Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

SARS-CoV-2 N protein-induced Dicer, XPO5, SRSF3, and hnRNPA3 downregulation causes pneumonia

Though RNAi and RNA-splicing machineries are involved in regulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication, their precise roles in coronavirus disease 2019 (COVID-19) pathogenesis remain unclear. Herein, we show that decreased RNAi component (Dicer and XPO5) and splicing factor (SRSF3 and hnRNPA3) expression correlate with increased COVID-19 severity. SARS-CoV-2 N protein induces the autophagic degradation of Dicer, XPO5, SRSF3, and hnRNPA3, inhibiting miRNA biogenesis and RNA splicing and triggering DNA damage, proteotoxic stress, and pneumonia. Dicer, XPO5, SRSF3, and hnRNPA3 knockdown increases, while their overexpression decreases, N protein-induced pneumonia's severity. Older mice show lower expression of Dicer, XPO5, SRSF3, and hnRNPA3 in their lung tissues and exhibit more severe N protein-induced pneumonia than younger mice. PJ34, a poly(ADP-ribose) polymerase inhibitor, or anastrozole, an aromatase inhibitor, ameliorates N protein- or SARS-CoV-2-induced pneumonia by restoring Dicer, XPO5, SRSF3, and hnRNPA3 expression. These findings will aid in developing improved treatments for SARS-CoV-2-associated pneumonia.

Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework's effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.

Inference of epidemic dynamics in the COVID-19 era and beyond

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

Studying the impacts of variant evolution for a generalized age-group transmission model

The differences of SARS-CoV-2 variants brought the changes of transmission characteristics and clinical manifestations during the prevalence of COVID-19. In order to explore the evolution mechanisms of SARS-CoV-2 variants and the impacts of variant evolution, the classic SIR (Susceptible-Infected-Recovered) compartment model was modified to a generalized SVEIR (Susceptible-Vaccinated-Exposed-Infected-Recovered) compartment model with age-group and varying variants in this study. By using of the SVEIR model and least squares method, the optimal fittings against the surveillance data from Fujian Provincial Center for Disease Control and Prevention were performed for the five epidemics of Fujian Province. The main epidemiological characteristics such as basic reproduction number, effective reproduction number, sensitivity analysis, and cross-variant scenario investigations were extensively investigated during dynamic zero-COVID policy. The study results showed that the infectivities of the variants became fast from wild strain to the Delta variant, further to the Omicron variant. Meanwhile, the cross-variant investigations showed that the average incubation periods were shortened, and that the infection scales quickly enhanced. Further, the risk estimations with the new variants were performed without implements of the non-pharmaceutical interventions, based on the dominant variants XBB.1.9.1 and EG.5. The results of the risk estimations suggested that non-pharmaceutical interventions were necessary on the Chinese mainland for controlling severe infections and deaths, and also that the regular variant monitors were still workable against the aggressive variant evolution and the emergency of new transmission risks in the future.

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.

Nonconserved epitopes dominate reverse preexisting T cell immunity in COVID-19 convalescents

The herd immunity against SARS-CoV-2 is continuously consolidated across the world during the ongoing pandemic. However, the potential function of the nonconserved epitopes in the reverse preexisting cross-reactivity induced by SARS-CoV-2 to other human coronaviruses is not well explored. In our research, we assessed T cell responses to both conserved and nonconserved peptides shared by SARS-CoV-2 and SARS-CoV, identifying cross-reactive CD8+ T cell epitopes using enzyme-linked immunospot and intracellular cytokine staining assays. Then, in vitro refolding and circular dichroism were performed to evaluate the thermal stability of the HLA/peptide complexes. Lastly, single-cell T cell receptor reservoir was analyzed based on tetramer staining. Here, we discovered that cross-reactive T cells targeting SARS-CoV were present in individuals who had recovered from COVID-19, and identified SARS-CoV-2 CD8+ T cell epitopes spanning the major structural antigens. T cell responses induced by the nonconserved peptides between SARS-CoV-2 and SARS-CoV were higher and played a dominant role in the cross-reactivity in COVID-19 convalescents. Cross-T cell reactivity was also observed within the identified series of CD8+ T cell epitopes. For representative immunodominant peptide pairs, although the HLA binding capacities for peptides from SARS-CoV-2 and SARS-CoV were similar, the TCR repertoires recognizing these peptides were distinct. Our results could provide beneficial information for the development of peptide-based universal vaccines against coronaviruses.

Association Between Artificial Intelligence Based Chest Computed Tomography and Clinical/Laboratory Characteristics with Severity and Mortality in COVID-19 Hospitalized Patients

Background

Some patients with COVID-19 rapidly develop respiratory failure or mortality, underscoring the necessity for early identification of those prone to severe illness. Numerous studies focus on clinical and lab traits, but only few attend to chest computed tomography. The current study seeks to numerically quantify pulmonary lesions using early-phase CT scans calculated through artificial intelligence algorithms in conjunction with clinical and laboratory helps clinicians to early identify the development of severe illness and death in a group of COVID-19 patients.

Methods

From December 15, 2022, to January 30, 2023, 191 confirmed COVID-19 patients admitted to Xinhua Hospital Affiliated with Shanghai Jiao Tong University School of Medicine were consecutively enrolled. All patients underwent chest CT scans and serum tests within 48 hours prior to admission. Variables significantly linked to critical illness or mortality in univariate analysis were subjected to multivariate logistic regression models post collinearity assessment. Adjusted odds ratio, 95% confidence intervals, sensitivity, specificity, Youden index, receiver-operator-characteristics (ROC) curves, and area under the curve (AUC) were computed for predicting severity and in-hospital mortality.

Results

Multivariate logistic analysis revealed that myoglobin (OR = 1.003, 95% CI 1.001-1.005), APACHE II score (OR = 1.387, 95% CI 1.216-1.583), and the infected CT region percentage (OR = 113.897, 95% CI 4.939-2626.496) independently correlated with in-hospital COVID-19 mortality. Prealbumin stood as an independent safeguarding factor (OR = 0.965, 95% CI 0.947-0.984). Neutrophil counts (OR = 1.529, 95% CI 1.131-2.068), urea nitrogen (OR = 1.587, 95% CI 1.222-2.062), SOFA score(OR = 3.333, 95% CI 1.476-7.522), qSOFA score(OR = 15.197, 95% CI 3.281-70.384), PSI score(OR = 1.053, 95% CI 1.018-1.090), and the infected CT region percentage (OR = 548.221, 95% CI 2.615-114,953.586) independently linked to COVID-19 patient severity.

Informing Public Health Policies with Models for Disease Burden, Impact Evaluation, and Economic Evaluation

Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2's pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.

Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking

BACKGROUND

Web search data have proven to bea valuable early indicator of COVID-19 outbreaks. However, the influence of co-morbid conditions with similar symptoms and the effect of media coverage on symptom-related searches are often overlooked, leading to potential inaccuracies in COVID-19 simulations.

METHOD

This study introduces a machine learning-based approach to estimate the magnitude of the impact of media coverage and comorbid conditions with similar symptoms on online symptom searches, based on two scenarios with quantile levels 10-90 and 25-75. An incremental batch learning RNN-LSTM model was then developed for the COVID-19 simulation in Australia and New Zealand, allowing the model to dynamically simulate different infection rates and transmissibility of SARS-CoV-2 variants.

RESULT

The COVID-19 infected person-directed symptom searches were found to account for only a small proportion of the total search volume (on average 33.68% in Australia vs. 36.89% in New Zealand) compared to searches influenced by media coverage and comorbid conditions (on average 44.88% in Australia vs. 50.94% in New Zealand). The proposed method, which incorporates estimated symptom component ratios into the RNN-LSTM embedding model, significantly improved COVID-19 simulation performance.

CONCLUSION

Media coverage and comorbid conditions with similar symptoms dominate the total number of online symptom searches, suggesting that direct use of online symptom search data in COVID-19 simulations may overestimate COVID-19 infections. Our approach provides new insights into the accurate estimation of COVID-19 infections using online symptom searches, thereby assisting governments in developing complementary methods for public health surveillance.

Within-household SARS-CoV-2 transmission and vaccine effectiveness in the first three COVID-19 school outbreaks in northern Viet Nam, September-December 2021

Objective

The risk of transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from schoolchildren to their household and the protective effects of vaccination in these settings remain poorly understood. We assessed the transmission dynamics of schoolchildren with SARS-CoV-2 within their households and the protective effects of coronavirus disease (COVID-19) vaccination among household members in Viet Nam.

Methods

We estimated the attack rate, vaccine effectiveness and adjusted risk ratio (aRR) of factors associated with SARS-CoV-2 transmission to household contacts of children confirmed to have COVID-19 who attended three schools in Ha Nam, Phu Tho and Thanh Hoa provinces between September and December 2021 using multivariable regression with household-level random effects.

Results

This retrospective cohort study included 157 children infected with SARS-CoV-2 and their 540 household contacts. The attack rate among household contacts was 24.6% (133/540). Overall, vaccine effectiveness among household contacts was 39% (95% confidence interval [CI]: -1 to -63), higher among males than females and higher in adults aged > 40 years. COVID-19 transmission was greater among female household contacts compared with males (aRR: 1.35, 95% CI: 0.94 to 1.95), although not statistically significant, and highest among those aged 19-39 years (aRR: 2.51, 95% CI: 1.50 to 4.21). Fully vaccinated household contacts had significantly lower infection risk (aRR: 0.46, 95% CI: 0.26 to 0.84).

Discussion

We found substantial onward transmission of SARS-CoV-2 from schoolchildren to household members, and older people were more likely to be protected by vaccination. We recommend that schoolchildren and all household members living with schoolchildren receive at least two doses of a COVID-19 vaccine. Recognizing the role of schoolchildren in the onward transmission of COVID-19 is an important lesson learned by Viet Nam that can help not only in managing other outbreaks but also in protecting schoolchildren by predicting the progress of the outbreak and preparing for a timely response.

BT100, a three-in-one, multipurpose disinfecting, deodorizing, and air-cleaning solution with an effective, gradual, and continuous gaseous chlorine dioxide-releasing substance

Aerosols carrying viruses that are released from the oral cavity of infected individuals are the primary, if not the only, means of transmission during viral respiratory disease epidemics. This makes crowded rooms and tiny, enclosed public areas like bathrooms prime environments for the transmission of diseases. Volatile organic compounds (VOCs) and formaldehyde are two contaminants that pose serious threats to human health and well-being in indoor environments. The varied disinfectant properties of chlorine dioxide (ClO2) make it a key player in treating a range of air quality issues. To balance effectiveness and safety, however, the careful application of chlorine dioxide is essential to achieving the best results in air quality while preserving human health and well-being. This study explores the many functions of chlorine dioxide, including the prevention of the spread of viruses, the elimination of harmful gases like ammonia and hydrogen sulfide, and its effects on formaldehyde and total volatile organic compounds (TVOCs) in indoor environments using BT100. The results indicate a reduction of 98.5%, 81.01%, 62.22%, 46.5%, and 63.84% in minimizing aerosolized viruses, ammonia, and hydrogen sulfide gas in addition to formaldehyde and total volatile organic compounds.

The Role of the Nuclear Factor-Kappa B (NF-κB) Pathway in SARS-CoV-2 Infection

COVID-19 is a global health threat caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is associated with a significant increase in morbidity and mortality. The present review discusses nuclear factor-kappa B (NF-κB) activation and its potential therapeutical role in treating COVID-19. COVID-19 pathogenesis, the major NF-κB pathways, and the involvement of NF-κB in SARS-CoV-2 have been detailed. Specifically, NF-κB activation and its impact on managing COVID-19 has been discussed. As a central player in the immune and inflammatory responses, modulating NF-κB activation could offer a strategic avenue for managing SARS-CoV-2 infection. Understanding the NF-κB pathway's role could aid in developing treatments against SARS-CoV-2. Further investigations into the intricacies of NF-κB activation are required to reveal effective therapeutic strategies for managing and combating the SARS-CoV-2 infection and COVID-19.

The association between vitamin D deficiency and the clinical outcomes of hospitalized COVID-19 patients

Background

Vitamin D deficiency is an emerging public health problem that affects more than one billion people worldwide. Vitamin D has been shown to be effective in preventing and reducing the severity of viral respiratory diseases, including influenza. However, the role of vitamin D in COVID-19 infection remains controversial. This study aimed to analyze the association of vitamin D deficiency on the clinical outcome of hospitalized COVID-19 patients.

Methods

A prospective cohort study was conducted among hospitalized COVID-19 patients at two COVID-19 referral hospitals in Indonesia from October 2021 until February 2022.

Results

The median serum 25(OH)D level in 191 hospitalized COVID-19 patients was 13.6 [IQR=10.98] ng/mL. The serum 25(OH)D levels were significantly lower among COVID-19 patients with vitamin D deficiency who had cardiovascular disease (p-value=0.04), the use of a ventilator (p-value=0.004), more severe COVID-19 cases (p-value=0.047), and mortality (p-value=0.002). Furthermore, serum 25(OH)D levels were significantly different between patients with mild and severe COVID-19 cases (p-value=0.019). Serum 25(OH)D levels in moderate and severe COVID-19 cases were significantly different (p-value=0.031). Lower serum 25(OH)D levels were significantly associated with an increased number of comorbidities (p-value=0.03), the severity of COVID-19 (p-value=0.002), and the use of mechanical ventilation (p-value=0.032). Mortality was found in 7.3% of patients with deficient vitamin D levels. However, patients with either sufficient or insufficient vitamin D levels did not develop mortality.

Conclusions

COVID-19 patients with vitamin D deficiency were significantly associated with having cardiovascular disease, mortality, more severe COVID-19 cases, and the used of mechanical ventilation. Lower serum 25(OH)D levels were associated with an increased number of comorbidities, COVID-19 severity, and the use of mechanical-ventilation. Thus, we suggest hospitalized COVID-19 patients to reach a sufficient vitamin D status to improve the clinical outcome of the disease.

Prediction models of COVID-19 fatality in nine Peruvian provinces: A secondary analysis of the national epidemiological surveillance system

There are initiatives to promote the creation of predictive COVID-19 fatality models to assist decision-makers. The study aimed to develop prediction models for COVID-19 fatality using population data recorded in the national epidemiological surveillance system of Peru. A retrospective cohort study was conducted (March to September of 2020). The study population consisted of confirmed COVID-19 cases reported in the surveillance system of nine provinces of Lima, Peru. A random sample of 80% of the study population was selected, and four prediction models were constructed using four different strategies to select variables: 1) previously analyzed variables in machine learning models; 2) based on the LASSO method; 3) based on significance; and 4) based on a post-hoc approach with variables consistently included in the three previous strategies. The internal validation was performed with the remaining 20% of the population. Four prediction models were successfully created and validate using data from 22,098 cases. All models performed adequately and similarly; however, we selected models derived from strategy 1 (AUC 0.89, CI95% 0.87-0.91) and strategy 4 (AUC 0.88, CI95% 0.86-0.90). The performance of both models was robust in validation and sensitivity analyses. This study offers insights into estimating COVID-19 fatality within the Peruvian population. Our findings contribute to the advancement of prediction models for COVID-19 fatality and may aid in identifying individuals at increased risk, enabling targeted interventions to mitigate the disease. Future studies should confirm the performance and validate the usefulness of the models described here under real-world conditions and settings.

Association between mortality risk levels and AIFELL triage scores for suspected COVID-19 patients

BACKGROUND

It is crucial to examine and classify patients as soon as possible to save their lives when they display Coronavirus Disease of 2019 (COVID-19) symptoms. The Altered sense of smell/taste, Inflammation, Fever, Elevated Lactate dehydrogenase, and Lymphocytopenia (AIFELL) evaluation tool is quick, easy, and simple for medical professionals.

OBJECTIVES

Determine the relationship between the COVID-19 patient confirmation and the AIFELL score. Examine any relationships between the AIFELL score and the degree of mortality.

MATERIALS AND METHODS

A retrospective study was conducted on 970 hospitalized (18 years or older) with a COVID-19 diagnosis in 2021. Patients admitted to the intensive care unit (ICU) as critical cases and moderate cases. The Chi-square test was utilized.

RESULTS

The scores of the AIFELL tool ranged from 0 to 6 points; the AIFELL score for COVID-19 symptoms with a high score (4-6) made up 41.5% of the patients. More than half of the patients (58.7%) were men; the oldest age group ranged from 40 to 50 years. A very high risk of dying due to a positive COVID-19 virus exists in more than a fifth of patients (21.5%). The ICU (37.5%) received around a third of the patients. The findings showed significant associations between levels of mortality risk and gender and age. There were significant associations between AIFELL scores and mortality risk levels. AIFELL scores, mortality risk levels, and patient admissions to the critical care unit were strongly associated.

CONCLUSION

The AIFELL scores were excellent for predicting COVID-19 mortality risk levels and ICU admission.

Volunteering and instrumental support during the first phase of the pandemic in Europe: the significance of COVID-19 exposure and stringent country's COVID-19 policy

BACKGROUND

The COVID-19 control policies might negatively impact older adults' participation in volunteer work, instrumental support provision, and the likelihood of receiving instrumental support. Studies that quantify changes in these activities and the related factors are limited. The current study aimed to examine the level of volunteering, instrumental support provision and receipt before and during the first phase of the COVID-19 pandemic in Europe and to determine whether older adults' volunteering, instrumental support provision and receipt were associated with individual exposure to COVID-19 and the stringency of country's COVID-19 control policy during the first phase of the COVID-19 pandemic.

METHODS

A cross-sectional survey using data from the Survey of Health, Ageing and Retirement in Europe (SHARE) Corona Survey 1 was designed to focus on community-dwelling Europeans aged ≥50 years. History of participation in volunteering work and instrumental support provision or receipt was assessed from the previous SHARE Wave data. The country's COVID-19 control policy stringency index (S-Index) was from the Oxford COVID-19 Government Response Tracker database. A total of 45,669 respondents from 26 European countries were included in the volunteering analysis. Seventeen European countries were included in the analyses of instrumental support provision (N = 36,518) and receipt (N = 36,526). The multilevel logistic regression model was fitted separately to analyse each activity.

RESULTS

The level of volunteering and instrumental support provision was lower during the pandemic, but instrumental support receipt was higher. The country S-Index was positively associated with support provision (OR:1.13;95%CI:1.02-1.26) and negatively associated with support receipt (OR:0.69;95%CI:0.54-0.88). Exposure to COVID-19 was positively associated with support receipt (OR:1.64;95%CI:1.38-1.95). COVID-19 exposure on close ones positively associated with volunteering (OR:1.47;95%CI:1.32-1.65), support provision (OR:1.28;95%CI:1.19-1.39), and support receipt (OR:1.25;95%CI:1.15-1.35).

CONCLUSIONS

The COVID-19 pandemic impacted older Europeans' volunteering, instrumental support provision, and instrumental support receipt from outside their household. When someone close to them was exposed to COVID-19, older Europeans were likely to receive instrumental support and to volunteer and provide instrumental support. A stricter country's COVID-19 control policy might motivate older adults to provide instrumental support, but it prevents them from receiving instrumental support from outside their households.

[Clinical symptoms and signs in hamsters during experimental infection with the SARS-CoV-2 virus (Coronaviridae: Betacoronavirus)]

INTRODUCTION

At the beginning of December 2019, humanity has faced a new problem caused by coronavirus. In Hubei province of central China, epidemic events associated with severe primary viral pneumonia in humans began to develop. The isolated etiological agent was identified as a representative of Coronaviridae family. The global pandemic associated with the new coronavirus infection, acute respiratory syndrome type 2 (Severe acute respiratory syndrome 2, SARS-CoV-2), has become a challenge for humanity.

OBJECTIVE

In our work, we assessed the replicative ability and pathogenesis of the SARS-CoV-2 virus in hamsters.

MATERIALS AND METHODS

Syrian hamsters (n=16) randomly divided into two groups were used in experiment. The first group was infected intranasally with the SARS-CoV-2 virus, strain SARS-CoV-2/human/KAZ/KZ_Almaty/2020 deposited in GenBank under number MZ379258.1. The second group remained as a control group. Clinical manifestations of the disease in hamsters were observed within 14 days. Samples were collected on days 3, 5, 7, 9, 12, and 14 postinfection. The obtained samples were tested for viral isolation in cell culture, histological examination and analysis of viral RNA by RT-PCR.

RESULTS

SARS-CoV-2 virus isolates showed efficient replication in the lungs of hamsters, causing pathological lung lesions in animals infected intranasally. Clinical manifestations of the disease in hamsters infected with this virus were characterized by a decrease in temperature and body weight, wetness and ruffled fur, and frequent stroking of the nasal planum. High virus titers were observed following the virus isolation in cell cultures from nasal, oral swabs and lungs of animals infected intranasally. Pathological autopsy demonstrated pathological changes in the lungs. Moreover, transmission by airborne droplets has been established when a healthy hamster was kept together with animals infected using the intranasal method.

CONCLUSION

In conclusion, our study showed that the Syrian hamster model is a useful tool for studying the SARS-CoV-2 pathogenesis, as well as testing vaccine candidates against acute respiratory syndrome type 2.

Antibody responses after BNT162b2 vaccination in Japanese geriatric intermediate care facilities

Background

To evaluate antibody responses against the primary series of vaccination of severe acute respiratory syndrome coronavirus-2 [SARS-CoV-2] vaccines in the staff and residents of Japanese geriatric intermediate care facilities.

Methods

All subjects (159 staff and 96 residents) received two doses of the BNT162b2 mRNA vaccine 3 weeks apart. Baseline data of subject were collected using a structured form. Serum samples were collected three times: before vaccination, 3 weeks after the first dose, and 4 weeks after the second dose, and anti-receptor binding domain of the spike protein of SARS-CoV-2 [anti-RBD] IgG was measured using two immunoassays.

Results

After the second dose, geometric mean titers [GMT] of anti-RBD with both the Abbott and Roche assay were significantly lower in residents than staff (2282 AU/mL vs. 8505 AU/mL, and 258 U/mL vs. 948 U/mL, respectively). Multivariate analysis of characteristics affecting antibody responses (≥1280 AU/mL for Abbott and > 210 U/mL for Roche) showed lower odds ratios [ORs] for older age (adjusted OR per 10 year increase [aOR] = 0.62, 95 % confidence interval [95 %CI]; 0.38-1.02), steroid usage (aOR = 0.09, 95 %CI; 0.01-0.60) and regular nonsteroidal anti-inflammatory drugs [NSAIDs] usage (aOR = 0.16, 95 %CI; 0.03-0.88).

Conclusions

Elderly people and steroid and NSAID users had lower antibody responses following the second vaccine dose.

The Impact of SIV-Induced Immunodeficiency on Clinical Manifestation, Immune Response, and Viral Dynamics in SARS-CoV-2 Coinfection

Persistent and uncontrolled SARS-CoV-2 replication in immunocompromised individuals has been observed and may be a contributing source of novel viral variants that continue to drive the pandemic. Importantly, the effects of immunodeficiency associated with chronic HIV infection on COVID-19 disease and viral persistence have not been directly addressed in a controlled setting. Here we conducted a pilot study wherein two pigtail macaques (PTM) chronically infected with SIVmac239 were exposed to SARS-CoV-2 and monitored for six weeks for clinical disease, viral replication, and viral evolution, and compared to our previously published cohort of SIV-naïve PTM infected with SARS-CoV-2. At the time of SARS-CoV-2 infection, one PTM had minimal to no detectable CD4+ T cells in gut, blood, or bronchoalveolar lavage (BAL), while the other PTM harbored a small population of CD4+ T cells in all compartments. Clinical signs were not observed in either PTM; however, the more immunocompromised PTM exhibited a progressive increase in pulmonary infiltrating monocytes throughout SARS-CoV-2 infection. Single-cell RNA sequencing (scRNAseq) of the infiltrating monocytes revealed a less activated/inert phenotype. Neither SIV-infected PTM mounted detectable anti-SARS-CoV-2 T cell responses in blood or BAL, nor anti-SARS-CoV-2 neutralizing antibodies. Interestingly, despite the diminished cellular and humoral immune responses, SARS-CoV-2 viral kinetics and evolution were indistinguishable from SIV-naïve PTM in all sampled mucosal sites (nasal, oral, and rectal), with clearance of virus by 3-4 weeks post infection. SIV-induced immunodeficiency significantly impacted immune responses to SARS-CoV-2 but did not alter disease progression, viral kinetics or evolution in the PTM model. SIV-induced immunodeficiency alone may not be sufficient to drive the emergence of novel viral variants.

Exploring the impact of population ageing on the spread of emerging respiratory infections and the associated burden of mortality

BACKGROUND

Increasing life expectancy and persistently low fertility levels have led to old population age structures in most high-income countries, and population ageing is expected to continue or even accelerate in the coming decades. While older adults on average have few interactions that potentially could lead to disease transmission, their morbidity and mortality due to infectious diseases, respiratory infections in particular, remain substantial. We aim to explore how population ageing affects the future transmission dynamics and mortality burden of emerging respiratory infections.

METHODS

Using longitudinal individual-level data from population registers, we model the Belgian population with evolving age and household structures, and explicitly consider long-term care facilities (LTCFs). Three scenarios are presented for the future proportion of older adults living in LTCFs. For each demographic scenario, we simulate outbreaks of SARS-CoV-2 and a novel influenza A virus in 2020, 2030, 2040 and 2050 and distinguish between household and community transmission. We estimate attack rates by age and household size/type, as well as disease-related deaths and the associated quality-adjusted life-years (QALYs) lost.

RESULTS

As the population is ageing, small households and LTCFs become more prevalent. Additionally, families with children become smaller (i.e. low fertility, single-parent families). The overall attack rate slightly decreases as the population is ageing, but to a larger degree for influenza than for SARS-CoV-2 due to differential age-specific attack rates. Nevertheless, the number of deaths and QALY losses per 1,000 people is increasing for both infections and at a speed influenced by the share living in LTCFs.

CONCLUSION

Population ageing is associated with smaller outbreaks of COVID-19 and influenza, but at the same time it is causing a substantially larger burden of mortality, even if the proportion of LTCF residents were to decrease. These relationships are influenced by age patterns in epidemiological parameters. Not only the shift in the age distribution, but also the induced changes in the household structures are important to consider when assessing the potential impact of population ageing on the transmission and burden of emerging respiratory infections.

Frequency of dysnatremia in patients admitted with COVID-19 infection and its prognostic implication

OBJECTIVE

We aimed to investigate the frequency of dysnatremia among patients admitted with COVID-19 infection and its association with inpatient mortality.

METHODS

This retrospective longitudinal study was conducted for 12 weeks. Serum sodium levels were recorded at admission, during the hospital stay, and within 48 hours of discharge or death. Logistic regression was used to determine the predictors of mortality.

RESULTS

This study included 574 patients (69.7% men, age 55.6 ± 14.4 years). On admission, mean sodium was 135.9 ± 6.4 mEq/L; 39% had hyponatremia and 4.7% had hypernatremia. During admission, hypernatremia increased to 18.8%; maximum sodium in patients who survived was 140.6 ± 5.0 mEq/L versus 151.0 ± 9.9 mEq/L in those who died. The final sodium was 145.4 ± 9.4 mEq/L in patients who died versus 137.7 ± 3.7 mEq/L in those who survived (odds ratio [OR]: 1.22, 95% confidence interval [CI]: 1.13-1.32). Other predictors of mortality included ischemic heart disease (OR: 3.65, 95% CI: 1.39-9.61), acute kidney injury (OR: 6.07, 95% CI: 2.39-15.42), invasive ventilation (OR: 28.4, 95% CI: 11.14-72.40), and length of stay (OR: 0.91, 95% CI: 0.86-0.97).

CONCLUSION

Hypernatremia was frequently observed in patients who were critically ill and died and may be considered a predictor of mortality in COVID-19 infection.

Clinical Severity of SARS-CoV-2 Variants during COVID-19 Vaccination: A Systematic Review and Meta-Analysis

Due to the variation in the SARS-CoV-2 virus, COVID-19 exhibits significant variability in severity. This presents challenges for governments in managing the allocation of healthcare resources and prioritizing health interventions. Clinical severity is also a critical statistical parameter for researchers to quantify the risks of infectious disease, model the transmission of COVID-19, and provide some targeted measures to control the pandemic. To obtain more accurate severity estimates, including confirmed case-hospitalization risk, confirmed case-fatality risk, hospitalization-fatality risk, and hospitalization-ICU risk, we conducted a systematic review and meta-analysis on the clinical severity (including hospitalization, ICU, and fatality risks) of different variants during the period of COVID-19 mass vaccination and provided pooled estimates for each clinical severity metric. All searches were carried out on 1 February 2022 in PubMed for articles published from 1 January 2020 to 1 February 2022. After identifying a total of 3536 studies and excluding 3523 irrelevant studies, 13 studies were included. The severity results show that the Delta and Omicron variants have the highest (6.56%, 0.46%, 19.63%, and 9.06%) and lowest severities (1.51%, 0.04%, 6.01%, and 3.18%), respectively, according to the four clinical severity metrics. Adults over 65 have higher severity levels for all four clinical severity metrics.

A systematical association analysis of 25 common virus infection and genetic susceptibility of COVID-19 infection

OBJECTIVES

Previous studies identified a number of diseases were associated with 2019 coronavirus disease (COVID-19). However, the associations between these diseases related viral infections and COVID-19 remains unknown now.

METHODS

In this study, we utilized single nucleotide polymorphisms (SNPs) related to COVID-19 from genome-wide association study (GWAS) and individual-level genotype data from the UK biobank to calculate polygenic risk scores (PRS) of 487,409 subjects for eight COVID-19 clinical phenotypes. Then, multiple logistic regression models were established to assess the correlation between serological measurements (positive/negative) of 25 viruses and the PRS of eight COVID-19 clinical phenotypes. And we performed stratified analyses by age and gender.

RESULTS

In whole population, we identified 12 viruses associated with the PRS of COVID-19 clinical phenotypes, such as VZV seropositivity for Varicella Zoster Virus (Unscreened/Exposed_Negative: β = 0.1361, P = 0.0142; Hospitalized/Unscreened: β = 0.1167, P = 0.0385) and MCV seropositivity for Merkel Cell Polyomavirus (Unscreened/Exposed_Negative: β = -0.0614, P = 0.0478). After age stratification, we identified seven viruses associated with the PRS of eight COVID-19 clinical phenotypes in the age < 65 years group. After gender stratification, we identified five viruses associated with the PRS of eight COVID-19 clinical phenotypes in the women group.

CONCLUSION

Our study findings suggest that the genetic susceptibility to different COVID-19 clinical phenotypes is associated with the infection status of various common viruses.

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.

Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series

The ability to infer the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is crucial for multiple applications. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from computer vision. Here, we develop an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with incidence that allows the identification of regime changes at single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions (NPIs) with 0.93-day accuracy over the time span of a year.

Analysis of Factors Influencing the Clinical Severity of Omicron and Delta Variants

The Omicron variant is the dominant strain circulating globally, and studies have shown that Omicron cases have milder symptoms than Delta cases. This study aimed to analyze the factors that affect the clinical severity of Omicron and Delta variants, evaluate and compare the effectiveness of COVID-19 vaccines with different technological platforms, and assess the vaccine effectiveness against different variants. We retrospectively collected the basic information of all local COVID-19 cases reported by Hunan Province to the National Notifiable Infectious Disease Reporting System from January 2021 to February 2023, including gender, age, clinical severity, and COVID-19 vaccination history. From 1 January 2021 to 28 February 2023, Hunan Province reported a total of 60,668 local COVID-19 cases, of which, 134 were infected with the Delta variant and 60,534 were infected with the Omicron variant. The results showed that infection with the Omicron variant (adjusted OR (aOR): 0.21, 95% CI: 0.14-0.31), getting vaccinated (booster immunization vs. unvaccinated aOR: 0.30, 95% CI: 0.23-0.39) and being female (aOR: 0.82, 95% CI: 0.79-0.85) were protective factors for pneumonia, while old age (≥60 years vs. <3 years aOR: 4.58, 95% CI: 3.36-6.22) was a risk factor for pneumonia. Being vaccinated (booster immunization vs. unvaccinated aOR: 0.11, 95% CI: 0.09-0.15) and female (aOR: 0.54, 95% CI: 0.50-0.59) were protective factors for severe cases, while older age (≥60 years vs. < 3 years aOR: 4.95, 95% CI: 1.83-13.39) was a risk factor for severe cases. The three types of vaccines had protective effects on both pneumonia and severe cases, and the protective effect on severe cases was better than that on pneumonia. The recombinant subunit vaccine booster immunization had the best protective effect on pneumonia and severe cases, with ORs of 0.29 (95% CI: 0.2-0.44) and 0.06 (95% CI: 0.02-0.17), respectively. The risk of pneumonia from Omicron variant infection was lower than that from Delta. Chinese-produced vaccines had protective effects on both pneumonia and severe cases, with recombinant subunit vaccines having the best protective effect on pneumonia and severe pneumonia cases. Booster immunization should be advocated in COVID-19 pandemic-related control and prevention policies, especially for the elderly, and booster immunization should be accelerated.

COVID-19 vaccination coverage and its cognitive determinants among older adults in Shanghai, China, during the COVID-19 epidemic

Objectives

This study aimed to examine the coverage of coronavirus disease 2019 (COVID-19) vaccination and its cognitive determinants among older adults.

Methods

A cross-sectional study was conducted using a questionnaire to conduct a survey among 725 Chinese older adults aged 60 years and above in June 2022, 2 months after the mass COVID-19 outbreak in Shanghai, China. The questionnaire covered demographic characteristics, COVID-19 vaccination status, internal risk perception, knowledge, and attitude toward the efficacy and safety of COVID-19 vaccines.

Results

The vaccination rate was 78.3% among the surveyed individuals. Self-reported reasons for unwillingness to get vaccinated (multiple selections) were "concerns about acute exacerbation of chronic diseases after vaccination (57.3%)" and "concerns regarding vaccine side effects (41.4%)." Compared to the unvaccinated group, the vaccinated group tended to have a higher score in internal risk perception (t = 2.64, P < 0.05), better knowledge of COVID-19 vaccines (t = 5.84, P < 0.05), and a more positive attitude toward the efficacy and safety of COVID-19 vaccines (t = 7.92, P < 0.05). The path analysis showed that the cognitive effect on vaccination behavior is relatively large, followed by the internal risk perception, and then the attitude toward COVID-19 vaccines. The more knowledgeable the participants were about COVID-19 vaccines, the more likely they were to receive the COVID-19 vaccines. In the multivariate logistic regression, the increased coverage of COVID-19 vaccination was associated with reduced age (OR = 0.53 95% CI 0.43-0.66, P < 0.001), being a resident in other places than Shanghai (OR = 0.40, 95% CI 0.17-0.92, P < 0.05), a shorter time of lockdown (OR = 0.33, 95% CI 0.13-0.83, P < 0.05), a history of other vaccines (OR = 2.58, 95% CI 1.45-4.60, P < 0.01), a fewer number of chronic diseases (OR = 0.49, 95% CI 0.38-0.62, P < 0.001), better knowledge about COVID-19 vaccines (OR = 1.60, 95% CI 1.17-2.19, P < 0.01), and a positive attitude toward COVID-19 vaccines (OR = 9.22, 95% CI 4.69-18.09, P < 0.001).

Conclusion

Acquiring accurate knowledge and developing a positive attitude toward COVID-19 vaccines are important factors associated with COVID-19 vaccination. Disseminating informed information on COVID-19 vaccines and ensuring efficacious communication regarding their efficacy and safety would enhance awareness about COVID-19 vaccination among older adults and consequently boost their vaccination coverage.

Studying of COVID-19 fractional model: Stability analysis

This article focuses on the recent epidemic caused by COVID-19 and takes into account several measures that have been taken by governments, including complete closure, media coverage, and attention to public hygiene. It is well known that mathematical models in epidemiology have helped determine the best strategies for disease control. This motivates us to construct a fractional mathematical model that includes quarantine categories as well as government sanctions. In this article, we prove the existence and uniqueness of positive bounded solutions for the suggested model. Also, we investigate the stability of the disease-free and endemic equilibriums by using the basic reproduction number (BRN). Moreover, we investigate the stability of the considering model in the sense of Ulam-Hyers criteria. To underpin and demonstrate this study, we provide a numerical simulation, whose results are consistent with the analysis presented in this article.

Modeling SARS-CoV-2 antibody seroprevalence and its determinants in Ghana: A nationally representative cross-sectional survey

Estimates of SARS-CoV-2 transmission rates have significant public health policy implications since they shed light on the severity of illness in various groups and aid in strategic deployment of diagnostics, treatment and vaccination. Population-based investigations have not been conducted in Ghana to identify the seroprevalence of SARS-CoV-2. We conducted an age stratified nationally representative household study to determine the seroprevalence of SARS-CoV-2 and identify risk factors between February and December 2021. Study participants, 5 years and older regardless of prior or current infection COVID-19 infection from across Ghana were included in the study. Data on sociodemographic characteristics, contact with an individual with COVID-19-related symptoms, history of COVID-19-related illness, and adherence to infection prevention measures were collected. Serum obtained was tested for total antibodies with the WANTAI ELISA kit. The presence of antibodies against SAR-COV-2 was detected in 3,476 of 5,348 participants, indicating a seroprevalence of 67.10% (95% CI: 63.71-66.26). Males had lower seroprevalence (65.8% [95% CI: 63.5-68.04]) than females (68.4% [95% CI: 66.10-69.92]). Seroprevalence was lowest in >20 years (64.8% [95% CI: 62.36-67.19]) and highest among young adults; 20-39 years (71.1% [95% CI 68.83,73.39]). Seropositivity was associated with education, employment status and geographic location. Vaccination status in the study population was 10%. Exposure is more likely in urban than rural areas thus infection prevention protocols must be encouraged and maintained. Also, promoting vaccination in target groups and in rural areas is necessary to curb transmission of the virus.

Multiscale network analysis identifies potential receptors for SARS-CoV-2 and reveals their tissue-specific and age-dependent expression

The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Here, we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. In particular, we focused on key regulators, cell receptors, and host processes that were hijacked by the virus for its advantage. ACE2-controlled processes involved CD300e (a TYROBP receptor) as a key regulator and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigated the age dependency of such receptors in different tissues. In summary, this study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific, age-dependent expression of the cell receptors involved in COVID-19.

Overview of the cardio-metabolic impact of the COVID-19 pandemic

Evidence has shown that cardiometabolic disorders (CMDs) are amongst the top contributors to COVID-19 infection morbidity and mortality. The reciprocal impact of COVID-19 infection and the most common CMDs, the risk factors for poor composite outcome among patients with one or several underlying diseases, the effect of common medical management on CMDs and their safety in the context of acute COVID-19 infection are reviewed. Later on, the changes brought by the COVID-19 pandemic quarantine on the general population's lifestyle (diet, exercise patterns) and metabolic health, acute cardiac complications of different COVID-19 vaccines and the effect of CMDs on the vaccine efficacy are discussed. Our review identified that the incidence of COVID-19 infection is higher among patients with underlying CMDs such as hypertension, diabetes, obesity and cardiovascular disease. Also, CMDs increase the risk of COVID-19 infection progression to severe disease phenotypes (e.g. hospital and/or ICU admission, use of mechanical ventilation). Lifestyle modification during COVID-19 era had a great impact on inducing and worsening of CMDs. Finally, the lower efficacy of COVID-19 vaccines was found in patients with metabolic disease.

Age-specific transmission dynamics under suppression control measures during SARS-CoV-2 Omicron BA.2 epidemic

BACKGROUND

From March to June 2022, an Omicron BA.2 epidemic occurred in Shanghai. We aimed to better understand the transmission dynamics and identify age-specific transmission characteristics for the epidemic.

METHODS

Data on COVID-19 cases were collected from the Shanghai Municipal Health Commission during the period from 20th February to 1st June. The effective reproductive number (R_t) and transmission distance between cases were calculated. An age-structured SEIR model with social contact patterns was developed to reconstruct the transmission dynamics and evaluate age-specific transmission characteristics. Least square method was used to calibrate the model. Basic reproduction number (R₀) was estimated with next generation matrix.

RESULTS

R₀ of Omicron variant was 7.9 (95% CI: 7.4 to 8.4). With strict interventions, R_t had dropped quickly from 3.6 (95% CI: 2.7 to 4.7) on 4th March to below 1 on 18th April. The mean transmission distance of the Omicron epidemic in Shanghai was 13.4 km (95% CI: 11.1 to 15.8 km), which was threefold longer compared with that of epidemic caused by the wild-type virus in Wuhan, China. The model estimated that there would have been a total 870,845 (95% CI: 815,400 to 926,289) cases for the epidemic from 20th February to 15th June, and 27.7% (95% CI: 24.4% to 30.9%) cases would have been unascertained. People aged 50-59 years had the highest transmission risk 0.216 (95% CI: 0.210 to 0.222), and the highest secondary attack rate (47.62%, 95% CI: 38.71% to 56.53%).

CONCLUSIONS

The Omicron variant spread more quickly and widely than other variants and resulted in about one third cases unascertained for the recent outbreak in Shanghai. Prioritizing isolation and screening of people aged 40-59 might suppress the epidemic more effectively. Routine surveillance among people aged 40-59 years could also provide insight into the stage of the epidemic and the timely detection of new variants.

TRIAL REGISTRATION

We did not involve clinical trial.

The Effects of Race/Ethnicity, Age, and Area Deprivation Index (ADI) on COVID-19 Disease Early Dynamics: Washington, D.C. Case Study

The COVID-19 pandemic and its associated mitigation strategies have significant psychosocial, behavioral, socioeconomic, and health impacts, particularly in vulnerable US populations. Different factors have been identified as influencers of the transmission rate; however, the effects of area deprivation index (as a measure of social determinants of health, SDoH) as a factor on COVID-19 disease early dynamics have not been established. We determined the effects of area deprivation index (ADI) and demographic factors on COVID-19 outcomes in Washington, D.C. This retrospective study used publicly available data on COVID-19 cases and mortality of Washington, D.C., during March 31st-July 4th, 2020. The main predictors included area deprivation index (ADI), age, and race/ethnicity. The ADI of each census block groups in D.C. (n=433) were obtained from Neighborhood Atlas map. Using a machine learning-based algorithm, the outcome variables were partitioned into time intervals: time duration (Pi, days), rate of change coefficient (Ei), and time segment load (Pi×Ei) for transmission rate and mortality. Correlation analysis and multiple linear regression models were used to determine associations between predictors and outcome variables. COVID-19 early transmission rate (E1) was highly correlated with ADI (SDoH; r= 0.88, p=0.0044) of the Washington, D.C. community. We also found positive association between ADI, age (0-17 years, r=0.91, p=0.0019), and race (African American/Black, r=0.86; p=0.0068) and COVID-19 outcomes. There was high variability in early transmission across the geographic regions (i.e., wards) of Washington, D.C., and this variability was driven by race/ethnic composition and ADI. Understanding the association of COVID-19 disease early transmission and mortality dynamics and key socio-demographic risk factors such as age, race, and ADI, as a measure of social determinants, will contribute to health equity/equality and distribution of economic resources/assistance and is essential for future predictive modeling of the COVID-19 pandemic to limit morbidity and mortality.

Lockdown Without Loss? A Natural Experiment of Net Payoffs from COVID-19 Lockdowns

Lacking a federal policy to control the spread of COVID-19, state governors ordered lockdowns and mask mandates, at different times, generating a massive natural experiment. The authors exploit this natural experiment to address four issues: (1) Were lockdowns effective in reducing infections? (2) What were the costs to consumers? (3) Did lockdowns increase (signaling effect) or reduce (substitution effect) consumers' mask adoption? (4) Did governors' decisions depend on medical science or nonmedical drivers? Analyses via difference-in-differences and generalized synthetic control methods indicate that lockdowns causally reduced infections. Although lockdowns reduced infections by 480 per million consumers per day (equivalent to a reduction of 56%), they reduced customer satisfaction by 2.2%, consumer spending by 7.5%, and gross domestic product by 5.4% and significantly increased unemployment by 2% per average state by the end of the observation period. A counterfactual analysis shows that a nationwide lockdown on March 15, 2020, would have reduced total cases by 60%, whereas the absence of any state lockdowns would have resulted in five times more cases by April 30. The average cost of reducing the number of cases by one new infection was about $28,000 in lower gross domestic product.

Synergistic Detrimental Effects of Cigarette Smoke, Alcohol, and SARS-CoV-2 in COPD Bronchial Epithelial Cells

Lung conditions such as COPD, as well as risk factors such as alcohol misuse and cigarette smoking, can exacerbate COVID-19 disease severity. Synergistically, these risk factors can have a significant impact on immunity against pathogens. Here, we studied the effect of a short exposure to alcohol and/or cigarette smoke extract (CSE) in vitro on acute SARS-CoV-2 infection of ciliated human bronchial epithelial cells (HBECs) collected from healthy and COPD donors. We observed an increase in viral titer in CSE- or alcohol-treated COPD HBECs compared to untreated COPD HBECs. Furthermore, we treated healthy HBECs accompanied by enhanced lactate dehydrogenase activity, indicating exacerbated injury. Finally, IL-8 secretion was elevated due to the synergistic damage mediated by alcohol, CSE, and SARS-CoV-2 in COPD HBECs. Together, our data suggest that, with pre-existing COPD, short exposure to alcohol or CSE is sufficient to exacerbate SARS-CoV-2 infection and associated injury, impairing lung defences.

The impact of social-relationship patterns on worsening mental health among the elderly during the COVID-19 pandemic: Evidence from Hungary

Evidence indicates the coronavirus pandemic has severely affected the oldest age group: in Hungary 92.7 per cent of those who died due to COVID-19 infection were 60 years of age or older. The age structure of the deceased was very similar in other countries. As, due to their underlying health status, elderly people are more vulnerable to the serious diseases caused by the coronavirus, strict government restrictions were introduced worldwide to protect them when the epidemic broke out. However, this social isolation can cause severe psychological and emotional strain. The study aim was to examine how patterns of social relationships were related to changes in mental health of the elderly during the COVID-19 pandemic in Hungary. We used the database of SHARE COVID-19 for our analysis. Descriptive and multivariate analyses were conducted. Findings of this study suggest that the network lacking contacts increases the severity of depression, anxiety, sleeping disorders, and loneliness the most among the elderly. Interestingly, the group with an intense close-knit network had a higher risk of worsening mental symptoms compared to multiple contact actives. Moreover, electronic communication - telephone, Skype - did not help either: there was a particularly high probability of worsening depression in this group. Another interesting result is that both in the group dominated by family contacts and among those with multiple relationships, the chances of worsening mental symptoms were lower, thus, personal contact - whether they include family members or other acquaintances - is truly protective. Results demonstrate that both the network characterised by lack of contact network and electronic contact activity contribute to greater risk of worsening mental health within elderly. A network pattern characterised by various types and forms of contacts is the most beneficial to mental health, and intensive contacts of limited scope alone are not sufficient to protect mental health.

Covid-19 and age discrimination: benefit maximization, fairness, and justified age-based rationing

Age-based rationing remains highly controversial. This question has been paramount during the Covid-19 pandemic. Analyzing the practices, proposals, and guidelines applied or put forward during the current pandemic, three kinds of age-based rationing are identified: an age-based cut-off, age as a tiebreaker, and indirect age rationing, where age matters to the extent that it affects prognosis. Where age is allowed to play a role in terms of who gets treated, it is justified either because this is believed to maximize benefits from scarce resources or because it is believed to be in accordance with the value of fairness understood as (a) fair innings, where less priority is given to those who have lived a full life or (b) an egalitarian concern for the worse off. By critically assessing prominent frameworks and practices for pandemic rationing, this article considers the balance the three kinds of age-based rationing strike between maximizing benefits and fairness. It evaluates whether elements in the proposals are, in fact, contrary to the justifications of these measures. Such shortcomings are highlighted, and it is proposed to adjust prominent proposals to care for the worse off more appropriately and better consider whether the acquired benefits befalls the young or the old.

How time-scale differences in asymptomatic and symptomatic transmission shape SARS-CoV-2 outbreak dynamics

Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and controllability. For example, prior work focusing on the initial exponential growth phase of an outbreak found that larger time scales for asymptomatic vs. symptomatic transmission can lead to under-estimates of the basic reproduction number as inferred from epidemic case data. Building upon this work, we use a series of nonlinear epidemic models to explore how differences in asymptomatic and symptomatic transmission time scales can lead to changes in the realized proportion of asymptomatic transmission throughout an epidemic. First, we find that when asymptomatic transmission time scales are longer than symptomatic transmission time scales, then the effective proportion of asymptomatic transmission increases as total incidence decreases. Moreover, these time-scale-driven impacts on epidemic dynamics are enhanced when infection status is correlated between infector and infectee pairs (e.g., due to dose-dependent impacts on symptoms). Next we apply these findings to understand the impact of time-scale differences on populations with age-dependent assortative mixing and in which the probability of having a symptomatic infection increases with age. We show that if asymptomatic generation intervals are longer than corresponding symptomatic generation intervals, then correlations between age and symptoms lead to a decrease in the age of infection during periods of epidemic decline (whether due to susceptible depletion or intervention). Altogether, these results demonstrate the need to explore the role of time-scale differences in transmission dynamics alongside behavioral changes to explain outbreak features both at early stages (e.g., in estimating the basic reproduction number) and throughout an epidemic (e.g., in connecting shifts in the age of infection to periods of changing incidence).

Association Between the Development of Thrombosis and Worsening of Disease Severity in Patients With Moderate COVID-19 on Admission - From the CLOT-COVID Study

BACKGROUND

The worsening of coronavirus disease 2019 (COVID-19) severity is a critical issue in current clinical settings and may be associated with the development of thrombosis.Methods and Results: This study used patient data obtained in the CLOT-COVID study, a retrospective multicenter cohort study. The demographics of patients with moderate COVID-19 on admission with and without worsened severity during hospitalization were compared and predictors were identified. Of 927 patients with moderate COVID-19 on admission, 182 (19.6%) had worsened severity during hospitalization. Patients with worsening of severity were older, more likely to have hypertension, diabetes, heart disease, and active cancer, and more likely to use pharmacological thromboprophylaxis. Patients with worsening of severity had higher D-dimer levels on admission and were more likely to develop thrombosis and major bleeding during hospitalization than those without worsening. Increased age (odds ratio [OR]: 1.02, 95% confidence interval [CI]: 1.01-1.03, P=0.005), diabetes (OR: 1.63, 95% CI: 1.11-2.33, P=0.012), D-dimer levels >1.0 μg/mL on admission (OR: 2.10, 95% CI: 1.45-3.03, P<0.001), and thrombosis (OR: 6.28, 95% CI: 2.72-14.53, P<0.001) were independently associated with worsening of COVID-19 severity.

CONCLUSIONS

Approximately 20% of patients with moderate COVID-19 had worsened severity during hospitalization. Increased age, diabetes, D-dimer levels >1.0 μg/mL on admission, and the development of thrombosis during hospitalization were significantly associated with worsened COVID-19 severity.

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.

Risk of hospitalization and vaccine effectiveness among COVID-19 patients in the UAE during the Delta and Omicron outbreaks

Introduction

A rapid increase in COVID-19 cases due to the spread of the Delta and Omicron variants in vaccinated populations has raised concerns about the hospitalization risk associated with, and the effectiveness of, COVID-19 vaccines.

Method

This case-control study aims to determine the hospitalization risk associated with the inactivated BBIBP-CorV (Sinopharm) and mRNA BNT162b2 (Pfizer-BionTech) vaccines, and their effectiveness reducing the rate of hospital admission between 28 May 2021 and 13 January 2022, during the Delta and Omicron outbreaks. The estimation of vaccine effectiveness of 4,618 samples was based on the number of patients hospitalized at different vaccination statuses, adjusted for confounding variables.

Results

Hospitalization risk increases in patients affected with the Omicron variant if patients are aged ≤ 18 years (OR 6.41, 95% CI 2.90 to 14.17; p < 0.001), and in patients affected with the Delta variant if they are aged > 45 years (OR 3.41, 95% CI 2.21 to 5.50; p < 0.001). Vaccine effectiveness reducing the rate of hospital admission for fully vaccinated participants infected with the Delta and Omicron variants was similar for both the BBIBP-CorV (94%, 95% CI 90% to 97%; 90%, 95% CI 74% to 96%) and BNT162b2 vaccines (95%, 95% CI 61% to 99.3%; 94%, 95% CI 53% to 99%), respectively.

Discussion

The BBIBP-CorV and BNT162b2 vaccines utilized in the UAE vaccination program were highly effective in reducing the rate of COVID-19-related hospitalization during the Delta and Omicron outbreaks, and further effort must be taken to achieve high vaccine coverage rates in children and adolescents in the global context to reduce the hospitalization risk associated with COVID-19 on an international scale.

[Predictions of a SEIR model for COVID-19 cases in Cali-Colombia]

OBJECTIVE

To predict the number of cases of COVID-19 in the city of Cali-Colombia through the development of a SEIR model.

METHODS

A SEIR compartmental deterministic model was used considering the states: susceptible (S), exposed (E), infected (I) and recovered (R). The model parameters were selected according to the literature review, in the case of the case fatality rate data from the Municipal Secretary of Health were used. Several scenarios were considered taking into account variations in the basic number of reproduction (R0), and the prediction until april 9 was compared with the observed data.

RESULTS

Through the SEIR model it was found that with the highest basic number of reproduction [2,6] and using the case fatality rate for the city of 2,0%, the maximum number of cases would be reached on June 1 with 195 666 (prevalence). However, when comparing the observed with the expected cases, at the beginning the observed occurrence was above the projected, but then the trend changes decreasing the slope.

CONCLUSIONS

SEIR epidemiological models are widely used methods for projecting cases in infectious diseases, however it must be taken into account that they are deterministic models that can use assumed parameters and could generate imprecise results.

Risk of mortality in COVID-19 patients: a meta- and network analysis

Understanding the most relevant hematological/biochemical characteristics, pre-existing health conditions and complications in survivors and non-survivor will aid in predicting COVID-19 patient mortality, as well as intensive care unit (ICU) referral and death. A literature review was conducted for COVID-19 mortality in PubMed, Scopus, and various preprint servers (bioRxiv, medRxiv and SSRN), with 97 observational studies and preprints, consisting of survivor and non-survivor sub-populations. This meta/network analysis comprised 19,014 COVID-19 patients, consisting of 14,359 survivors and 4655 non-survivors. Meta and network analyses were performed using META-MAR V2.7.0 and PAST software. The study revealed that non-survivors of COVID-19 had elevated levels of gamma-glutamyl transferase and creatinine, as well as a higher number of neutrophils. Non-survivors had fewer lymphocytes and platelets, as well as lower hemoglobin and albumin concentrations. Age, hypertension, and cerebrovascular disease were shown to be the most influential risk factors among non-survivors. The most common complication among non-survivors was heart failure, followed by septic shock and respiratory failure. Platelet counts, creatinine, aspartate aminotransferase, albumin, and blood urea nitrogen levels were all linked to ICU admission. Hemoglobin levels preferred non-ICU patients. Lower levels of hemoglobin, lymphocytes, and albumin were associated with increased mortality in ICU patients. This meta-analysis showed that inexpensive and fast biochemical and hematological tests, as well as pre-existing conditions and complications, can be used to estimate the risk of mortality in COVID-19 patients.

Aspects of Nanotechnology for COVID-19 Vaccine Development and Its Delivery Applications

Coronavirus, a causative agent of the common cold to a much more complicated disease such as "severe acute respiratory syndrome (SARS-CoV-2), Middle East Respiratory Syndrome (MERS-CoV-2), and Coronavirus Disease 2019 (COVID-19)", is a member of the coronaviridae family and contains a positive-sense single-stranded RNA of 26-32 kilobase pairs. COVID-19 has shown very high mortality and morbidity and imparted a significantly impacted socioeconomic status. There are many variants of SARS-CoV-2 that have originated from the mutation of the genetic material of the original coronavirus. This has raised the demand for efficient treatment/therapy to manage newly emerged SARS-CoV-2 infections successfully. However, different types of vaccines have been developed and administered to patients but need more attention because COVID-19 is not under complete control. In this article, currently developed nanotechnology-based vaccines are explored, such as inactivated virus vaccines, mRNA-based vaccines, DNA-based vaccines, S-protein-based vaccines, virus-vectored vaccines, etc. One of the important aspects of vaccines is their administration inside the host body wherein nanotechnology can play a very crucial role. Currently, more than 26 nanotechnology-based COVID-19 vaccine candidates are in various phases of clinical trials. Nanotechnology is one of the growing fields in drug discovery and drug delivery that can also be used for the tackling of coronavirus. Nanotechnology can be used in various ways to design and develop tools and strategies for detection, diagnosis, and therapeutic and vaccine development to protect against COVID-19. The design of instruments for speedy, precise, and sensitive diagnosis, the fabrication of potent sanitizers, the delivery of extracellular antigenic components or mRNA-based vaccines into human tissues, and the administration of antiretroviral medicines into the organism are nanotechnology-based strategies for COVID-19 management. Herein, we discuss the application of nanotechnology in COVID-19 vaccine development and the challenges and opportunities in this approach.

Correlates of Coronavirus Disease 2019 Inpatient Mortality at a Southern California Community Hospital With a Predominantly Hispanic/Latino Adult Population

Background

Studies of inpatient coronavirus disease 2019 (COVID-19) mortality risk factors have mainly used data from academic medical centers or large multihospital databases and have not examined populations with large proportions of Hispanic/Latino patients. In a retrospective cohort study of 4881 consecutive adult COVID-19 hospitalizations at a single community hospital in Los Angeles County with a majority Hispanic/Latino population, we evaluated factors associated with mortality.

Methods

Data on demographic characteristics, comorbidities, laboratory and clinical results, and COVID-19 therapeutics were abstracted from the electronic medical record. Cox proportional hazards regression modeled statistically significant, independently associated predictors of hospital mortality.

Results

Age ≥65 years (hazard ratio [HR] = 2.66; 95% confidence interval [CI] = 1.90-3.72), male sex (HR = 1.31; 95% CI = 1.07-1.60), renal disease (HR = 1.52; 95% CI = 1.18-1.95), cardiovascular disease (HR = 1.45; 95% CI = 1.18-1.78), neurological disease (HR = 1.84; 95% CI = 1.41-2.39), D-dimer ≥500 ng/mL (HR = 2.07; 95% CI = 1.43-3.0), and pulse oxygen level <88% (HR = 1.39; 95% CI = 1.13-1.71) were independently associated with increased mortality. Patient household with (1) multiple COVID-19 cases and (2) Asian, Black, or Hispanic compared with White non-Hispanic race/ethnicity were associated with reduced mortality. In hypoxic COVID-19 inpatients, remdesivir, tocilizumab, and convalescent plasma were associated with reduced mortality, and corticosteroid use was associated with increased mortality.

Conclusions

We corroborate several previously identified mortality risk factors and find evidence that the combination of factors associated with mortality differ between populations.

Stochastic compartmental models of the COVID-19 pandemic must have temporally correlated uncertainties

Compartmental models are an important quantitative tool in epidemiology, enabling us to forecast the course of a communicable disease. However, the model parameters, such as the infectivity rate of the disease, are riddled with uncertainties, which has motivated the development and use of stochastic compartmental models. Here, we first show that a common stochastic model, which treats the uncertainties as white noise, is fundamentally flawed since it erroneously implies that greater parameter uncertainties will lead to the eradication of the disease. Then, we present a principled modelling of the uncertainties based on reasonable assumptions on the contacts of each individual. Using the central limit theorem and Doob’s theorem on Gaussian Markov processes, we prove that the correlated Ornstein–Uhlenbeck (OU) process is the appropriate tool for modelling uncertainties in the infectivity rate. We demonstrate our results using a compartmental model of the COVID-19 pandemic and the available US data from the Johns Hopkins University COVID-19 database. In particular, we show that the white noise stochastic model systematically underestimates the severity of the Omicron variant of COVID-19, whereas the OU model correctly forecasts the course of this variant. Moreover, using an SIS model of sexually transmitted disease, we derive an exact closed-form solution for the final distribution of infected individuals. This analytical result shows that the white noise model underestimates the severity of the pandemic because of unrealistic noise-induced transitions. Our results strongly support the need for temporal correlations in modelling of uncertainties in compartmental models of infectious disease.

Risk and Protective Factors for COVID-19 Morbidity, Severity, and Mortality

The outbreak of the coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become an evolving global health crisis. Currently, a number of risk factors have been identified to have a potential impact on increasing the morbidity of COVID-19 in adults, including old age, male sex, pre-existing comorbidities, and racial/ethnic disparities. In addition to these factors, changes in laboratory indices and pro-inflammatory cytokines, as well as possible complications, could indicate the progression of COVID-19 into a severe and critical stage. Children predominantly suffer from mild illnesses due to COVID-19. Similar to adults, the main risk factors in pediatric patients include age and pre-existing comorbidities. In contrast, supplementation with a healthy diet and sufficient nutrition, COVID-19 vaccination, and atopic conditions may act as protective factors against the infection of SARS-CoV-2. COVID-19 vaccination not only protects vulnerable individuals from SARS-CoV-2 infection, more importantly, it may also reduce the development of severe disease and death due to COVID-19. Currently used therapies for COVID-19 are off-label and empiric, and their impacts on the severity and mortality of COVID-19 are still unclear. The interaction between asthma and COVID-19 may be bidirectional and needs to be clarified in more studies. In this review, we highlight the clinical evidence supporting the rationale for the risk and protective factors for the morbidity, severity, and mortality of COVID-19.

Malondialdehyde acetaldehyde adduction of surfactant protein D attenuates SARS-CoV-2 spike protein binding and virus neutralization

BACKGROUND

Over 43% of the world's population regularly consumes alcohol. Although not commonly known, alcohol can have a significant impact on the respiratory environment. Living in the time of the COVID-19 pandemic, alcohol misuse can have a particularly deleterious effect on SARS-CoV-2-infected individuals and, in turn, the overall healthcare system. Patients with alcohol use disorders have higher odds of COVID-19-associated hospitalization and mortality. Even though the detrimental role of alcohol on COVID-19 outcomes has been established, the underlying mechanisms are yet to be fully understood. Alcohol misuse has been shown to induce oxidative damage in the lungs through the production of reactive aldehydes such as malondialdehyde and acetaldehyde (MAA). MAA can then form adducts with proteins, altering their structure and function. One such protein is surfactant protein D (SPD), which plays an important role in innate immunity against pathogens.

METHODS AND RESULTS

In this study, we examined whether MAA adduction of SPD (SPD-MAA) attenuates the ability of SPD to bind SARS-CoV-2 spike protein, reversing SPD-mediated virus neutralization. Using ELISA, we show that SPD-MAA is unable to competitively bind spike protein and prevent ACE2 receptor binding. Similarly, SPD-MAA fails to inhibit entry of wild-type SARS-CoV-2 virus into Calu-3 cells, a lung epithelial cell line, as well as ciliated primary human bronchial epithelial cells isolated from healthy individuals.

CONCLUSIONS

Overall, MAA adduction of SPD, a consequence of alcohol overconsumption, represents one mechanism of compromised lung innate defense against SARS-CoV-2, highlighting a possible mechanism underlying COVID-19 severity and related mortality in patients who misuse alcohol.