Coronavirus Disease 2019 Data Research Articles

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.

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.

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.

Coronavirus disease 2019 and lung cancer: where are we?

Oncology patients are more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection due to hospital contact and an immunological system that can be compromised by antineoplastic therapy and supportive treatments. Certain similarities have been described in the physiopathology of coronavirus disease 2019 (COVID-19) and lung cancer (LC) that may explain the higher probability of these patients of developing a more serious disease with more frequent hospitalizations and even death, especially with the addition of smoking, cardiovascular and respiratory comorbidities, old age and corticosteroids use. Pre-existing lesions and cancer therapies change the normal architecture of the lungs, so diagnostic scales such as COVID-19 Reporting and Data System (CO-RADS) are of vital importance for a correct diagnosis and patient homogenization, with a high inter-observer correlation. Moreover, anticancer treatments have required an adaptation to reduce the number of visits to the hospital [hypofractionated radiotherapy (RT), larger intervals between chemotherapy cycles, delay in follow-up tests, among others]. In a way, this has also caused a delay in the diagnosis of new cancers. On the other hand, vaccination has had a positive impact on the mortality of these patients, who maintain a similar seroprevalence to the rest of the population, with a similar impact in mortality.

Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data

BackgroundReal-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks.MethodsFor exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented.ResultsOur study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results.ConclusionsExtending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.

Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach.

Severe coronavirus disease 2019 (COVID-19) has caused a great threat to human health. Metabolic associated fatty liver disease (MAFLD) is a liver disease with a high prevalence rate. Previous studies indicated that MAFLD led to increased mortality and severe case rates of COVID-19 patients, but its mechanism remains unclear. This study analyzed the transcriptional profiles of COVID-19 and MAFLD patients and their respective healthy controls from the perspectives of bioinformatics and systems biology to explore the underlying molecular mechanisms between the 2 diseases. Specifically, gene expression profiles of COVID-19 and MAFLD patients were acquired from the gene expression omnibus datasets and screened shared differentially expressed genes (DEGs). Gene ontology and pathway function enrichment analysis were performed for common DEGs to reveal the regulatory relationship between the 2 diseases. Besides, the hub genes were extracted by constructing a protein-protein interaction network of shared DEGs. Based on these hub genes, we conducted regulatory network analysis of microRNA/transcription factors-genes and gene - disease relationship and predicted potential drugs for the treatment of COVID-19 and MAFLD. A total of 3734 and 589 DEGs were screened from the transcriptome data of MAFLD (GSE183229) and COVID-19 (GSE196822), respectively, and 80 common DEGs were identified between COVID-19 and MAFLD. Functional enrichment analysis revealed that the shared DEGs were involved in inflammatory reaction, immune response and metabolic regulation. In addition, 10 hub genes including SERPINE1, IL1RN, THBS1, TNFAIP6, GADD45B, TNFRSF12A, PLA2G7, PTGES, PTX3 and GADD45G were identified. From the interaction network analysis, 41 transcription factors and 151 micro-RNAs were found to be the regulatory signals. Some mental, Inflammatory, liver diseases were found to be most related with the hub genes. Importantly, parthenolide, luteolin, apigenin and MS-275 have shown possibility as therapeutic agents against COVID-19 and MAFLD. This study reveals the potential common pathogenesis between MAFLD and COVID-19, providing novel clues for future research and treatment of MAFLD and severe acute respiratory syndrome coronavirus 2 infection.

Autumn in New York: The Case of Long-Term Care Facilities in the "Safe Staffing" Lawsuit With Less Staffing But Similar COVID-19 Outcomes.

In 2022, 239 New York state long-term care facilities (LTCFs) challenged a "Safe Staffing" law in court. Our study compares LTCFs involved and not involved in the lawsuit, testing for differences in staffing measures and resident outcomes during the first year of the coronavirus disease 2019 (COVID-19) pandemic. New York LTCF-level data were obtained from the Centers for Medicare and Medicaid Services 2019 organization and 2020 COVID-19 data files. These data were then linked to data from the Long-Term Care Community Coalition, which identified the LTCFs involved in the "Safe Staffing" lawsuit. We first tested for differences in reported 2019 staffing levels by lawsuit involvement. Second, we specified "Doubly Robust" regression models to test if lawsuit involvement was associated with differences in resident COVID-19 infections, COVID-19 deaths, and overall mortality. LTCFs involved in the lawsuit reported lower staff ratings and fewer staffing hours compared to LTCFs not involved in the lawsuit. Despite finding higher rates of admissions with COVID-19 in LTCFs involved in the lawsuit, we did not find that COVID-19 infections, COVID-19 deaths, or overall mortality differed by lawsuit involvement. LTCFs involved in the lawsuit were deemed by policymakers as reducing staff, earning excess profits, and placing residents at risk. While these LTCFs reported lower staffing levels, we observed no differences in resident outcomes during the first year of the COVID-19 pandemic. Researchers and policymakers should develop more nuanced perspectives concerning the relationship among LTCF staffing, outcomes, and organizational profitability.

The Correlation between Hofstede's Cultural Dimensions and COVID-19 Data in the Early Stage of the COVID-19 Pandemic Period.

COVID-19 (coronavirus disease 2019) has become the deadliest virus to affect the international community in recent history, with more than 760 million people infected and more than 6.87 million deaths as of March 2023; therefore, based on Hofstede's national cultural theory, this study collected Hofstede's six national cultural dimensions on a global scale, namely, power distance (PDI), individualism/collectivism (IDV), masculinity/femininity (MAS), uncertainty avoidance (UAI), long-term/short-term orientation (LTO), and indulgence/restraint (IVR) scores, and COVID-19 data from the World Health Organization (WHO) from 22 February 2020 to 30 February 2021. Then, based on eight items of global COVID-19 data, this study analyzed the correlation between Hofstede's six dimensions and the COVID-19 data from six regions (Africa (AFRO), Europe (EURO), the Americas (AMRO), the Western Pacific (WPRO), South East Asia (SEARO), and the Eastern Mediterranean (EMRO)) divided by the WHO. This study found the following: (1) Hofstede's six cultural dimensions indeed have a significant correlation with the COVID-19 data of different WHO regions in different ways. (2) Except for IDV and UAI, PDI is a highly critical factor and has a significant correlation with the COVID-19 data from AFRO and EMRO. MAS also is an important factor and has a significant correlation with COVID-19 data from WPRO and SEARO. Meanwhile, LTO has a significant correlation with some COVID-19 data from the AMRO region, and IVR has a significant correlation with some COVID-19 data from the EURO region. Finally, the new insights from this study are worthy of further study by scholars, and they will be of great help to global governments and medical institutions in formulating policies to suppress infectious diseases in the future.

Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case.

The development of an epidemic always exhibits multiwave oscillation owing to various anthropogenic sources of transmission. Particularly in populated areas, the large-scaled human mobility led to the transmission of the virus faster and more complex. The accurate prediction of the spread of infectious diseases remains a problem. To solve this problem, we propose a new method called the multi-source dynamic ensemble prediction (MDEP) method that incorporates a modified susceptible-exposed-infected-removed (SEIR) model to improve the accuracy of the prediction result. The modified SEIR model is based on the compartment model, which is suitable for local-scale and confined spaces, where human mobility on a large scale is not considered. Moreover, compartmental models cannot be used to predict multiwave epidemics. The proposed MDEP method can remedy defects in the compartment model. In this study, multi-source prediction was made on the development of coronavirus disease 2019 (COVID-19) and dynamically assembled to obtain the final integrated result. We used the real epidemic data of COVID-19 in three cities in China: Beijing, Lanzhou, and Beihai. Epidemiological data were collected from 17 April, 2022 to 12 August, 2022. Compared to the one-wave modified SEIR model, the MDEP method can depict the multiwave development of COVID-19. The MDEP method was applied to predict the number of cumulative cases of recent COVID-19 outbreaks in the aforementioned cities in China. The average accuracy rates in Beijing, Lanzhou, and Beihai were 89.15%, 91.74%, and 94.97%, respectively. The MDEP method improved the prediction accuracy of COVID-19. With further application to other infectious diseases, the MDEP method will provide accurate predictions of infectious diseases and aid governments make appropriate directives.