Coronavirus Disease 2019 Data Research Articles

Identification of diagnostic candidate genes in COVID-19 patients with sepsis.

Coronavirus Disease 2019 (COVID-19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID-19 patients with sepsis. We obtained a COVID-19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID-19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration. The COVID-19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID-19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes. Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA-DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID-19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID-19 patients with sepsis.

The epidemiology and phylogenetic trends of Omicron subvariants from BA.5 to XBB.1 in Taiwan

BackgroundOmicron, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, entered Taiwan at the end of 2021. The Taiwanese government ended its "zero-COVID" policy in March 2022. Multiple coronavirus disease 2019 (COVID-19) outbreaks began in April 2022. We monitored the replacement of Omicron subvariants after BA.1/BA.2 and analyzed their correlation with COVID-19 outbreaks. MethodsWe collected SARS-CoV-2 real-time qRTPCR-positive nasopharyngeal swabs from Kaohsiung Medical University Hospital (KMUH), Kaohsiung City, Taiwan, and performed sequencing for specimens exhibiting a cytopathic effect in Vero E6 cells to determine their clades and lineages. We analyzed the medical records of COVID-19 patients and identified hospitalization risk factor(s). We retrieved SARS-CoV-2 sequences identified in Taiwan from GISAID and analyzed their correlation with COVID-19 data from the Taiwan Centers for Disease Control. ResultsWe analyzed the phylogenesis of KMUH-47 to KMUH-104 (SARS-CoV-2 isolates identified herein) and all of the Omicron subvariants from BA.5 to XBB.1 (n = 1930). Age and comorbidities were hospitalization risk factors. Men generally exhibited a greater fatality rate than women. COVID-19-related deaths predominantly occurred in individuals over 70 years old. The COVID-19-related case fatality rate increased as nucleotide (NT) and amino acid (AA) substitutions increased. The number of COVID-19-related cases and deaths progressively decreased with each outbreak between August 2022 and October 2023. ConclusionHospitalization was associated with age and the presence of comorbidities. COVID-19-related fatality was linked to sex, age, and the accumulation of NT and AA substitutions in emerging Omicron subvariants.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases

It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by “Harmony” algorithm. The data was standardized and normalized by using “Seurat” package, and “SingleR” algorithm was used for cell grouping annotation. The “Findmarker” function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.

Use of reporting templates for chest radiographs in a coronavirus disease 2019 context: measuring concordance of radiologists with three international templates.

Reporting templates for chest radiographs (CXRs) for patients presenting or being clinically managed for severe acute respiratory syndrome coronavirus 2 [coronavirus disease 2019 (COVID-19)] has attracted advocacy from international radiology societies. We aim to explore the effectiveness and useability of three international templates through the concordance of, and between, radiologists reporting on the presence and severity of COVID-19 on CXRs. Seventy CXRs were obtained from a referral hospital, 50 from patients with COVID-19 (30 rated "classic" COVID-19 appearance and 20 "indeterminate") and 10 "normal" and 10 "alternative pathology" CXRs. The recruited radiologists were assigned to three test sets with the same CXRs but with different template orders. Each radiologist read their test set three times and assigned a classification to the CXR using the Royal Australian New Zealand College of Radiology (RANZCR), British Society of Thoracic Imaging (BSTI), and Modified COVID-19 Reporting and Data System (Dutch; mCO-RADS) templates. Inter-reader variability and intra-reader variability were measured using Fleiss' kappa coefficient. Twelve Australian radiologists participated. The BSTI template had the highest inter-reader agreement (0.46; "moderate" agreement), followed by RANZCR (0.45) and mCO-RADS (0.32). Concordance was driven by strong agreement in "normal" and "alternative" classifications and was lowest for "indeterminate." General consistency was observed across classifications and templates, with intra-reader variability ranging from "good" to "very good" for COVID-19 CXRs (0.61), "normal" CXRs (0.76), and "alternative" (0.68). Reporting templates may be useful in reducing variation among radiology reports, with intra-reader variability showing promise. Feasibility and implementation require a wider approach including referring and treating doctors plus the development of training packages for radiologists specific to the template being used.

Investigation of the relationship between COVID-19 and pancreatic cancer using bioinformatics and systems biology approaches.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, poses a huge threat to human health. Pancreatic cancer (PC) is a malignant tumor with high mortality. Research suggests that infection with SARS-CoV-2 may increase disease severity and risk of death in patients with pancreatic cancer, while pancreatic cancer may also increase the likelihood of contracting SARS-CoV-2, but the link is unclear. This study investigated the transcriptional profiles of COVID-19 and PC patients, along with their respective healthy controls, using bioinformatics and systems biology approaches to uncover the molecular mechanisms linking the 2 diseases. Specifically, gene expression data for COVID-19 and PC patients were obtained from the Gene Expression Omnibus datasets, and common differentially expressed genes (DEGs) were identified. Gene ontology and pathway enrichment analyses were performed on the common DEGs to elucidate the regulatory relationships between the diseases. Additionally, hub genes were identified by constructing a protein-protein interaction network from the shared DEGs. Using these hub genes, we conducted regulatory network analyses of microRNA/transcription factors-genes relationships, and predicted potential drugs for treating COVID-19 and PC. A total of 1722 and 2979 DEGs were identified from the transcriptome data of PC (GSE119794) and COVID-19 (GSE196822), respectively. Among these, 236 common DEGs were found between COVID-19 and PC based on protein-protein interaction analysis. Functional enrichment analysis indicated that these shared DEGs were involved in pathways related to viral genome replication and tumorigenesis. Additionally, 10 hub genes, including extra spindle pole bodies like 1, holliday junction recognition protein, marker of proliferation Ki-67, kinesin family member 4A, cyclin-dependent kinase 1, topoisomerase II alpha, cyclin B2, ubiquitin-conjugating enzyme E2 C, aurora kinase B, and targeting protein for Xklp2, were identified. Regulatory network analysis revealed 42 transcription factors and 23 microRNAs as transcriptional regulatory signals. Importantly, lucanthone, etoposide, troglitazone, resveratrol, calcitriol, ciclopirox, dasatinib, enterolactone, methotrexate, and irinotecan emerged as potential therapeutic agents against both COVID-19 and PC. This study unveils potential shared pathogenic mechanisms between PC and COVID-19, offering novel insights for future research and therapeutic strategies for the treatment of PC and SARS-CoV-2 infection.

The impact of comorbidity status in COVID-19 vaccines effectiveness before and after SARS-CoV-2 omicron variant in northeastern Mexico: a retrospective multi-hospital study.

The end of the coronavirus disease 2019 (COVID-19) pandemic has been declared by the World Health Organization on May 5, 2023. Several vaccines were developed, and new data is being published about their effectiveness. However, the clinical trials for the vaccines were performed before the Omicron variant appeared and there are population groups where vaccine effectiveness still needs to be tested. The overarching goal of the present study was to analyze the effects of COVID-19 vaccination before and after the Omicron variant in patients considering comorbidities in a population from Nuevo Leon, Mexico. Epidemiological COVID-19 data from the Mexican Social Security Institute were collected from 67 hospitals located in northeastern Mexico, from July 2020 to May 2023, and a total of 669,393 cases were compiled, 255,819 reported a SARS-CoV-2 positive reverse transcription quantitative polymerase chain reaction (RT-qPCR) test or a positive COVID-19 antigen rapid test. Before Omicron (BO, 2020-2021), after 14 days of two doses of COVID-19 vaccine, BNT162b2 and ChAdOx1 vaccines were effective against infection in non-comorbid and all comorbid subgroups, whereas after Omicron (AO, 2022- 2023) there was no significant effectiveness against infection with none of the vaccines. Regarding hospitalization BO, BNT162b2, ChAdOx1, CoronaVac and mRNA-1273 significantly protected non-comorbid patients whereas BNT162b2, ChAdOx1, and mRNA-1273, protected all comorbid subgroups against hospitalization. AO, BNT162b2, ChAdOx1, CoronaVac and mRNA-1273 were effective against hospitalization in non-comorbid patients whereas for most comorbid subgroups BNT162b2, ChAdOx1 and CoronaVac were effective against hospitalization. Non-comorbid patients were protected against death as an outcome of COVID-19 during the BO period with most vaccines whereas a reduction in effectiveness was observed AO with mRNA-1273 vaccines in patients with hypertension, and diabetes mellitus. BO, COVID-19 vaccines were effective against infection, hospitalization, and death whereas AO, COVID-19 vaccines failed to protect the population from COVID-19 infection. A varying effectiveness against hospitalization and death is observed AO.

Post-COVID-19: Time to Change Our Way of Life for a Better Future.

Background and Objectives: From the year 1 anno Domini until 1855, with the third plague, major pandemics occurred on average every 348 years. Since then, they have occurred on average every 33 years, with coronavirus disease 2019 (COVID-19) now underway. Even though current technologies have greatly improved the way of life of human beings, COVID-19, with more than 700,000,000 cases and 6,950,000 deaths worldwide by the end of 2023, reminds us that much remains to be done. This report looks back at 18 months of COVID-19, from March 2020 to August 2021, with the aim of highlighting potential solutions that could help mitigate the impact of future pandemics. Materials and Methods: COVID-19 data, including case and death reports, were extracted daily from the Worldometer platform to build a database for the macroscopic analysis of the spread of the virus around the world. Demographic data were integrated into the COVID-19 database for a better understanding of the spatial spread of the SARS-CoV-2 virus in cities/municipalities. Without loss of generality, only data from the top 30 (out of 200 and above) countries ranked by total number of COVID-19 cases were analyzed. Statistics (regression, t-test (p < 0.05), correlation, mean ± std, etc.) were carried out with Excel software (Microsoft® Excel® 2013 (15.0.5579.1001)). Spectral analysis, using Matlab software (license number: 227725), was also used to try to better understand the temporal spread of COVID-19. Results: This study showed that COVID-19 mainly affects G20 countries and that cities/municipalities with high population density are a powerful activator of the spread of the virus. In addition, spectral analysis highlighted that the very first months of the spread of COVID-19 were the most notable, with a strong expansion of the SARS-CoV-2 virus. On the other hand, the following six months showed a certain level of stability, mainly due to multiple preventive measures such as confinement, the closure of non-essential services, the wearing of masks, distancing of 2 m, etc. Conclusion: Given that densely populated cities and municipal areas have largely favored the spread of the SARS-CoV-2 virus, it is believed that such a demographic context is becoming a societal problem that developed countries must address in a manner that is adequate and urgent. COVID-19 has made us understand that it is time to act both preventatively and curatively. With phenomenological evidence suggesting that the next pandemic could occur in less than 50 years, it may be time to launch new societal projects aimed at relieving congestion in densely populated regions.

Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.

Web mining and sentiment analysis of COVID-19 discourse in online forum communities

Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.

The Prevalence and Vaccination Coverage Rates of COVID-19 in Asia: A Cross-sectional Study after Four Years

Asia is the largest and most populous continent and is divided into five regions: Eastern Asia, South-eastern Asia, Central Asia, Southern Asia, and Western Asia. Understanding the pattern of the coronavirus disease 2019 (COVID-19) prevalence in Asia could help researchers and decision-makers learn how to respond to public health emergencies more effectively. By the end of 2023, it would have been nearly four years since the first cases of COVID-19 were detected. In this article, COVID-19 data, including the vaccination coverage rate, were used to examine the COVID-19 prevalence across Asia by the end of 2023 based on income and geographic division. Data on COVID-19, including the vaccination coverage rates, for various Asian countries were collected from reliable, publicly available websites. Then, COVID-19 data were analyzed based on income and geographic division. An analysis of variance (ANOVA) test and an Independent Samples t-test were also performed to assess the variation of the means of COVID-19 data across the continent. As of January 1, 2024, COVID-19 data across Asia's five regions were inconsistent. West Asia was most affected, with the highest cases and deaths per million and most tests performed per population. Eastern Asia had the lowest deaths per million, tests per population, and case-fatality rates (CFRs). The COVID-19 vaccination rates range between 2.39% and 105.7%, while the booster vaccination rates varied from 0% in Kyrgyzstan and Laos to 141.7% in Japan. The ANOVA test showed significant variance in the mean, median age, mean vaccination coverage rates, and mean booster vaccination rates, where Eastern Asia had higher averages than the other parts. The independent t-test revealed significant differences in COVID-19 data means, except for CFRs, where low- and lower-middle-income countries had lower averages than upper-middle and high-income countries. Inconsistent COVID-19 data, including vaccination coverage, across Asia highlights the need for uniform standards in case identification and diagnosis, ensuring equitable vaccine distribution, and providing essential diagnostic resources to all countries for future emergencies.

No Panic in Pandemic: The Impact of Individual Choice on Public Health Policy

Problem definition: Public health interventions, such as social distancing and lockdown, play an important role in containing infectious disease outbreaks, such as coronavirus disease 2019 (COVID-19). Yet, these interventions could cause significant financial losses because of the disruption to regular socioeconomic activities. Moreover, an individual’s activity level is influenced not only by public health policies but also by one’s perception of the disease burden of infection. Strategic planning is required to optimize the timing and intensity of these public health interventions by considering individual responses. Methodology/results: We use the multinomial logit choice model to characterize individual reactions to the risk of infection and public health interventions and integrate it into a repeated Stackelberg game with the susceptible-infected-recovered disease transmission dynamics. We find that the individual equilibrium activity level is higher than the socially optimal activity level because of an individual’s ignorance of the negative externality imposed on others. As a result, implementing lockdown and social distancing policies at moderate disease prevalence may be equivalently critical, if not more, compared with their implementations when the disease prevalence is at its peak level. To verify these findings, we conduct numerical studies based on representative COVID-19 data in Minnesota. Managerial implications: Our results call for policymakers’ attention to consider the impact of individuals’ responses in the planning for different pandemic containment measures. Individuals’ responses in the pandemic may significantly affect the optimal implementation of lockdown and social distancing policies. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0514 .

COVID-19 diagnostic testing and vaccinations among First Nations in Manitoba: A nations-based retrospective cohort study using linked administrative data, 2020-2021.

Differential access to healthcare has contributed to a higher burden of illness and mortality among First Nations compared to other people in Canada. Throughout the Coronavirus Disease 2019 (COVID-19) pandemic, First Nations organizations in Manitoba partnered with public health and Manitoba government officials to ensure First Nations had early, equitable and culturally safe access to COVID-19 diagnostic testing and vaccination. In this study, we examined whether prioritizing First Nations for vaccination was associated with faster uptake of COVID-19 vaccines among First Nations versus All Other Manitobans (AOM). In this retrospective cohort study, we used linked, whole-population administrative data from the Manitoba healthcare system (February 2020 to December 2021) to determine rates of COVID-19 diagnostic testing, infection, and vaccination, and used adjusted restricted mean survival time (RMST) models to test whether First Nations received their first and second vaccine doses more quickly than other Manitobans. The cohort comprised 114,816 First Nations (50.6% female) and 1,262,760 AOM (50.1% female). First Nations were younger (72.3% were age 0 to 39 years) compared to AOM (51% were age 0 to 39 years) and were overrepresented in the lowest 2 income quintiles (81.6% versus 35.6% for AOM). The 2 groups had a similar burden of comorbidities (65.8% of First Nations had none and 6.3% had 3 or more; 65.9% of AOM had none and 6.0% had 3 or more) and existing mental disorders (36.9% of First Nations were diagnosed with a mood/anxiety disorder, psychosis, personality disorder, or substance use disorder versus 35.2% of AOM). First Nations had crude infection rates of up to 17.20 (95% CI 17.15 to 17.24) COVID-19 infections/1,000 person-months compared with up to 6.24 (95% CI 6.16 to 6.32) infections/1,000 person-months among AOM. First Nations had crude diagnostic testing rates of up to 103.19 (95% CI 103.06 to 103.32) diagnostic COVID-19 tests/1,000 person-months compared with up to 61.52 (95% CI 61.47 to 61.57) tests/1,000 person-months among AOM. Prioritizing First Nations to receive vaccines was associated with faster vaccine uptake among First Nations versus other Manitobans. After adjusting for age, sex, income, region of residence, mental health conditions, and comorbidities, we found that First Nations residents received their first vaccine dose an average of 15.5 (95% CI 14.9 to 16.0) days sooner and their second dose 13.9 (95% CI 13.3 to 14.5) days sooner than other Manitobans in the same age group. The study was limited by the discontinuation of population-based COVID-19 testing and data collection in December 2021. As well, it would have been valuable to have contextual data on potential barriers to COVID-19 testing or vaccination, including, for example, information on social and structural barriers faced by Indigenous and other racialized people, or the distrust Indigenous people may have in governments due to historical harms. In this study, we observed that the partnered COVID-19 response between First Nations and the Manitoba government, which oversaw creation and enactment of policies prioritizing First Nations for vaccines, was associated with vaccine acceptance and quick uptake among First Nations. This approach may serve as a useful framework for future public health efforts in Manitoba and other jurisdictions across Canada.

Retrospective Analysis of the Impact of SARS-CoV-2 (COVID-19) on Pregnancy and Neonatal Outcomes.

Background: The novel coronavirus disease 2019 (COVID-19) was more devastating in people with comorbidities such as advanced age and immunodeficiency. Another group affected by COVID-19 was pregnant women. Immunological changes during pregnancy and conditions such as gestational diabetes and pre-eclampsia that occur during pregnancy also have effects on the fetus. The aim of this study was to analyze the effects of PCR-proven COVID-19 infection during pregnancy on fetus and newborn. Methods: Between December 2019 and October 2021, data from pregnant women with COVID-19 symptoms or a history of contact with people with COVID-19, infected with PCR-proven COVID-19 virus, were analyzed retrospectively. Clinical and laboratory data of pregnant women were analyzed. Death data associated with COVID-19 were evaluated. Clinical and laboratory findings of newborns related to COVID-19 and mortality data related to COVID-19 were recorded. The study received approval from the Gazi Yasargil Training and Research Hospital ethics committee (09.07.2021/853). Results: We evaluated 327 pregnant women who were followed up in our hospital and whose deliveries ended in live birth, stillbirth, miscarriage, or curettage. One hundred eighty-five (56.6%) of the pregnant women had at least one COVID-19-related symptom. We evaluated the data of 306 live births, 21 intrauterine fetal deaths, and 13 postnatal deaths. Among the postnatal deaths, five infants succumbed directly due to COVID-19 infection. A total of 23 live-born babies (7.5%) were classified as small for gestational age (SGA), while 80 babies (26.1%) were born before 37 weeks of gestation, and 32 babies (10.4%) were born before 32 weeks. Cord blood gas analysis revealed that 19 infants (6.3%) had pH < 7 and base excess (BE) < -12. The rate of perinatal asphyxia was significantly higher in babies born to mothers who did not survive (p = 0.027). A considerable number of infants, 119 (40.3%), were admitted to the neonatal intensive care unit (NICU). Among the seven infants with positive PCR results admitted to the NICU, five (4.2%) did not survive. Conclusion: While COVID-19 infection in pregnancy seriously affects mortality and morbidity in pregnant women, it also causes mortality and morbidity on the fetus.

Clinical characteristics and disease course before and after SARS-CoV-2 infection in a large cohort of systemic sclerosis patients.

The objective of this study is to evaluate the clinical presentations and adverse outcomes of Coronavirus Disease 2019 (COVID-19) in patients with systemic sclerosis (SSc) and assess the impact of SSc features on the clinical course of COVID-19. In this multicenter, retrospective study, SSc patients with COVID-19 were included. Clinical features of SSc, along with detailed COVID-19 data, were extracted from medical records and patient interviews. The study included 112 patients (mean age 51.4 ± 12.8 years; 90.2% female). SSc-associated interstitial lung disease (ILD) was evident in 57.1% of the patients. The findings revealed hospitalization in 25.5%, respiratory support in 16.3%, intensive care unit admission in 3.6%, and a mortality rate of 2.7% among SSc patients with COVID-19. Risk factors for respiratory failure, identified through univariate analysis, included ILD (OR: 7.49, 95% CI: 1.63-34.46), ≥1 comorbidity (OR: 4.55, 95% CI: 1.39-14.88), a higher physician global assessment score at the last outpatient visit (OR 2.73, 95% CI: 1.22-6.10), and the use of mycophenolate at the time of infection (OR: 5.16, 95 %CI: 1.79-14.99). Notably, ≥1 comorbidity emerged as the sole significant predictor of the need for respiratory support in COVID-19 (OR: 5.78, 95% CI: 1.14-29.23). In the early post-COVID-19 period, 17% of patients reported the progression of the Raynaud phenomenon, and 10.6% developed new digital ulcers. Furthermore, progression or new onset of dyspnea and cough were detected in 28.3% and 11.4% of patients, respectively. This study suggests a potential association between adverse outcomes of COVID-19 and SSc-related ILD, severe disease activity, and the use of mycophenolate. Additionally, it highlights that having comorbidities is an independent risk factor for the need for respiratory support in COVID-19 cases.

BRICS and the Race to Net- Zero Emissions by 2050: Is COVID-19 a Barrier or an Opportunity?

The emerging economies of Brazil, Russia, India, China and South Africa (BRICS) have significant influence on the global economic and environmental trajectories. They have carbon intensive economic systems, which contribute significantly to total global greenhouse gas (GHG) emissions, leading to climate change. However, BRICS have joined the race to net-zero emissions by 2050 in the quest for a climate neutral and sustainable global economy. The journey, however, is not without challenges and opportunities. The proliferation of the coronavirus disease 2019 (COVID-19) had mixed reactions from scientists regarding its implications on net-zero trajectories. While statistical data show a correlation between COVID-19 and a decrease in total emissions, it is envisaged that COVID-19 compromised the efforts to develop carbon neutral economies. Hence, there is still a need for more scientific examination of COVID-19's impact on net-zero ambitions, especially in the emerging economies. This study focuses on India and South Africa's trajectories. Statistical analysis of secondary data from authentic interactive web-based dashboards for COVID-19 data repositories, namely Our World in Data and Climate Action Tracker was performed in conjunction with the document analysis approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Some of the COVID-19 challenges as indicated by the results of the study include curtailing the technology transfer staircase in the energy sector, retaliatory emissions for recovery and resource diversion. The opportunities presented by COVID-19 in the quest for carbon neutrality include behavioral changes in investment, production and consumption patterns with a focus on low carbon options. Governments and stakeholders need to focus on addressing the barriers whilst riding on the opportunities presented by the pandemic to achieve net-zero emissions by 2050.

International scientific communication on COVID-19 data: management pitfalls understanding.

During the pandemic, countries utilized various forms of statistical estimations of coronavirus disease-2019 (COVID-19) impact. Differences between databases make direct comparisons and interpretations of data in different countries a challenge. We evaluated country-specific approaches to COVID-19 data and recommended changes that would improve future international collaborations. We compared the COVID-19 reports presented on official UK (National Health System), Israeli (Department of Health), Latvian (Center for Disease Prevention and Control) and USA (Centers for Disease Control and Prevention) health authorities' websites. Our analysis demonstrated critical differences in the ways COVID-19 statistics were made available to the general and scientific communities. Specifically, the differences in approaches were found in the presentation of the number of infected cases and tests, and percentage of positive cases, the number of severe cases, the number of vaccinated, and the number and percent of deaths. Findability, Accessibility, Interoperability and Reusability principles could guide the development of essential global standards that provide a basis for communication within and outside of the scientific community.

Application of harmony search algorithm in optimizing autoregressive integrated moving average: A study on a data set of Coronavirus Disease 2019

World Health Organization (WHO) declared Coronavirus Disease 2019 (COVID-19), caused by the virus named Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2), as a global pandemic on March 11, 2020. Several researchers have used various statistical models and techniques to study and forecast the trend of spread and magnitude of the impact of SARS-CoV-2. ARIMA is one such model that has been widely used for this purpose, but in most cases, the model was not optimized suitably. In this paper, a music-inspired metaheuristic optimization algorithm, named Harmony Search (HS), has been integrated with ARIMA in order to improve the forecasting of the COVID-19 data set. The accuracy of forecasting has significantly increased after optimizing the model using the proposed algorithm. Furthermore, a novel application of HS has also been discussed in this paper.

PP27 The Private Healthcare Responses Against COVID-19 Emergencies In Brazil

IntroductionThe Corona Virus Disease 2019 (COVID-19) pandemic is the biggest public health crisis of all time. Private health care plays a major role in health globally. We conducted a study to document the engagement of a Brazilian private health care organization to influence decisions to private and public health emergencies of COVID-19.MethodsThis retrospective study evaluated the outputs of the health technology assessment (HTA) group of Unimed-BH, a private Health Maintenance Organization (HMO) with 1.5 million participants in Belo Horizonte, Minas Gerais state. The study evaluated the impacts on the overview of health local decisions in the municipality and the national supplementary health agency (ANS) during the period from March 2020 to December 2022.ResultsDuring the pandemic, Unimed-BH made all its sanitary decisions based on scientific research, such as the use of masks and appropriate medications. Even though some medicines, such as regdanvimab, were authorized for emergency use by the Brazilian Health Regulatory Agency (ANVISA), Unimed-BH did not recommend their use due to uncertain evidence, and months later, ANVISA withdrew the registration.Unimed-BH also conducted a systematic review of ivermectin for COVID-19 treatment, which showed no effect, and therefore did not recommend its use. Additionally, Unimed-BH provided weekly updates on COVID-19 data, including suspected and confirmed cases, hospitalizations, and deaths in their customer portfolio. The organization also actively supported the decisions made by ANS and municipal managers using evidence and statistics on the pandemic. The Unimed-BH HTA group produced a total of 167 reports from March 2020 to December 2022.ConclusionsBelo Horizonte had the lowest in-hospital mortality rates with COVID-19 in Brazil. Unimed-BH’s HTA reports provided evidence-based assessments for decision-making, proposed partnership with policymakers, fomented information transparency, and strict follow-up on pandemic numbers, which may have contributed to the lower fatality rate in our city. These findings underscore the importance of private healthcare organizations in responding to COVID-19 emergencies, and their potential to support evidence-based decision-making and minimize the impact of the pandemic.

Changes in Caregiver Burden in Older Adults' Caregivers During the COVID-19 Outbreak.

This study investigated the changes in caregiver burden during the coronavirus disease 2019 (COVID-19) pandemic. The study sample included 827 caregivers for older adults (65+) from the National Health and Aging Trends Study (NHATS) COVID-19 data set. We used paired t-tests to analyze data. The caregivers' assistance in basic and instrumental activities of daily living for care recipients, as well as the amount of time they provided, increased significantly (all ps < .001) during the pandemic compared with before. Approximately, 40% of caregivers reported changes in the amount of assistance they provided during the pandemic. Financial, emotional, and physical difficulties among caregivers have increased, with emotional difficulties being the most frequently reported. The majority of caregivers reported not utilizing any social services during the pandemic. The pandemic may have heightened the caregiver burden. It is important to ensure that caregivers have access to and utilize social services in a physically and psychologically safe manner.

Survey on chest CT findings in COVID-19 patients in Okinawa, Japan: differences between the delta and omicron variants

To investigate the frequency of pneumonia and chest computed tomography (CT) findings in patients with coronavirus disease 2019 (COVID-19) during the fifth Delta variant-predominant and sixth Omicron variant-predominant waves of the COVID-19 pandemic in Okinawa, Japan. A survey on chest CT examinations for patients with COVID-19 was conducted byhospitals with board-certified radiologists who provided treatment for COVID-19 pneumonia in Okinawa Prefecture. Data from 11 facilities were investigated. Indications for chest CT; number of COVID-19 patients undergoing chest CT; number of patients with late-onset pneumonia, tracheal intubation, and number of deaths; and COVID-19 Reporting and Data System classifications of initial chest CT scans were compared by the chi-squared test between the two pandemic waves (Delta vs. Omicron variants). A total of 1944 CT scans were performed during the fifth wave, and 1178 were performed during the sixth wave. CT implementation rates, which were the number of patients with COVID-19 undergoing CT examinations divided by the total number of COVID-19 cases in Okinawa Prefecture during the waves, were 7.1% for the fifth wave and 2.1% for the sixth wave. The rates of tracheal intubation and mortality were higher in the fifth wave. Differences between the distributions of the CO-RADS classifications were statistically significant for the fifth and sixth waves (p < 0.0001). In the fifth wave, CO-RADS 5 (typical of COVID-19) was most common (65%); in the sixth wave, CO-RADS 1 (no findings of pneumonia) was most common (50%). The finding of “typical for other infection but not COVID-19” was more frequent in the sixth than in the fifth wave (13.6% vs. 1.9%, respectively). The frequencies of pneumonia and typical CT findings were higher in the fifth Delta variant-predominant wave, and nontypical CT findings were more frequent in the sixth Omicron variant-predominant wave of the COVID-19 pandemic in Okinawa, Japan.