COVID-19 Cases Research Articles

Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms.

Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elderly and those with underlying health issues like chronic diseases. This study aimed to develop a machine learning model to improve the prediction of COVID-19 in patients with acute respiratory symptoms. Data from 915 patients in two hospitals in Saudi Arabia were used, categorized into four groups based on chronic lung conditions and COVID-19 status. Four supervised machine learning algorithms-Random Forest, Bagging classifier, Decision Tree, and Logistic Regression-were employed to predict COVID-19. Feature selection identified 12 key variables for prediction, including CXR abnormalities, smoking status, and WBC count. The Random Forest model showed the highest accuracy at 99.07%, followed by Decision Tree, Bagging classifier, and Logistic Regression. The study concluded that machine learning algorithms, particularly Random Forest, can effectively predict and classify COVID-19 cases, supporting the development of computer-assisted diagnostic tools in healthcare.

Assessing the Efficacy and Challenges of Tofacitinib in the Management of COVID-19: A Systematic Review and Meta-analysis of Cohort Studies

Objectives: Global healthcare faces challenges in combating COVID-19, with rising cases despite widespread vaccination. Severe COVID-19 cases, marked by acute respiratory distress and cytokine release syndrome, highlight the importance of managing cytokine storms. Janus kinase (JAK) inhibitors, such as tofacitinib, show promise in this regard. While tofacitinib is recommended for severe cases, challenges include adverse effects, conflicting studies, and the need for further investigation of new virus strains. Overcoming these hurdles is crucial for developing an effective treatment protocol and reducing COVID-19 mortality. Methods: This study conducted a comprehensive search across PubMed, Scopus, and ISI Web of Science for observational studies on tofacitinib treatment in human adults with COVID-19. The search covered a specified period up to 2024. Data extraction, including study characteristics and quality assessment, employed the Newcastle Ottawa Scale and a modified Cochrane tool. Statistical analysis, conducted with Comprehensive Meta-Analysis Software, assessed heterogeneity and significance levels. Results: The meta-analysis of the three studies showed a significant reduction in mortality (Risk Ratio: 0.372, 95% CI: 0.213–0.649, P-value = 0.001) with low heterogeneity (Cochrane P-value = 0.793), while no significant reduction in the need for mechanical ventilation was observed (Cochrane P-value = 0.194). Conclusion: Tofacitinib administration shows a significant reduction in COVID-19 mortality. However, the limited studies on its efficacy highlight the need for cautious interpretation in clinical assessments.

Revisiting COVID-19 Maps with Bayesian Hierarchical Modelling: Socioeconomic Insights from Istanbul

Abstract Objectives This study explores the spatial disparities in COVID-19 incidence across neighborhoods in Uskudar, Istanbul, and examines the relationship between these disparities and neighborhood-level socioeconomic status. Methods This ecological study analyzed cumulative COVID-19 case data for 33 neighborhoods in Uskudar, collected up to 31 December 2020. Socioeconomic status was assessed using the Socioeconomic Development Index (SEDI) scores from Istanbul Metropolitan Municipality. Spatial analysis included Moran’s I statistics and Local Indicators of Spatial Association (LISA) cluster analysis. Bayesian Hierarchical Modeling with the Besag-York-Mollie (BYM) model was employed to decompose spatially structured and unstructured variations, adjusting for SEDI scores and their sub-components. Model validation involved convergence diagnostics and Monte Carlo error assessments. Results A total of 29,834 COVID-19 cases were identified, with an incidence rate of 56.1 per 1000 residents. Spatial analysis revealed significant clustering of high incidence rates in peripheral and inner city neighborhoods. Moran’s I statistic for COVID-19 incidence was 0.331 (p = 0.003), indicating positive spatial autocorrelation. SEDI scores were also spatially autocorrelated (Moran’s I = 0.293; p = 0.005) and negatively correlated with COVID-19 incidence (r = -0.570; p = 0.001). Bayesian hierarchical models confirmed the association between lower socioeconomic status and higher COVID-19 incidence, even after adjusting for spatial autocorrelation. Conclusions The study highlights significant spatial and socioeconomic disparities in COVID-19 incidence in Uskudar, Istanbul. Lower socioeconomic status is associated with higher COVID-19 incidence, underscoring the need for region-specific public health strategies that consider socioeconomic factors. These findings can guide future interventions to mitigate the impact of the pandemic on vulnerable populations. Key messages • Study reveals COVID-19 incidence in Uskudar is significantly influenced by socioeconomic status, highlighting need for targeted health interventions. • Positive spatial autocorrelation found in COVID-19 cases and socioeconomic index suggests clustering in specific Uskudar neighborhoods.

The negative health assessment and the different consumption of dietary supplements

Abstract Background There is an insufficient level of research carried on the consumption of dietary supplements among diverse social and demographic groups, especially among those with a negative assessment of personal health. Therefore, the aim of this study was to assess the consumption of dietary supplements among working-age residents of Lithuania in the period from 2021 to 2023 with respect to social and demographic factors and the assessment of personal health. Materials and methods The data for this study were collected after conducting three independent cross-sectional surveys in 2021, in 2022, and in 2023. Each of the samples included 1600 working-age adult residents of Lithuania. Distribution of the respondents by the assessment of personal health and gender, age, marital status, education level, place of residence, employment, income, and COVID-19 cases among the respondents and members of their families was assessed. Results The consumption of dietary supplements differed each year and accounted for 78.1%, 71.6%, and 72.7% of the respondents, respectively (p < 0.05). In 2022, the prevalence of the consumption of dietary supplements was lower in the majority of social and demographic groups (p < 0.05). In 2023, it was higher among females, younger residents, and those from larger families, who suffered from COVID-19 (p < 0.05). Despite similar changes found in the consumption of dietary supplements among those who negatively assessed their health, this group showed more prevalent consumption of dietary supplements among residents with non-university education, unemployed respondents, and those with lower income (p < 0.05). Conclusions Despite a significantly lower prevalence in the consumption of dietary supplements in 2022, it was higher again in 2023. A negative assessment of personal health is associated with opposite tendencies in the prevalence of the consumption of dietary supplements in the general population. Key messages • Despite a significantly lower prevalence in the consumption of dietary supplements in 2022, it was higher again in 2023. • The negative assessment of personal health should be taken into account when planning nutritional interventions due to its association with different consumption of dietary supplements.

The Effect of the COVID-19 Pandemic on the Working Conditions and Efficiency of Radiographers at Ahvaz Jundishapur University of Medical Sciences' Radiology and CT Scan Imaging Centers

Background: Radiographers are highly skilled imaging technology users who work directly with COVID-19 cases. Objectives: This study aims to assess the impact of COVID-19 on the working conditions and efficiency of radiographers and their level of satisfaction with personal protective equipment (PPE), cleaning supplies, and facilities during the COVID-19 epidemic. Methods: A prospective observational study was conducted among radiographers registered in the radiology and CT scan imaging centers of Ahvaz Jundishapur University of Medical Sciences and some private centers between June and October 2022. A questionnaire with 28 multiple-choice questions was used to collect information. The questionnaire consisted of two sections; the first part contained demographic information. In the second part, participants answered questions about changes in working conditions, such as changes in stress level, work efficiency, the need for more rest, imaging time, use of equipment, and personal protective equipment at work, based on their experiences and observations during the COVID-19 pandemic. The data analysis was performed using Statistical Product Service Solutions (SPSS) software, version 26.0, with statistical significance assumed at a P-value < 0.05. Results: One hundred and fifty radiographers completed the questionnaire. The mean age and mean work experience of participants were 33.2 ± 8.3 years and 9 ± 7.2 years, respectively. The results showed that most participants of both sexes agreed that they needed more rest (P-value < 0.05); their work efficiency had not decreased significantly during the COVID-19 epidemic. The stress level of participants had significantly increased compared to before the epidemic. There were significant differences between government-owned hospitals and private radiology centers regarding the presence of a COVID-19-related protocol, the compulsory use of masks, the existence of decontamination instructions, the type of air decontamination, the contact with imaging equipment manufacturers, the timing of decontamination, and the type of decontamination applied to the imaging equipment (P-value < 0.05). Conclusions: This study demonstrated that the reduction in work efficiency among radiographers was not significant compared to before the COVID-19 pandemic. According to the study's results, radiographers' working conditions underwent numerous adjustments over the course of the COVID-19 epidemic.

SARS-CoV-2 variants induce increased inflammatory gene expression but reduced interferon responses and heme synthesis as compared with wild type strains

SARS-CoV-2 variants of concern (VOC) have been associated with increased viral transmission and disease severity. We investigated the mechanisms of pathogenesis caused by variants using a host blood transcriptome profiling approach. We analysed transcriptional signatures of COVID-19 patients comparing those infected with wildtype (wt), alpha, delta or omicron strains seeking insights into infection in Asymptomatic cases.Comparison of transcriptional profiles of Symptomatic and Asymptomatic COVID-19 cases showed increased differentially regulated gene (DEGs) of inflammatory, apoptosis and blood coagulation pathways, with decreased T cell and Interferon stimulated genes (ISG) activation. Between SARS-CoV-2 strains, an increasing number of DEGs occurred in comparisons between wt and alpha (196), delta (1425) or, omicron (2313) infections. COVID-19 cases with alpha or, delta variants demonstrated suppression transcripts of innate immune pathways. EGR1 and CXCL8 were highly upregulated in those infected with VOC; heme biosynthetic pathway genes (ALAS2, HBB, HBG1, HBD9) and ISGs were downregulated. Delta and omicron infections upregulated ribosomal pathways, reflecting increased viral RNA translation. Asymptomatic COVID-19 cases infected with delta infections showed increased cytokines and ISGs expression. Overall, increased inflammation, with reduced host heme synthesis was associated with infections caused by VOC infections, with raised type I interferon in cases with less severe disease.

Using HIS administrative data for initial assessment of comorbid patients’ status (Covid-19 case)

Abstract Background Despite numerous published articles, most of the studies on COVID-19 focused on influence of clinical characteristics and treatments on severity. Yet, the epidemiological viewpoint was rarely discussed. Based on our experience with comorbid hospitalized COVID-19 patients, we aim to show that HIS administrative data can be used to identify individuals that are more prone to worse outcome thus improving patient management not only during pandemics. Methods A retrospective single-center study was conducted using archived anonymized HIS administrative data for all 8319 COVID-19 patients in the university hospital ‘Saint Marina’-Varna, Bulgaria, in the period 14/06/2020-31/12/2021. Up to 7 comorbidities per patient were registered in HIS with ICD-10 codes, used for our analyses. Non-parametric tests and logistic regression were used at α=.05, 95%CI. Results Comorbid patients were 5741(69%), 50.3% ≥69y, 52% male; 1602(27.9%) died, 64% ≥69y, 54.4% male. Deceased were older (p< .01) and had more comorbidities (p< .01). Multimorbid patients (OR = 4.43; 3.80-5.17), male (OR = 4.72; 3.83-5.82) and older ones (OR = 1.91; 1.57-2.34) had higher odds of dying. The odds of dying increased with the number of comorbidities from OR=.635; .559-.721 for 1 to OR = 4.23; 3.28-5.46 for 7. From 4967 comorbid diseases (451 ICD-10 codes), only those from code groups A, J, N, E, I, Z were suitable for analysis. Patients with A41.9 (OR = 213.69; 52.88-863.43), J80 (OR = 52.88; 32.58-85.56), N17 (OR = 18.63; 10.24-33.87), E11.7 (OR = 4.72; 3.09-7.19), I13.2 (OR = 4.67; 3.151-6.93), Z95 (OR = 3.81; 2.74-5.31), I63 (OR = 3.56; 2.25-5.61), E66 (OR = 3.12; 2.10-4.64) died more often. Conclusions Our findings match the results based on EMR data: death with COVID-19 was associated with comorbidities (sepsis, acute RDS and kidney, heart and renal failure, diabetes, cerebral infarction, obesity, implants and grafts), age, gender, i.e. HIS administrative data can be used for quick assessment of comorbid patients’ status. Key messages • HIS administrative data can be used for quick assessment of multimorbidity and following personalized treatment is highly recommended to decrease hospital mortality not only in times of pandemic. • Older Bulgarians have many comorbidities from ICD code groups A, J, N, E, I, Z, and are more prone to death, especially in times of pandemic. Public health measures are needed to prevent comorbidity.

Safely reopening and operating a primary healthcare facility after closure due to SARS-CoV-2 infection in a healthcare worker – Nairobi, Kenya, 2020

The first COVID-19 case in a healthcare worker in Kenya was reported on March 30, 2020, in Nairobi, leading to a 41-day closure of the health facility where he had worked. We assessed infection prevention and control (IPC) activities and implemented recommendations to re-open and operate the facility. We conducted a risk assessment of the facility in April 2020 using a modified World Health Organization, six-element IPC facility risk assessment tool. IPC recommendations were made, and a follow-up assessment of their implementation was conducted in July 2020. Breaches in IPC measures included poor ventilation in most service delivery areas; lack of physical distancing between patients; inadequate COVID-19 information, education, and communication materials; lack of standard operating procedures on cleaning and disinfecting high-touch areas; insufficient IPC training; inadequate hand hygiene facilities; insufficient personal protective equipment supplies; and an inactive IPC committee. Strengthening IPC measures is critical to prevent healthcare facility closures.

Inclusion of Long Covid patients in the care trajectory post-Covid-19: evidence from Belgium

Abstract Background Evidence suggests that 10-30% of COVID-19 infected people develop persistent symptoms that can be diagnosed as Long Covid. Most Long Covid cases are in patients with a mild acute COVID-19 infection and are mainly treated in primary health care. In July 2022, a care trajectory (CT) post-Covid-19 was launched in Belgium, in which Long Covid patients can benefit from reimbursement of treatment costs when consulting health professionals in primary health care for their rehabilitation. Patients are included in this CT mainly through their general practitioner (GP) and depending on patients’ care needs, treatment is organized in a monodisciplinary or multidisciplinary way. Methods A population-based study on data collected by the Belgian network of Sentinel General Practitioners (SGP) on all Long Covid patients consulting their GP during March-December 2023. Information on inclusion in the CT post-Covid-19 was also registered. Descriptive statistics were performed. Results End of 2023 in Belgium, GPs initiated the CT post-Covid-19 for more than 1000 patients, this mainly for monodisciplinary treatment, less than 10% of included patients received multidisciplinary follow-up. By the SGP, among the registered Long Covid sufferers (N = 14), a total of six cases were included in the CT post-Covid-19. Of these cases, the majority were female, of older age, higher educated and had one or two chronic conditions. Most had a mild or moderate prior COVID-19 infection and all were vaccinated multiple times against COVID-19. Symptoms lasted one year or more for 83.3% of included patients. Treatment focused mainly on self-management, energy management and physical exercise programs, mainly in collaboration with physiotherapists. Conclusions This study yields recent data on patients included in the CT post-COVID-19 in primary care in Belgium. It is important to focus on guidance, follow-up and rehabilitation of Long Covid sufferers by healthcare professionals in Belgium. Key messages • Long Covid patients included in the care trajectory post-Covid-19 in Belgium experience years of debilitating symptoms after their prior COVID-19 infection. • The establishment of the care trajectory post-Covid-19 for Long Covid patients in primary care since July 2022 stimulated collaboration between care givers in Belgian primary health care.

Using Social Network Analysis during COVID-19 contact tracing in Libya, 2020

Abstract Background Social network analysis (SNA) may provide valuable insights into individual-level transmission dynamics, allowing for targeted contact tracing interventions. We used SNA to understand the variability in SARS-CoV-2 transmission at the case level during the first wave in Libya. Methods We analyzed the spread of COVID-19 in Libya from March to August 2020 using contact tracing data from 287 confirmed cases. We created a network to visualize the transmission patterns, representing each case as a node and connections as links. We focused on direct contacts identified through contact tracing and calculated network measures such as out-degree centrality, in-degree centrality, and betweenness centrality. Gephi software was used for data visualization and understanding the network structure. Results Of the 287 COVID-19 cases, 264 (92%) had nodes with zero outdegrees, indicating that they did not contribute to infection transmission. The remaining 23 (8%) had non-zero out-degree measures and transmitter infection to other cases, 10 (4%) of whom were super-spreaders (outdegree≥5) causing 70% of the transmission. Six (2%) had no epidemiological contact, indicated by zero in-degree centrality measures; 63% had zero betweenness centrality measures, suggesting they did not act as intermediaries connecting separate transmission chains within the network. The network contained nine clusters of connected nodes, with the largest having 91 (24%) cases; the mean path length between cases was 1.846, and the network diameter was 4. Conclusions The study revealed distinct clusters of connected cases, along with the presence of super-spreaders. This network structure underscores the critical role of early identification and isolation of individuals with high transmission potential to effectively contain outbreaks. This revised conclusion directly references the findings of nine clusters and super-spreaders, demonstrating how these results support the importance of early intervention. Key messages • Social network analysis identifies clusters and super-spreaders in COVID-19 transmission, emphasizing the need for targeted interventions. • Connected clusters and super-spreaders highlight the importance of contact tracing and early isolation for effective control of COVID-19 outbreaks.

Risk of long COVID and associated symptoms after acute SARS-COV-2 infection in ethnic minorities: a Danish nationwide cohort study

Abstract Background and aim Ethnic minorities living in high-income countries have been disproportionately affected by COVID-19 in terms of infection rates, hospitalisations, and deaths; however, less is known about long COVID in these populations. Our aim was to examine the risk of long COVID and associated symptoms among ethnic minorities. Methods We used Danish nationwide register-based cohort data on individuals diagnosed with COVID-19 aged ≥18 years (n = 2 334 271) between January 2020 and August 2022. We calculated the risk of long COVID diagnosis and long COVID symptoms among ethnic minorities compared with native Danes using multivariable Cox proportional hazard regression and logistic regression, respectively. Results Of the diagnosed COVID-19 cases, 1 973 998 (84.6%) were native Danes and 360 273 (15.4%) were ethnic minorities. After adjustment for age, sex, civil status, education, family income, and Charlson comorbidity index, ethnic minorities from North Africa (adjusted hazard ratio [aHR] 1.41, 95% CI [1.12,1.79]), Middle East (aHR 1.38, 95% CI [1.24,1.55]), Eastern Europe (aHR 1.35, 95% CI [1.22,1.49]), and Asia (aHR 1.23, 95% CI [1.09,1.40]), had significantly greater risk of long COVID diagnosis than native Danes. In the analysis by largest countries of origin, the greater risks of long COVID diagnosis were found in Iraqis (aHR 1.56, 95% CI [1.30,1.88]), Turks (aHR 1.42, 95% CI [1.24,1.63]), and Somalis (aHR 1.42, 95% CI [1.07,1.91]). A significant factor associated with an increased risk of long COVID diagnosis was COVID-19 hospitalisation. Conclusions Migrants and ethnic minorities had higher risk of long-COVID compared to native Danes.

Urban Community Participation In Dealing With The Covid-19 Pandemic Through Environment-Based On Covid-19 Handling Policies In The City Of Mataram

The city of Mataram is an area that has a high number of Covid-19 cases in West Nusa Tenggara Province. Covid-19 has seriously impacted all aspects of life, from health, economy, society, culture, and tourism to religious rituals. This study aims to analyze the policies for handling Covid-19 in Mataram City. Second, analyze the form of community participation in efforts to deal with Covid-19 in Mataram City. This study uses the participation theory of Cohen and Uphoff as an analytical tool. This research is qualitative research with a descriptive approach. In- depth interviews, observation, and documentation employed data collection techniques. The results of this study indicate that this environment-based policy for handling Covid-19 effectively suppresses the spread of Covid-19 in Mataram City. This policy is implemented by involving the participation of all elements of government, local leaders, and the community. Community participation in handling Covid-19 starts with high public awareness of Covid-19, then implementing health protocols. Community participation is also seen in the formulation of policies for handling Covid-19, the movement to join a Task Force (Satgas), carrying out self- help movements to help each other, and being involved in evaluating the handling of Covid-19 in the City of Mataram.

Child COVID-19 rates and migrant status: A quantitative study in the Lisbon area (Mar 2020-Jun 2022)

Abstract Introduction According to OECD data, immigrant children were over-represented among COVID-19 cases compared to non-immigrant children. However, evidence is missing on whether their migrant status is associated with testing positive for COVID-19. Therefore, we aimed to estimate COVID-19 positivity rates among children residing in five municipalities of the Lisbon metropolitan area, Portugal, and to analyze migrant status as a determinant of health. Methods We conducted a prospective cross-sequential cohort study with 989 children born in 2015, 2018, and 2020 (48.5% non-immigrants; 51.5% immigrants). Sociodemographic data were collected through interview-based questionnaires, and the child’s birth weight and gestational age were obtained from the primary health center’s information system (SClinico). COVID-19 test results (conducted between March 2020 and June 2022) were obtained from the national surveillance system (SINAVE Lab) and categorized into two primary outcomes: ever positive for COVID-19 (yes/no) and positivity frequency for COVID-19 (count). Robust and standard Poisson regressions were used to estimate the association between primary outcomes and demographic, socioeconomic, and clinical variables. We estimate relative risks (RR) and prevalence ratios (PR) with 95% confidence intervals. Results The positivity rate is lower for immigrant children (12% vs 17%). Compared with non-immigrant, and adjusting for other factors, immigrant children are 52% less likely to be ever positive for COVID-19 (PR = 0.482; IC 95%: 0.392-0.594), and the estimated number of positive tests is 54% lower (RR = 0.456; IC 95%: 0.362-0.574). Conclusions Being an immigrant is a protective factor against testing positive for COVID-19, aligning with the well-known immigrant paradox. These results underscore the need for further research into the immunological profiles of this population and are crucial for guiding strategies to manage emerging public health crises. Key messages • Immigrant children living in the Lisbon metropolitan area may have a protective factor against testing positive for COVID-19, consistent with the well-known immigrant paradox. • Understanding COVID-19 positivity rates and associated factors in children is crucial for guiding effective strategies to manage and mitigate emerging public health crises.

Lecturer

COVID-19 is a highly contagious viral illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It primarily affects the respiratory system but can also impact other significant organs. Its rapid spread has heightened interest in biomarkers for screening and early diagnosis. This study evaluated D-dimer, C-reactive protein (CRP), and complete blood count (CBC) levels in patients with COVID-19 compared to healthy individuals. A total of 50 healthy individuals and 50 COVID-19 patients, aged 20 to 65, were included in the study, excluding individuals with conditions such as leukemia, thalassemia, or pregnancy. Whole blood and plasma samples were collected for CBC and D-dimer assays and stored at 2-4°C. The COVID-19 patients had significantly higher levels of D-dimer, CRP, white blood cells (WBC), granulocytes, and granulocyte percentage as compared with the healthy individuals (p<0.01), lower levels of lymphocyte percentage, mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) (p<0.01), as shown. The severe COVID-19 cases often show elevated D-dimer levels, indicating a higher risk of thrombosis. Increased CRP levels correlate with more severe conditions and help assess inflammation severity. A CBC provides insights into blood components, like RBC and WBC, and platelets. During COVID-19, CBC results may reveal lymphopenia, a low lymphocyte count linked to increased severity. For the most accurate and current information, consult healthcare professionals, reliable health organizations, or recent scientific studies on COVID-19.

Chest x-ray images: transfer learning model in COVID-19 detection.

This research aims to develop an effective algorithm for diagnosing COVID-19 in chest X-rays using the transfer learning method and support vector machines. In total, data was collected from 10 clinics, including both large city hospitals and smaller medical institutions. This ensured a diverse range of geographical and demographic information in the sample. An extensive data set was collected, including 10,000 chest X-ray images. 5000 images represent normal cases, 3993 images represent pneumonia cases, and 1007 images represent COVID-19 cases. Machine learning methods were applied to develop a classification model, and the results were compared with seven state-of-the-art models and a lightweight CNN architecture. The results showed that the proposed method achieves high accuracy values (Accuracy): 0.95 for COVID-19, 0.89 for pneumonia, and 0.92 for normal images (p < 0.05). Comparison with other models demonstrates statistically significant superiority of our method in accuracy across all three classes. The EfficientNet-B0 model surpasses our method only in accuracy for normal images with p < 0.01, confirming the advantages of our method. Our method demonstrates high sensitivity values (Sensitivity): 0.96 for COVID-19, 0.88 for pneumonia, and 0.93 for normal images (p < 0.05), outperforming most of the compared models. Correlation analysis showed Pearson coefficients of 0.92, 0.89, and 0.94 for COVID-19, pneumonia, and normal images, respectively, confirming a high degree of consistency between predicted and true class labels. In addition, the model was validated on external datasets to assess its generalizability. This validation confirmed its high level of effectiveness in a variety of clinical settings. This study confirms the importance of applying machine learning methods in medical applications and opens new perspectives for early diagnosis of infectious diseases. The practical application of the obtained results can enhance the efficiency of diagnosis and control the spread of COVID-19, as well as contribute to the development of innovative methods in medical practice.

High SARS-CoV-2 secondary infection rates in households with children in Georgia, United States, Fall 2020-Winter 2021.

A wide range of household secondary infection rates has been reported, and the role of children in population transmission dynamics for SARS-CoV-2 remains ill-defined. We sought to better understand household infection early in the pandemic. A cross-sectional study of 17 households in the Atlanta metropolitan area with at least one child and one case of COVID-19 in the prior 1-4 months were recruited between December 2020 and April 2021. Self-collected saliva samples were tested on a multiplexed platform to detect IgG antibodies that bind to SARS-CoV-2 antigens. Secondary infection rates (SIR) were calculated and compared. We report results on 17 families, including 66 individuals. We found an average SIR of 0.58; children and adults were similarly infected (62% children vs. 75% adults) (p = 0.2). Two out of 17 households had a pediatric index per our definition. Number of pediatric infections per household (p = 0.18), isolation (p = 0.34), and mask wearing (p = 0.80) did not differ significantly among households with an SIR above the mean vs. those with SIR below the mean. Households with higher SIR also had a higher number of symptomatic cases (p < 0.001). We demonstrated high household SIRs at the early stages of the pandemic in late 2020 to early 2021 with similar impact on children and adults. The ease of collecting saliva and the detection of asymptomatic infections highlight the advantages of this strategy and potential for scale-up.

Beyond the school: principals’ insights on expanded roles in times of crisis. The case of COVID-19 in Turkey

ABSTRACT This study examined the perspectives of school principals on the workload of the additional administrative responsibilities they were assigned during COVID-19 in Turkey. The purpose of this study was to gain insight into the nature of these additional responsibilities and to assess their impact on the principals who took them on. The research group consisted of 62 school administrators (principals and vice principals), from public elementary schools. The research data were collected through semi-structured interviews. The collected data were analyzed using content analysis. The results showed that principals were assigned tasks without regard to their specific roles, with no differentiation based on their positions. The study also revealed a variety of emotional experiences among the participants, ranging from fear and anxiety to feelings of satisfaction and commitment. Overall, this study provided valuable insights into the complex dynamics of principals’ experiences during COVID-19 and highlighted the need for transparent communication, equitable task allocation, and adequate support mechanisms to maintain their well-being and commitment.

The need for translational bioethics within perinatal healthcare and policy making: A COVID-19 case study

The COVID-19 pandemic highlighted many issues that can occur due to lack of translation between the spheres of bioethics and clinical practice. In this paper, we examine how mothers and newborn infants were inappropriately separated during the initial stages of the pandemic due to inconsistent application of ethical principles in determining policy. One of the significant challenges that translational bioethics face is the complexity regarding its implementation into the health service environment. As outlined in the literature, it may be postulated that responsibility for translating bioethics from philosophical concepts into practice is the duty of those training in philosophical theory and reasoning. However, the use of bioethics in informing clinical practice is not just the case of needing a translator but rather requires an interpreter in the widest sense: professionals attuned to both bioethics and clinical practice, who can communicate with both groups effectively. A two-way dialogue needs to be more cohesively established to ensure clinical practice is guided by ethical principles and to focus academic debate towards the pragmatic issues that require ethical exploration. Utilising the translational bioethics model described by Bærøe and applying it to our perinatal COVID case study, we examine how an integrated translational bioethics approach could have prevented the harm and disruption to mother–infant dyads during the initial phase of the pandemic in 2020.

Pre-COVID-19 cognitive social capital trajectories and peri-COVID-19 depression in China, 2014–2020: A longitudinal study

BackgroundSocial capital is dynamic; however, little is known about the association of its dynamics with peri-pandemic health. We examined the longitudinal association of pre-COVID-19 cognitive social capital trajectories with peri-COVID-19 depressive symptoms and the moderating effect of province-level COVID-19 severity on the association in China. MethodsWe employed four-wave data from China Family Panel Studies between 2014 and 2020. Depressive symptoms in 2020 were measured by the 8-item Center for Epidemiological Studies-Depression Scale. Pre-COVID-19 cognitive social capital from 2014 to 2018 included dichotomized (high/low) generalized trust, trust in neighbors, trust in local government officials, and reciprocity, each of which included five trajectories: persistently low, decreased, fluctuated, increased, and persistently high. Province-level COVID-19 severity in 2020 was a factor score constructed by the number of provincial COVID-19 cases and deaths. We conducted mixed-effects linear regression to answer our research question. ResultsPersistently low generalized trust (β: 0.46; 95 % CI: 0.15,0.78), persistently low (β: 0.57; 95 % CI: 0.22, 0.92), decreased (β: 0.36; 95 % CI: 0.07, 0.65) and increased (β: 0.40; 95 % CI: 0.12, 0.68) trust in neighbors, and persistently low (β: 0.39; 95 % CI: 0.02, 0.77) and decreased (β: 0.68; 95 % CI: 0.38, 0.97) reciprocity, compared with their persistently high trajectories, were associated with a higher level of peri-COVID-19 depressive symptoms. We did not find robust evidence to support the moderating effect of province-level COVID-19 severity. ConclusionsLong-term strategies to increase cognitive social capital and prevent cognitive social capital decline are needed to protect mental health against a pandemic.

Genomic Surveillance for SARS-CoV-2 Variants: Circulation of Omicron XBB and JN.1 Lineages - United States, May 2023-September 2024.

CDC continues to track the evolution of SARS-CoV-2, including the Omicron variant and its descendants, using national genomic surveillance. This report summarizes U.S. trends in variant proportion estimates during May 2023-September 2024, a period when SARS-CoV-2 lineages primarily comprised descendants of Omicron variants XBB and JN.1. During summer and fall 2023, multiple descendants of XBB with immune escape substitutions emerged and reached >10% prevalence, including EG.5-like lineages by June 24, FL.1.5.1-like lineages by August 5, HV.1 lineage by September 30, and HK.3-like lineages by November 11. In winter 2023, the JN.1 variant emerged in the United States and rapidly attained predominance nationwide, representing a substantial genetic shift (>30 spike protein amino acid differences) from XBB lineages. Descendants of JN.1 subsequently circulated and reached >10% prevalence, including KQ.1-like and KP.2-like lineages by April 13, KP.3 and LB.1-like lineages by May 25, and KP.3.1.1 by July 20. Surges in COVID-19 cases occurred in winter 2024 during the shift to JN.1 predominance, as well as in summer 2023 and 2024 during circulation of multiple XBB and JN.1 descendants, respectively. The ongoing evolution of the Omicron variant highlights the importance of continued genomic surveillance to guide medical countermeasure development, including the selection of antigens for updated COVID-19 vaccines.