COVID-19 Cases Research Articles

DIABETES MELLITUS TIPO 2 COMO FATOR DE RISCO PARA O AGRAVO DA COVID-19 NA POPULAÇÃO BRASILEIRA: UMA REVISÃO SISTEMÁTICA

INTRODUCTION: The novel coronavirus, named SARS-CoV-2, is the etiological agent of COVID-19, a highly contagious infectious disease that primarily affects the lower respiratory tract. Type 2 diabetes mellitus may be a factor worsening this condition. OBJECTIVE: To identify the prevalence of diabetes mellitus as a risk factor for COVID-19 in Brazil from 2019 to 2023, assess the severity of cases in different states, analyze the sociodemographic factors affecting prognosis, and clarify the role of pharmacists in monitoring and managing the therapy of these patients. METHODOLOGY: This is a systematic review study using the PRISMA method, divided into four stages. The first stage involved searching for scientific works to answer the question about the frequency of worsening COVID-19 cases associated with type 2 diabetes mellitus in Brazil. The databases consulted were Scielo, PubMed, and the Virtual Health Library (BVS), using keywords such as “Diabetes mellitus,” “COVID-19,” “Brazil,” “Worsening,” and “Death.” RESULTS: Seveteen articles that met the inclusion criteria were selected. The studies indicate that diabetes mellitus is a significant risk factor for the severity and mortality of COVID-19 in Brazil, with mortality rates between 40% and 55% among diabetic patients. Additionally, the disease is associated with other comorbidities, such as hypertension and obesity, contributing to the need for ICU admissions and longer hospital stays. CONCLUSION: Type 2 diabetes mellitus is a significant risk factor for complications and mortality in COVID-19 patients, resulting in higher ICU admission rates and deaths, highlighting the need for rigorous glycemic control. The pharmacist's role is essential in monitoring glycemic levels and educating patients about treatment, contributing to reducing complications and improving clinical outcomes.

Subjective well-being and mental health during the COVID-19 pandemic in Hungary

Purpose This study aims to examine the impact of COVID-19 pandemics on subjective well-being (SWB) and mental health in Hungary, with a particular focus on the roles of socioeconomic and COVID-19-related factors in shaping these outcomes. Design/methodology/approach A cross-sectional online survey was conducted among 2,000 Hungarian adults from May 25 to June 8, 2021. The data was collected using standardized questionnaires, including the SWLS, GAD-7 and PHQ-9. Correlation analysis, one-way ANOVA and linear regression were used. Findings The study found significant anxiety (41.5%), depression (48.1%) and life dissatisfaction (41.9%) among 2,000 participants. Females reported higher anxiety and depression than males. Single individuals had the lowest life satisfaction. Higher income levels are correlated with greater life satisfaction. Quarantine and vaccination were linked to higher life satisfaction and lower psychological distress. The regression showed that older age, being married and higher income were linked to lower depression and anxiety. Severity of infection and quarantine increased both depression and anxiety. Infection reduced life satisfaction, but severe cases showed a slight improvement in satisfaction, highlighting complex health and well-being relationships. Practical implications The findings reveal complex relationships, including unexpected resilience in severe COVID-19 cases. Originality/value This study offers insights into the impact of the COVID-19 pandemic on SWB and mental health in Hungary. By analyzing socioeconomic, demographic and pandemic-related factors in a large sample, it highlights complex relationships between quarantine, infection severity, vaccination and well-being. The findings contribute to understanding how these factors influence mental health, informing targeted interventions during health crises.

Relation of CXCL10 and CXCR3 Gene Polymorphisms with COVID-19 Severity and its Serum Biochemical Markers

CXCL10 (rs201830102) is a chemokine involved in immune cell recruitment, while its receptor, CXCR3 (rs779120264), mediates immune responses through the activation of T cells. These genes are critical in the immune response to viral infections, including COVID-19. The study aimed to explore the relationship between polymorphisms in the CXCL10 gene and CXCR3 receptor with disease severity in COVID-19 patients. In this cross-sectional analytical study, 100 COVID-19 patients were enrolled after ethical approval, and written informed consent was obtained from each participant. Polymorphisms in CXCL10 and CXCR3 were analyzed by sequencing, while biochemical and hematological parameters were assessed using appropriate methods. Descriptive and inferential statistics were employed to analyze continuous and categorical data. Significant associations were observed between severe COVID-19 cases and elevated levels of serum D-dimer, ferritin, random blood sugar (RBS), neutrophils, and erythrocyte sedimentation rate (ESR), along with reduced hemoglobin levels. Lymphocyte and platelet counts were significantly lower with increased disease severity. The wild genotype of CXCL10 was notably associated with elevated ferritin levels, suggesting that certain gene variants may offer protective effects. However, no significant correlation was found between CXCR3 and CXCL10 polymorphisms and other serum biomarkers. Our study confirmed a significant rise in serum D-dimer, ferritin, RBS, neutrophils, and ESR, along with a reduction in hemoglobin, lymphocyte, and platelet counts in severe COVID-19 cases compared to mild ones. Notably, mutations in the CXCL10 gene were linked to less severe COVID-19 outcomes.

Evaluation of Suppressor of Cytokine Signaling 1, Interleukin-29 and Lysosomal Trafficking Regulator in Severe COVID-19

Background: The pathogenesis of Severe SARS-CoV-2 is closely linked to severe immune responses and inflammation caused by the SARS-CoV-2 virus. In this context, the suppressor of cytokine signaling 1 (SOCS1) has a crucial role in inhibiting cytokine-induced immune responses. On the other hand, interleukin-29 (IL-29) and lysosomal trafficking regulator (LYST) are important molecules involved in inducing immune responses. Objectives: This study aimed to assess the mRNA levels of SOCS1, IL-29, and LYST in the SARS-CoV-2-infected patients with severe symptoms. Methods: In this cross-sectional study, 70 SARS-CoV-2 infected patients with severe symptoms and 70 healthy controls were evaluated. RNA was extracted from peripheral blood and after cDNA synthesis, the mRNA levels of SOCS1, IL-29, and LYST were assessed by Real-Time PCR technique Results: The study revealed that severe COVID-19 patients exhibited a significant increase in mRNA levels of IL-29 compared to healthy individuals. However, there were no observed alterations in the mRNA levels of SOCS1 and LYST in the patient group. Conclusions: The results emphasize the importance of IL-29 as a potential biomarker or therapeutic target for severe COVID-19 cases. Further research is needed to investigate the specific mechanisms through which IL-29 influences immune responses and contributes to the development of severe disease. Additionally, exploring other factors that may regulate SOCS1 and LYST expression could provide a more comprehensive understanding of their roles in COVID-19 pathogenesis.

Coming out of isolation: impacts of COVID-19 on physical activity, diet, mental well-being, and sleep over time

IntroductionThe COVID-19 pandemic lockdown significantly disrupted daily routines and impacted physical activity, diet, mental well-being, and sleep. This mixed-methods study investigates these changes over three periods—pre-pandemic, pandemic onset, and one-year post-onset—to understand their causes and inform public health policy for improved resilience during future crises.MethodsA mixed-methods study was conducted with 34 US participants who completed open-ended qualitative questions and quantitative assessments in May 2020 and May 2021. Participants were recruited via social media from 10 states with high COVID-19 cases. Quantitative data categorized health changes (positive, negative, or no change), while qualitative data were analyzed thematically to explore reasons for these changes and uncover interrelationships among health behaviors.ResultsQuantitative results showed that, during the pandemic onset, most participants experienced negative changes in diet, mental well-being, and sleep, while physical activity varied. By one-year post-onset, improvements were noted across all health pillars, with mental well-being and sleep showing the most significant positive changes. Thematic analysis of qualitative data revealed stress, anxiety, and personal motivations as key drivers of health behaviors. Participants’ narratives provided a deeper understanding of these shifts that a Likert-style survey alone could not capture, highlighting the interconnectedness of health pillars.DiscussionThe findings demonstrate the importance of addressing mental well-being to improve overall health resilience. Public health interventions should prioritize mental health and consider the interrelated nature of health behaviors. The mixed-methods approach combined quantitative trends with qualitative insights, offering a comprehensive understanding of why health changes occurred, providing actionable guidance for future public health policy.

Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique

Deep learning and artificial intelligence offer promising tools for improving the accuracy and efficiency of diagnosing various lung conditions using portable chest x-rays (CXRs). This study explores this potential by leveraging a large dataset containing over 6,000 CXR images from publicly available sources. These images encompass COVID-19 cases, normal cases, and patients with viral or bacterial pneumonia. The research proposes a novel approach called "Enhancing COVID Prediction with ESN-MDFS" that utilizes a combination of an Extreme Smart Network (ESN) and a Mean Dropout Feature Selection Technique (MDFS). This study aimed to enhance multi-class lung condition detection in portable chest X-rays by combining static texture features with dynamic deep learning features extracted from a pre-trained VGG-16 model. To optimize performance, preprocessing, data imbalance, and hyperparameter tuning were meticulously addressed. The proposed ESN-MDFS model achieved a peak accuracy of 96.18% with an AUC of 1.00 in a six-fold cross-validation. Our findings demonstrate the model’s superior ability to differentiate between COVID-19, bacterial pneumonia, viral pneumonia, and normal conditions, promising significant advancements in diagnostic accuracy and efficiency.

COVID-19 inflammatory signature in a Mozambican cohort: unchanged red blood series and reduced levels of IL-6 and other proinflammatory cytokines

Summary Background Alterations in haematological, biochemical parameters and cytokine levels, were reported in patients with COVID-19, however, there is an underrepresentation of the African population, which could provide evidence for understanding SARS-CoV-2 pathogenesis and useful tools for clinical management of cases. In this study, we aimed to determine the haematological, biochemical and cytokine profile in Mozambican individuals with SARS-CoV-2. Methods A cohort of 85 Mozambican individuals with RT-PCR SARS-CoV-2 results, was stratified into negative, asymptomatic, mild, moderate, and severe categories. Haematological, biochemical and cytokines measurement were performed on samples from the study participants. Principal component analysis (PCA) was performed to identify similar patterns among the study cases. Comparisons between groups were performed using the Kruskal-Wallis test. Receiver operating characteristic (ROC) and area under the curve (AUC) analysis were conducted to evaluate the ability of these parameters to distinguish severe from non-severe cases of SARS-CoV-2 infection. Results SARS-CoV-2 infection was associated with a significant (p < 0.05) decrease in peripheral blood absolute counts of total lymphocytes and eosinophils, below the reference values along with no abnormal change (p > 0.05) in red blood cell count, haemoglobin, platelets and other red series parameters. At the serum level, SARS-CoV-2 infection was associated with an increase in serum levels of C-reactive protein (C-RP) and glucose above the reference values and to a significant reduction a significant (p < 0.05) reduction in levels of interferon-gamma (INF-γ), Tumour Necrosis Factor alfa (TNF-α) and the interleukin 1 beta (IL-1β) and IL-6 in severe cases, when compared to negative cases. Haematological, biochemical and cytokine profiles segregate severe from non-severe cases of COVID-19 with an excellent performance of C-RP (AUC = 0.95; p < 0.001) and good performance of lymphocytes (AUC = 0.88; p < 0.001) and IL-15 (AUC = 0.86; p < 0.001). Conclusion The lack of variation in red and platelet series, coupled with a decrease in the levels of classical pro-inflammatory in severe cases, deviates from what has been reported in other contexts suggesting, that there may be peculiarities in COVID-19 manifestation within the context of this study population. Furthermore, these results identify parameters with potential for clinical management of COVID-19 and therefore good resource allocation, particularly for severe cases.

Clinical Characteristics and Outcomes of Cardiac Findings in Young Persons Following SARS-CoV-2 Infection

We studied cardiac complications in young persons with severe acute respiratory syndrome coronavirus 2. In a prospective cohort of 127 Military Health System beneficiaries 0–22 years old, 3.1% had cardiac abnormalities, all resolved within 6 months. Our findings support guidelines against routine cardiac screening in mild COVID-19 cases without cardiac symptoms.

The Study of Lung Ultrasound, Chest CT and Chest X-ray in Children with COVID-19 Hospitalized in Mofid Children's Hospital During a Six-Month Period

Background: Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The primary diagnostic tool for pediatric COVID-19 patients is polymerase chain reaction (PCR). Chest imaging findings in pediatric COVID-19 cases are often normal or mild. The correlation of lung ultrasound (LUS) with chest computed tomography (CT) and chest X-ray (CXR) in pediatric COVID-19 patients has not been extensively studied. Objectives: This study aimed to assess the correlation of LUS with chest CT and CXR in detecting COVID-19 pneumonia in children. Methods: This single-center cross-sectional study included patients under 18 years of age diagnosed with COVID-19 by PCR or by abnormal chest CT findings suggestive of COVID-19, admitted to Mofid Pediatric Hospital between December 2021 and August 2022. All patients underwent LUS. Approximately half of the patients also had a chest CT, and CXR was performed on 35 patients. Lung ultrasound and CT scores were calculated, and the correlation between these scores was evaluated. The correlation between LUS and CXR was also analyzed. Results: Sixty patients were included, of whom 21 were female, with a mean age of 4.9 ± 4.0 years. A significant correlation was observed between LUS and CT scores (correlation coefficient = 0.467, P = 0.011). Lesion distribution was similar between LUS and CT. However, no significant correlation was found between LUS scores and CXR findings (P = 0.392). Sixteen out of 19 patients with normal CXRs had LUS scores ≤ 4. Notably, three patients with normal CXRs had LUS scores of 8, 14, and 14. Conclusions: Lung ultrasound was more sensitive than CXR and demonstrated a significant correlation with CT. Lung ultrasound may serve as a safe alternative to CT for detecting COVID-19 pneumonia in pediatric patients.

Looking back at Covid-19 government restrictions: were local lockdown regions with tighter restrictions less adherent before the restrictions and more adherent after?

Abstract It is assumed that increases in Covid-19 cases are caused by people not adhering to advised individual transmission-reducing behaviours. Upon the implementation of restrictions, the hypothesis is that those individuals will change their behaviour. We aimed to retrospectively explore adherence to physical distancing before and after restrictions (e.g., lockdowns) were implemented in a region of Scotland. We assessed adherence, intention, and self-efficacy to physical distancing in a series of cross-sectional telephone surveys of a representative sample of adults in Scotland. We included data from before regional restrictions and after restrictions and examined whether regions with and without restrictions differed in adherence. A total of 1724 Scottish adults (675 men, M age = 52.79 years, SD = 17.92) participated (879 (51.0%) pre-restriction, 466 (27.0%) from a restricted region). ANOVA showed that none of the main effects (for region or time) nor the interaction effect were significant. There was a main effect of time on self-efficacy, such that self-efficacy was lower post-restriction measures (M = 4.13, SD = 0.81) compared to pre-restriction time (M = 4.22, SD = 0.79). There was no evidence that adherence was weaker before restrictions were implemented in regions with higher case rates. Nor was there evidence that imposing restrictions increased adherence. In a future pandemic, it is advisable to assess behaviour and beliefs about Covid-19, risk, and behaviours on an ongoing basis and to use that as indicators of the need for intervention even before cases rates start to go up.

Differential Inflammatory and Immune Response to Viral Infection in the Upper-Airway and Peripheral Blood of Mild COVID-19 Cases

Background/Objectives: COVID-19 is characterised by a wide variety of clinical manifestations, and clinical tests and genetic analysis might help to predict patient outcomes. Methods: In the current study, the expression of genes related to immune response (CCL5, IFI6, OAS1, IRF9, IL1B, and TGFB1) was analysed in the upper airway and paired-blood samples from 25 subjects infected with SARS-CoV-2. Relative gene expression was determined by RT-qPCR. Results: CCL5 expression was higher in the blood than in the upper airway (p < 0.001). In addition, a negative correlation was found between IFI6 and viral load (p = 0.033) in the upper airway, suggesting that the IFI6 expression inhibits the viral infection. Concerning sex, women expressed IL1B and IRF9 in a higher proportion than men at a systemic level (p = 0.008 and p = 0.049, respectively). However, an increased expression of IRF9 was found in men compared to women in the upper airway (p = 0.046), which could be due to the protective effect of IRF9, especially in men. Conclusions: The higher expression of CCL5 in blood might be due to the key role of this gene in the migration and recruitment of immune cells from the systemic circulation to the lungs. Our findings confirm the existence of sex differences in the immune response to early stages of the infection. Further studies in a larger cohort are necessary to corroborate the current findings.

Epidemiological analysis of the incidence of COVID-19 among the population of the Republic of Cameroon using the quantum Geographic Information System (QGIS)

The results of a retrospective epidemiological analysis of the incidence of COVID-19 in the regions of the Republic of Cameroon in dynamics from 2020 to 2023 are presented. It was noted that COVID-19 cases were first reported in the Central Region of Cameroon, and then spread to the remaining 9 administrative regions of the country with an average weekly growth rate of 38.1 ± 18.6% in 2020, 15.3 ± 13.1% in 2021, and 13.4 ± 12.2% in 2022, respectively, however, the incidence rate varied significantly in time and space. Epidemiological analysis made it possible to rank the territory of Cameroon by the magnitude of the incidence rate. The highest rate was recorded in the North region (233.1‰), in the Coastal region (204.9‰) and in the Center region (173.7‰). The regions of Adamawa and the Far North had the lowest incidence rates (25.6‰ and 22.5‰, respectively). The results of a comparative analysis of the incidence of COVID-19 infection by time and territory and factors contributing to the rapid spread of infection are presented. Comparative mapping of reported COVID-19 cases in space and time was performed for the 10 administrative regions of the country using the full version of the QGIS (quantum GIS) LTR program; with the help of which the distribution of cases across the territory and dynamics over time was clearly shown. During the study period, the link between COVID-19 cases reported in Cameroon and neighboring African countries was demonstrated. It has been shown that the distribution of cases in the national territory does not always depend on the population density in administrative areas, but is also associated with other risk factors that may affect the occurrence and spread of COVID-19 cases.

Investigating the Prevalence of Bacterial Infections in Patients with Coronavirus Disease 2019 Hospitalized in Intensive Care Unit and Determining their Antibiotic Resistance Patterns

Background: COVID-19 patient hospitalization, particularly in intensive care units, exposes them to bacterial and fungi co-infections, which can have very serious conse-quences, including increased mortality. In addition, antibiotic resistance among pathogens is a hidden threat behind COVID-19. Methods: In the period from 2020 September to 2021 August, bacterial isolates from COVID-19 patients admitted to the ICU of Sina Hospital in Hamadan, Iran, were collected and iden-tified based on standard biochemical tests. COVID-19 cases were confirmed based on clinical symptoms, computed tomography, and polymerase chain reaction. Antimicrobial susceptibil-ity tests were conducted using disc diffusion and broth microdilution methods. Results: In total, 207 bacterial isolates were collected, with Klebsiella pneumoniae account-ing for 69 (33.33%) and Acinetobacter baumannii accounting for 59 (28.15%). The frequency and percentage of isolated bacteria were as follows: Alcaligenes species 28 (13.59%), Staph-ylococcus aureus 18 (8.73%), Pseudomonas aeruginosa 15 (7.28%), Escherichia coli 11 (5.33%), Stenotrophomonas maltophilia 3 (1.45%), Enterococcus species 3 (1.45%), and Ser-ratia species 1 (0.48%). About 95.38% resistance to ceftazidime and cefotaxime and 92.31% resistance to ciprofloxacin and cefepime were found in K. pneumoniae isolates. A. baumannii isolates were 100% resistant to cefotaxime, ceftriaxone, and cefepime. About 22.22% re-sistance to vancomycin and 66.67% resistance to clindamycin, erythromycin, and cefoxitin were seen in S. aureus isolates. Conclusion: Knowledge of bacterial co-infections and their antibiotic resistance pattern in COVID-19 patients can help in choosing effective antibiotics for the treatment and prevention of antibiotic resistance.

Detection of SARS-CoV-2 in wastewater as an earlier predictor of COVID-19 epidemic peaks in Venezuela

Wastewater-based epidemiological surveillance has proven to be a useful and cost-effective tool for detecting COVID-19 outbreaks. Here, our objective was to evaluate its potential as an early warning system in Venezuela by detecting SARS-CoV-2 RNA in wastewater and its correlation with reported cases of COVID-19. Viral RNA was concentrated from wastewater collected at various sites in Caracas (northern Venezuela), from September 2021 to July 2023, using the polyethylene glycol (PEG) precipitation method. Viral quantification was performed by RT-qPCR targeting the N1 and ORF1ab genes. A significant association (p < 0.05) was found between viral load in wastewater and reported cases of COVID-19 up to six days after sampling. During the whole study, two populated areas of the city were persistent hotspots of viral infection. The L452R mutation, suggestive of the presence of the Delta variant, was identified in the only sample where a complete genomic sequence could be obtained. Significant differences (p < 0.05) between the physicochemical conditions of the wastewater samples positive and negative for the virus were found. Our results support proof of concept that wastewater surveillance can serve as an early warning system for SARS-CoV-2 outbreaks, complementing public health surveillance in those regions where COVID-19 is currently underreported.

Big Data Analytics for Forecasting Tourism Recovery in Bali Island Using Multivariate Time Series

Bali is a famous tourist area and can significantly contribute to the Indonesian tourism sector. The COVID-19 pandemic has made Indonesian tourism, including Bali tourism, experience a decline. In March 2022, COVID-19 cases decreased, and the government began to relax some policies. The tourism sector is vital in economic recovery efforts after the COVID-19 pandemic. Therefore, it is necessary to identify tourism recovery to determine strategies and policies related to Indonesian tourism, especially in Bali. Multivariate time series forecasting of tourism demand can be used to identify tourism recovery using several significant data sources. The methods used are Vector Autoregressive (VAR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data used are the monthly official number of tourists, room occupancy rate, Google Trends, number of booking.com user reviews, and nighttime light intensity in Bali Province from January 2019 to December 2022. The results show that the best forecasting method is VAR, and modeling with multivariate time series forecasting can improve the performance of forecasting results. In addition, big data can be used as a source of supporting data that can provide better forecasting results, and the size of the dataset affects the selection of the best model. Furthermore, the descriptive and forecasting analysis results show that Bali tourism has experienced post-pandemic tourism recovery. The strategies and policies of the Bali government to restore Bali tourism faster are good enough.

Comparison of clinical characteristics and outcomes between COVID-19 survivors and non-survivors: a retrospective observational study

Background To compare the clinical characteristics of COVID-19 survivors and non-survivors who were transferred from general wards to the critical care units in four tertiary hospitals of Nepal. Methods This study utilized electronic data from the National Intensive Care Unit (ICU) registry managed by the Nepal Intensive Care Research Foundation (NICRF). A retrospective observational study was conducted among 78 eligible COVID-19 patients admitted to the intensive care units of four different hospitals between 2020 and 2022. The Mann-Whitney U test was used to compare each continuous variable between the survivors and non survivors, while Pearson's chi-squared test was used to examine the association between each categorical variable and outcome. Results Among 78 cases of COVID-19 studied, 29 (37.1%) had died, and 49 (62.8%) had recovered. The median age of the patient was 57.5 years, with a higher percentage of males (66.6%). Hypertension (41%) and diabetes (26.9%) were common comorbidities, whereas pneumonia (41%) and acute respiratory distress syndrome (19.2%) were common complications. Most patients required oxygen therapy (96.2%). Corticosteroids (76.9%) and anticoagulants (83.3%) were commonly administered medications. Median of mean arterial pressure was 85 mm Hg [ IQR- 79.2 - 99.5] in non-survivors and 93.3 mm Hg [IQR- 86.6 - 102.6 ] in survivors which was significantly different between the two groups (p=0.04). Nine of the patients had cardiac dysfunction on ICU admission, of which none survived (p=0.001). Out of ten patients requiring inotropes or vasopressors, only two survived (p=0.001). Conclusions Non-survivor COVID patients had lower mean arterial pressure on admission to intensive care units. A higher proportion of patients with cardiac dysfunction and requiring inotropes or vasopressors could not survive.

Scale-specific effects of urban landscape pattern on the COVID-19 epidemic in Hangzhou, China

ContextUnderstanding the scale-specific effects of different landscape variables on the COVID-19 epidemics is critical for developing the precise and effective prevention and control strategies within urban areas.ObjectiveBased on the landscape epidemiology framework, we analyzed the scale-specific effects of urban landscape pattern on COVID-19 epidemics in Hangzhou, China.MethodsWe collected COVID-19 cases in Hangzhou from 2020‒2022 and combined the datasets of land use and land cover (LULC) and social gathering point (SGP) to quantify the urban landscape pattern. Optimal general linear model with stepwise regression was applied to explore the dominant landscape factors driving the COVID-19 transmission in the city. Furthermore, multi-scale geographically weighted regression illustrated the spatial heterogeneity and scale specificity of these landscape variables’ effects to COVID-19 epidemic.ResultsEight landscape variables of LULC and SGP patterns were identified which explained 68.5% of the variance in spatial risk of COVID-19. Different optimal bandwidths across these variables in MGWR indicated their scale-specific effects. LSI of green space enhanced the spatial risk across the entire region. The effects of landscape contagion, the number of water bodies, LSI of cropland and built-up areas, and the density of commercial houses were detected to vary between urban and suburban areas. The effects of LSI of water bodies and the density of shopping malls were found to vary among different districts.ConclusionsIn this study, we firstly discriminated the scale-specific effects of different landscape variables on the COVID-19 epidemic in the urban region. These findings can help to optimize the differentiated zoning prevention and control strategies for COVID-19 in cities and guide policy-making and urban planning at a multi-scale hierarchical perspective to improve public health and urban sustainability.

A study of the enablers and barriers to the collection of sociodemographic data by public health units in Ontario, Canada during the COVID-19 pandemic

BackgroundCollection and use of sociodemographic data (SDD), including race, ethnicity and income, are foundational to understanding health inequities. Ontario’s public health units collected SDD as part of COVID-19 case management and vaccination activities. This research aimed to identify enablers and barriers to collecting SDD during COVID-19 case management and vaccination.MethodsAs part of a larger mixed-method research study [1], qualitative methods were used to identify enablers and barriers to SDD collection during the COVID-19 pandemic. Purposive sampling was used to recruit participants from Ontario’s 34 public health units. Sixteen focus groups and eight interviews were conducted virtually using Zoom. Interview data were transcribed and analyzed using inductive and deductive qualitative description.ResultsSDD collection enablers included: legally mandating SDD collection and having dedicated data systems, technological and legal supports, senior management championing SDD collection, establishing rapport and trust between staff and clients, and gaining insight from the experiences from local communities and other jurisdictions. Identified barriers to SDD collection included: provincial data systems being perceived as lacking user-friendliness, SDD collection “was not a priority,” time and other constraints on building staff and client rapport, and perceived discomfort with asking and answering personal SDD questions.ConclusionA combination of provincial and local organizational strategies including supportive data systems, training, and frameworks for data collection and use, are needed to normalize and scale up SDD collection by local health units beyond the context of the COVID-19 pandemic.

Transient anti-interferon autoantibodies in the airways are associated with recovery from COVID-19.

Preexisting anti-interferon-α (anti-IFN-α) autoantibodies in blood are associated with susceptibility to life-threatening COVID-19. However, it is unclear whether anti-IFN-α autoantibodies in the airways, the initial site of infection, can also determine disease outcomes. In this study, we developed a multiparameter technology, FlowBEAT, to quantify and profile the isotypes of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and anti-IFN-α antibodies in longitudinal samples collected over 20 months from the airways and blood of 129 donors spanning mild to severe COVID-19. We found that nasal IgA1 anti-IFN-α autoantibodies were induced after infection onset in more than 70% of mild and moderate COVID-19 cases and were associated with robust anti-SARS-CoV-2 immunity, fewer symptoms, and efficient recovery. Nasal anti-IFN-α autoantibodies followed the peak of host IFN-α production and waned with disease recovery, revealing a regulated balance between IFN-α and anti-IFN-α response. In contrast, systemic IgG1 anti-IFN-α autoantibodies appeared later and were detected only in a subset of patients with elevated systemic inflammation and worsening symptoms. These data reveal a protective role for nasal anti-IFN-α in the immunopathology of COVID-19 and suggest that anti-IFN-α autoantibodies may serve a homeostatic function to regulate host IFN-α after viral infection in the respiratory mucosa.

Clinical characteristics and antibody response to Omicron variants among solid carcinoma patients in China on the 2022.12–2023.4 wave of the COVID-19 pandemic

BackgroundChina experienced a surge of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variants after adjusting its zero-coronavirus disease 2019 (COVID-19) policy. Although infections with Omicron variants are generally less severe than infections with previous SARS-CoV-2 variants, the clinical characteristics, persistent symptoms, and antibody responses in solid carcinoma patients (SCPs) with COVID-19 during the Omicron wave are unclear.MethodsWe conducted a cross-sectional study in April 2023, recruiting healthy controls (HCs) from the community and SCPs from Zhejiang Provincial People’s Hospital. Serum samples were collected, and a questionnaire was used to assess SARS-CoV-2 infection status, including demographic characteristics, clinical manifestations, and “long COVID” symptoms. Humoral immune responses were analyzed by enzyme-linked immunosorbent assays (ELISAs) targeting immunoglobulin G (IgG) antibodies against the receptor-binding domain (RBD; Omicron BA.4/5) protein and cell culture-based neutralization assays against Omicron variants (BA.4/5, BF.7, XBB.1.5, and EG.5).ResultsIn total, 298 SCPs and 258 HCs were enrolled. Self-reported COVID-19 case rates were significantly lower in SCPs than in HCs (78.5% vs. 93.8%, P&amp;lt;0.001). Common COVID-19 symptoms were similar between the two groups, primarily comprising general (92.6% vs. 84.9%) and respiratory symptoms (51.9% vs. 48.2%) after acute infection. There was no significant difference in persistent symptoms at 1–3 months post-infection (P=0.353); fatigue was the most common symptom (45.0% vs. 44.8%). SCPs exhibited lower anti-RBD-IgG titers compared with HCs (1.061 vs. 1.978, P=0.001). The 50% pseudovirus neutralization titer (pVNT50) values for prevalent Omicron strains (BA.4/5 and BF.7) were lower in SCPs than in HCs (621.0 [288.8, 1333.0] vs. 894.1 [458.5, 1637.0] and 529.6 [215.3, 1264.5] vs. 463.1 [185.2, 914.0], respectively). Levels of antibodies against subsequent variants (XBB.1.5 and EG.5) also were reduced. There were no significant differences among carcinoma types in the levels of antibodies against Omicron variants. However, SCPs who received the SARS-CoV-2 vaccine or had COVID-19 during the Omicron wave displayed higher antibody levels.ConclusionsThis study elucidated the clinical and immunological characteristics of SCPs during the Omicron wave in China after the shift away from a zero-COVID-19 policy. Our findings provide insights regarding factors that influence COVID-19 symptoms and antibody levels in this population.