Epidemiological contact tracing data of the confirmed COVID-19 cases from 23 January 2020 to 30 September 2021 were obtained. By applying branching process models, we jointly estimated the time-varying R and k. Individual-based outbreak simulations were conducted to compare the time-varying assessment of the superspreading potential with the typical non-time-varying estimate of k over a period of time. We found that the COVID-19 transmission in Hong Kong exhibited substantial superspreading during the initial phase of the epidemics, with only 1 % (95 % Credible interval [CrI]: 0.6-2 %), 5 % (95 % CrI: 3-7 %) and 10 % (95 % CrI: 8-14 %) of the most infectious cases generated 80 % of all transmission for the first, second and third epidemic waves, respectively. After implementing local public health interventions, R estimates dropped gradually and k estimates increased thereby reducing the risk of SSEs to approaching zero. Outbreak simulations indicated that the non-time-varying estimate of k may overlook the possibility of large outbreaks. Hence, an estimation of the time-varying k as a compliment of R as a monitoring of both disease transmissibility and superspreading potential, particularly when public health interventions were relaxed is crucial for minimizing the risk of future outbreaks.

The use of the nutritional supplements during the covid-19 outbreak in Saudi Arabia: A cross-sectional study

BACKGROUND

COVID-19 causes moderate to severe illness and is spreading globally. During a pandemic, vitamins and minerals are vital to health. Therefore, the prevalence and epidemiology of supplement use in Saudi Arabia during the COVID-19 pandemic must be known.

METHODS

This cross-sectional study was conducted in Saudi Arabia using an online survey. The study was conducted from June to March 2022 on both adults and children. The link to the survey was shared on social media platforms. The survey included questions on participants' demographics, vaccination status, supplements they used, and side effects of supplements. Participation in this study was optional, and there was no obligation to participate. There was a declaration about the aim of the study and different objectives before starting the survey.

RESULTS

The present study reported that most of the participants reported that they used vitamin C (64.6 %), zinc (51.9 %), multivitamins (46.1 %), black seeds (26.7 %), garlic (Allium sativum) (15.4 %), omega-3 (22.1 %), vitamin D (22.2 %), echinacea (10.1 %), manuka honey (26.0 %), curcumin (13.6 %), ginger (22.5 %), royal jelly (12.9 %), and propolis (7.5 %) before and during the COVID-19 pandemic period. These supplements were used more frequently by subjects during the pandemic than before.

DISCUSSION AND CONCLUSION

The respondents' risk of these supplements' use may partially reflect the public's behavioral response during a pandemic. Future studies can document the health beliefs and motivations of nutritional supplement users.

The use of medicinal plants in the prevention of COVID-19 using the Health Belief Model: A survey based on the Iranian population

BACKGROUND

Because of the spread of coronavirus disease 2019 (COVID-19), the preventive measures have increased, such as focusing on the use of medicinal plants in most communities, including Iran. The purpose of this study was to identify the knowledge, attitude, and performance of individuals toward the use of medicinal plants and to identify the predictors of the use of medicinal plants in the prevention of COVID-19.

MATERIALS AND METHODS

This descriptive-analytical study (February-April 2021) was performed on 3840 Iranian men and women aged 20-70 years selected as a multi-stage cluster study. At the first stage, all provinces were divided into five regions: North, South, East, West, and Center. In the second stage, a provincial center and a city were randomly selected from each region (North: Sari, Babol; South: Bushehr, Bandar Genaveh; East: Mashhad, Sabzevar; West: Hamedan, Toisarkan; Center: Yazd, Ardakan). Data were collected by a researcher-made scale based on the Health Belief Model (HBM). Data analysis was performed applying Pearson correlation coefficient, logistic regression, and linear regression.

RESULTS

The results showed that people have relatively high knowledge and positive attitude toward the use of medicinal plants in prevention of COVID-19. The most important reason for positive attitude was the perceived benefits with the mean of 75.06%. Also, half of the people had poor performance. Correlation coefficient showed that the use of medicinal plants with perceived sensitivity (p = 0.000, r = 0.3), perceived benefits (p = 0.012, r = 0.126), perceived barriers (p = 0.000, r = 0.179), and perceived self-efficacy (p = 0.000, r = 0.305) had a significant correlation. The strongest correlation between perceived self-efficacy was observed with the use of herbs in prevention of COVID-19. The HBM constructs can predict 26% of the variance for the use of medicinal plants in the prevention of COVID-19, among which perceived self-efficacy (β = 0.230) was the most powerful predictor.

CONCLUSION

Based on the results, the predictive role of self-efficacy constructs for the use of medicinal plants in prevention of COVID-19 has been confirmed according to the HBM. Therefore, methods of increasing self-efficacy such as training programs and providing appropriate intervention models can be used not only as promoters of using medicinal plants in prevention of COVID-19 but also for improving people's performance in the proper use of medicinal plants.

The reproduction rate of severe acute respiratory syndrome coronavirus 2 different variants recently circulated in human: a narrative review

SARS-COV-2 is responsible for the current worldwide pandemic, which started on December 2019 in Wuhan, China. On March 2020 World Health Organization announced COVID-19 as the new pandemic. Some SARS-COV-2 variants have increased transmissibility, cause more severe disease (e.g., increased hospitalizations or deaths), are resistant to antibodies produced by the previous infection or vaccination, and there is more difficulty in treatment and diagnosis of them. World Health Organization considered them as SARS-CoV-2 variants of concern. The introductory reproduction rate (R₀) is an epidemiologic index of the transmissibility of the virus, defined as the average number of persons infected by the virus after known contact with an infectious person in a susceptible population. An R₀ > 1 means that the virus is spreading exponentially, and R₀ < 1, means that the outbreak is subsiding. In various studies, the estimated R and VOC growth rates were reported to be greater than the ancestral strains. However, it was also a low level of concordance between the estimated Rt of the same variant in different studies. It is because the R of a variant not only dependent on the biological and intrinsic factors of the virus but also several parameters can affect the R0, including the duration of contagiousness and the likelihood of infection per contact. Evaluation of changes in SARS-CoV-2 has shown that the rate of human-to-human transmission of this virus has increased. Like other viruses with non-human sources which succeeded in surviving in the human population, SARS-CoV-2 has gradually adapted to the human population, and its ability to transmit from human to human has increased. Of course, due to the continuous changes in this virus, it is crucial to survey the rate of transmission of the virus over time.

Generalized Gamma-CUSUM control chart with application of COVID-19 deaths

The increase in the number of infections and the worrisome state of mortality linked to the COVID-19 pandemic demand an optimal statistical model and efficient monitoring scheme to analyze the deaths. This paper aims to model the COVID-19 mortality in Nigeria using four non-normal distributions grouped under the generalized gamma distribution, by specifying the best-fit distribution to model the number of deaths linked to the COVID-19 pandemic. In addition, a control chart to monitor the COVID-19 deaths based on the best-fit distribution is proposed. The performance of the proposed Gamma-CUSUM chart as a monitoring scheme was compared with the standard normal-CUSUM chart. The results revealed that the Gamma-CUSUM chart first signals a change in the number of deaths on day 68 while there was no change in the number of deaths for the standard normal-CUSUM chart. Also, the exact point of change was visible on the Gamma-CUSUM chart which was impossible on a standard normal-CUSUM control chart.

Selenium in the Prevention of SARS-CoV-2 and Other Viruses

The rapid spread of new pathogens (SARS-CoV-2 virus) that negatively affect the human body has huge consequences for the global public health system and the development of the global economy. Appropriate implementation of new safety regulations will improve the functioning of the current model supervising the inhibition of the spread of COVID-19 disease. Compliance with all these standards will have a key impact on the health behavior of individual social groups. There have been demonstrably effective treatments that proved to be effective but were rapidly dismissed for unknown reasons, such as ivermectin and hydroxychloroquine. Various measures are used in the world to help inhibit its development. The properties of this element provide hope in preventing the development of the SARS-CoV-2 virus. The aim of this review is to synthesize the latest literature data and to present the effect of sodium selenite in reducing the incidence of COVID-19 disease.

Optimal control of the coronavirus pandemic with both pharmaceutical and non-pharmaceutical interventions

Coronaviruses are types of viruses that are widely spread in humans, birds, and other mammals, leading to hepatic, respiratory, neurologic, and enteric diseases. The disease is presently a pandemic with great medical, economical, and political impacts, and it is mostly spread through physical contact. To extinct the virus, keeping physical distance and taking vaccine are key. In this study, a dynamical transmission compartment model for coronavirus (COVID-19) is designed and rigorously analyzed using Routh-Hurwitz condition for the stability analysis. A global dynamics of mathematical formulation was investigated with the help of a constructed Lyapunov function. We further examined parameter sensitivities (local and global) to identify terms with greater impact or influence on the dynamics of the disease. Our approach is data driven to test the efficacy of the proposed model. The formulation was incorporated with available confirmed cases from January 22, 2020, to December 20, 2021, and parameterized using real-time series data that were collected on a daily basis for the first 705 days for fourteen countries, out of which the model was simulated using four selected countries: USA, Italy, South Africa, and Nigeria. A least square technique was adopted for the estimation of parameters. The simulated solutions of the model were analyzed using MAPLE-18 with Runge-Kutta-Felberg method (RKF45 solver). The model entrenched parameters analysis revealed that there are both disease-free and endemic equilibrium points. The solutions depicted that the free equilibrium point for COVID-19 is asymptotic locally stable, when the epidemiological reproduction number condition ( R 0 < 1 ) . The simulation results unveiled that the pandemic can be controlled if other control measures, such as face mask wearing in public areas and washing of hands, are combined with high level of compliance to physical distancing. Furthermore, an autonomous derivative equation for the five-dimensional deterministic was done with two control terms and constant rates for the pharmaceutical and non-pharmaceutical strategies. The Lagrangian and Hamilton were formulated to study the model optimal control existence, using Pontryagin's Maximum Principle describing the optimal control terms. The designed objective functional reduced the intervention costs and infections. We concluded that the COVID-19 curve can be flattened through strict compliance to both pharmaceutical and non-pharmaceutical strategies. The more the compliance level to physical distance and taking of vaccine, the earlier the curve is flattened and the earlier the economy will be bounce-back.