Artificial Neural Network Prediction of COVID-19 Daily Infection Count.

This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.

Prevalence and duration of SARS-CoV-2 fecal shedding in breastfeeding dyads following maternal COVID-19 diagnosis.

Background: There is a paucity of data on the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in feces of lactating women with coronavirus disease 2019 (COVID-19) and their breastfed infants as well as associations between fecal shedding and symptomatology. Objective: We examined whether and to what extent SARS-CoV-2 is detectable in the feces of lactating women and their breastfed infants following maternal COVID-19 diagnosis. Methods: This was a longitudinal study carried out from April 2020 to December 2021 involving 57 breastfeeding maternal-infant dyads: 33 dyads were enrolled within 7 d of maternal COVID-19 diagnosis, and 24 healthy dyads served as controls. Maternal/infant fecal samples were collected by participants, and surveys were administered via telephone over an 8-wk period. Feces were analyzed for SARS-CoV-2 RNA. Results: Signs/symptoms related to ears, eyes, nose, and throat (EENT); general fatigue/malaise; and cardiopulmonary signs/symptoms were commonly reported among mothers with COVID-19. In infants of mothers with COVID-19, EENT, immunologic, and cardiopulmonary signs/symptoms were most common, but prevalence did not differ from that of infants of control mothers. SARS-CoV-2 RNA was detected in feces of 7 (25%) women with COVID-19 and 10 (30%) of their infants. Duration of fecal shedding ranged from 1-4 wk for both mothers and infants. SARS-CoV-2 RNA was sparsely detected in feces of healthy dyads, with only one mother's and two infants' fecal samples testing positive. There was no relationship between frequencies of maternal and infant SARS-CoV-2 fecal shedding (P=0.36), although presence of maternal or infant fever was related to increased likelihood (7-9 times greater, P≤0.04) of fecal shedding in infants of mothers with COVID-19.

COVID-19 diagnostic prediction on chest CT scan images using hybrid quantum-classical convolutional neural network.

Notwithstanding the extensive research efforts directed towards devising a dependable approach for the diagnosis of coronavirus disease 2019 (COVID-19), the inherent complexity and capriciousness of the virus continue to pose a formidable challenge to the precise identification of affected individuals. In light of this predicament, it is essential to devise a model for COVID-19 prediction utilizing chest computed tomography (CT) scans. To this end, we present a hybrid quantum-classical convolutional neural network (HQCNN) model, which is founded on stochastic quantum circuits that can discern COVID-19 patients from chest CT images. Two publicly available chest CT image datasets were employed to evaluate the performance of our model. The experimental outcomes evinced diagnostic accuracies of 99.39% and 97.91%, along with precisions of 99.19% and 98.52%, respectively. These findings are indicative of the fact that the proposed model surpasses recently published works in terms of performance, thus providing a superior ability to precisely predict COVID-19 positive instances.Communicated by Ramaswamy H. Sarma.

Target sign in COVID-19, radiological interpretation and diagnostic contribution of digital thoracic tomosynthesis.

INTRODUCTION: Our objectives are: To describe the radiological semiology, clinical-analytical features and prognosis related to the target sign (TS) in COVID-19. To determine whether digital thoracic tomosynthesis (DTT) improves the diagnostic ability of radiography. MATERIAL AND METHODS: Retrospective, descriptive, single-centre, case series study, accepted by our ethical committee. Radiological, clinical, analytical and follow-up characteristics of patients with COVID-19 and TS on radiography and DTT between November 2020 and January 2021 were analysed. RESULTS: Eleven TS were collected in 7 patients, median age 35 years, 57% male. All TS presented with a central nodule and a peripheral ring, and in at least 82%, the lung in between was of normal density. All TS were located in peripheral, basal regions and 91% in posterior regions. TS were multiple in 43%. Contiguous TS shared the peripheral ring. Other findings related to pneumonia were associated in 86% of patients. DTT detected 82% more TS than radiography. Only one patient underwent a CT angiography of the pulmonary arteries, positive for acute pulmonary thromboembolism. Seventy-one per cent presented with pleuritic pain. No distinctive laboratory findings or prognostic worsening were detected. CONCLUSIONS: TS in COVID-19 predominates in peripheral and declining regions and can be multiple. Pulmonary thromboembolism was detected in one case. It occurs in young people, frequently with pleuritic pain and does not worsen the prognosis. DTT detects more than 80 % of TS than radiography.

Public Health Response to SARS-CoV-2 in Assisted Living Facilities in New York State: March 2020-December 2022.

CONTEXT: Assisted living facility (ALF) residents are especially vulnerable to SARS-CoV-2 infection due to the age and comorbidities of the resident population and the social nature of these facilities. OBJECTIVE: To collate all New York State Department of Health guidance and regulations to control transmission of SARS-CoV-2 infection within ALFs from March 2020 through December 2022 and to include US Food and Drug Administration COVID-19 testing and vaccine authorizations. DESIGN: A narrative chronological review of all New York State Department of Health guidance. RESULTS: Documents and associated guidance and regulations are divided into 4 sections: (1) lockdown until COVID-19 vaccine emergency use authorization; (2) COVID-19 vaccine authorization until phased reopening; (3) phased reopening, vaccination requirements, and booster vaccination; (4) the period of the bivalent booster. CONCLUSION: Controlling the spread of SARS-CoV-2 within ALFs required a multifactorial approach that included stringent infection control measures, testing, and vaccination and careful attention to the social structure and support systems within ALFs. The SARS-CoV-2 pandemic highlighted the complexity of controlling spread of an easily transmissible respiratory pathogen in assisted living communities and the need to structure infection control programs within the diverse ALFs that provide care for our aging population.

CECT: Controllable ensemble CNN and transformer for COVID-19 image classification.

The COVID-19 pandemic has resulted in hundreds of million cases and numerous deaths worldwide. Here, we develop a novel classification network CECT by controllable ensemble convolutional neural network and transformer to provide a timely and accurate COVID-19 diagnosis. The CECT is composed of a parallel convolutional encoder block, an aggregate transposed-convolutional decoder block, and a windowed attention classification block. Each block captures features at different scales from 28 × 28 to 224 × 224 from the input, composing enriched and comprehensive information. Different from existing methods, our CECT can capture features at both multi-local and global scales without any sophisticated module design. Moreover, the contribution of local features at different scales can be controlled with the proposed ensemble coefficients. We evaluate CECT on two public COVID-19 datasets and it reaches the highest accuracy of 98.1% in the intra-dataset evaluation, outperforming existing state-of-the-art methods. Moreover, the developed CECT achieves an accuracy of 90.9% on the unseen dataset in the inter-dataset evaluation, showing extraordinary generalization ability. With remarkable feature capture ability and generalization ability, we believe CECT can be extended to other medical scenarios as a powerful diagnosis tool. Code is available at https://github.com/NUS-Tim/CECT.

COVID-19 surveillance in a bone marrow transplantation unit: experience from a Brazilian tertiary-care teaching hospital.

PURPOSE: In this work, we aimed to describe the strategy of the weekly SARS-CoV-2 RT-PCR surveillance program that was implemented in our bone marrow transplantation (BMT) unit. METHODS: Our unit performed SARS-CoV-2 RT-PCR before admission and then weekly during hospitalization even if the patient was asymptomatic. From May 2021 to May 2022, we collected data from all patients that were admitted in the BMT unit to perform transplantation. The total of SARS-CoV-2 RT-PCR performed and the positive rate were described. RESULTS: During the study period, 65 patients were admitted for HSCT. A total of 414 SARS-CoV-2 RT-PCR were performed. Two cases were detected (positivity rate, 0.48%). After the positive test, both patients were isolated outside the BMT unit. CONCLUSION: We postulate that diagnosing these patients and isolating them outside the transplantation unit may have prevented secondary symptomatic cases.

Development and Evaluation of Surveillance System for Identifying Jail-Associated COVID-19 Cases in Minnesota, USA, 2022.

Confinement facilities are high-risk settings for the spread of infectious disease, necessitating timely surveillance to inform public health action. To identify jail-associated COVID-19 cases from electronic laboratory reports maintained in the Minnesota Electronic Disease Surveillance System (MEDSS), Minnesota, USA, the Minnesota Department of Health developed a surveillance system that used keyword and address matching (KAM). The KAM system used a SAS program (SAS Institute Inc., https://www.sas.com) and an automated program within MEDSS to identify confinement keywords and addresses. To evaluate KAM, we matched jail booking data from the Minnesota Statewide Supervision System by full name and birthdate to the MEDSS records of adults with COVID-19 for 2022. The KAM system identified 2,212 cases in persons detained in jail; sensitivity was 92.40% and specificity was 99.95%. The success of KAM demonstrates its potential to be applied to other diseases and congregate-living settings for real-time surveillance without added reporting burden.

Validity of inpatient electronic health record-based measures of oxygen-related therapy in the United States: Lessons applicable for studying COVID-19 endpoints.

INTRODUCTION: During the COVID-19 pandemic, inpatient electronic health records (EHRs) have been used to conduct public health surveillance and assess treatments and outcomes. Invasive mechanical ventilation (MV) and supplemental oxygen (O2) use are markers of severe illness in hospitalized COVID-19 patients. In a large US system (n = 142 hospitals), we assessed documentation of MV and O2 use during COVID-19 hospitalization in administrative data versus nursing documentation. METHODS: We identified 319 553 adult hospitalizations with a COVID-19 diagnosis, February 2020-October 2022, and extracted coded, administrative data for MV or O2. Separately, we developed classification rules for MV or O2 supplementation from semi-structured nursing documentation. We assessed MV and O2 supplementation in administrative data versus nursing documentation and calculated ordinal endpoints of decreasing COVID-19 disease severity. Nursing documentation was considered the gold standard in sensitivity and positive predictive value (PPV) analyses. RESULTS: In nursing documentation, the prevalence of MV and O2 supplementation among COVID-19 hospitalizations was 14% and 75%, respectively. The sensitivity of administrative data was 83% for MV and 41% for O2, with both PPVs above 91%. Concordance between sources was 97% for MV (κ = 0.85), and 54% for O2 (κ = 0.21). For ordinal endpoints, administrative data accurately identified intensive care and MV but underestimated hospitalizations with O2 requirements (42% vs. 18%). CONCLUSIONS: In comparison to nursing documentation, administrative data under-ascertained O2 supplementation but accurately estimated severe endpoints such as MV. Nursing documentation improved ascertainment of O2 among COVID-19 hospitalizations and can capture oxygen requirements in adults hospitalized with COVID-19 or other respiratory illnesses.

Rapid identification of SARS-CoV-2 strains via isothermal enzymatic recombinase amplification and nanopore sequencing.

Surveillance of the SARS-CoV-2 genome has become a crucial technique in the management of COVID-19, aiding the pandemic response and supporting effective public health interventions. Typically, whole-genomic sequencing is used along with PCR-based target enrichment techniques to identify SARS-CoV-2 variants, which is a complicated and time-consuming process that requires central laboratory facilities. Thus, there is an urgent need to develop rapid and cost-effective tools for precise on-site detection and identification of SARS-CoV-2 strains. In this study, we demonstrate the rapid diagnosis of COVID-19 and identification of SARS-CoV-2 variants by amplification and sequencing of the entire SARS-CoV-2 S gene using isothermal enzymatic recombinase amplification combined with the advanced Oxford nanopore sequencing technique. The entire procedure, from sampling to sequencing, takes less than 8 hours and can be performed with limited resources. The newly developed method has noteworthy implications for examining the transmission dynamics of the virus, detecting novel genetic variants, and assessing the effect of mutations on diagnostic approaches, antiviral treatments, and vaccines.

COVID-19 post-traumatic stress disorder: the role of ACEs, alexithymia, and attachment in the Italian population.

OBJECTIVE: The COVID-19 pandemic is considered a collective traumatic event. Several studies have highlighted high levels of post-traumatic stress disorder (PTSD) symptoms among the general population during the pandemic. The general aim of this research is to explore the role of adverse childhood experiences (ACEs), alexithymia, and anxiety and avoidance attachment dimensions as risk factors that are making individuals more vulnerable to PTSD-COVID-related symptoms. SUBJECTS AND METHODS: The COVID-19-PTSD Questionnaire, 20-Item Toronto Alexithymia Scale (TAS-20), Adverse Childhood Experiences Questionnaire, and the Experiences in Close Relationships-Revised Form (ECR-R) were administered to 224 participants who were between 18 and 65 years of age, and residents of Italy. Socio-demographic variables were also collected. The data was collected between October 2021 and March 2022. RESULTS: The findings of the Spearman correlation analysis showed several significant associations between alexithymia, attachment dimensions, and PTSD symptoms related to COVID-19 diagnosis and age. A multivariable logistic regression model was performed using the COVID-19-PTSD total scores over/under the clinical cut-off as dependent variables and age, gender, anxiety and avoidance attachment scores, ACEs, and total alexithymia as independent variables, with alexithymia total score (B = .071; p = .001), ECR-R Anxiety (B = .034; p = .001) and ECR-R Avoidance (B = -.033; p = .024) showing to respectively increase and reduce the possibility of reporting clinical symptomatology. CONCLUSIONS: Emotional regulation and attachment have been shown to be risk factors for COVID-19 PTSD symptomatology. Focused intervention programs and emotional education can be useful tools for developing protective factors in the general population.

The effects of social support and self-efficacy on hopefulness in low-income older adults during COVID-19 pandemic.

BACKGROUND: Social support and self-efficacy play a significant role in improving positive psychological well-being in marginalized older adults. However, to date, there are few studies identifying the relationships during the COVID-19 pandemic. We examined the effect of social support and self-efficacy on hopefulness in a majority Black sample of marginalized low-income older adults during the COVID-19 pandemic. METHODS: This study used baseline data from a clinical trial designed to increase COVID-19 testing in Essex County, NJ, United States. The dataset involved participants 50 years old or older. We conducted: 1) cross-sectional descriptive/frequency statistics to understand the sociodemographic characteristics, 2) multivariate linear regression to investigate the direct relationships between social support subscales or self-efficacy and hopefulness, and 3) mediation analyses to examine the mediating role of self-efficacy in the relationship between social support and hopefulness. RESULTS: Our findings showed that self-efficacy had a partial mediating effect on the relationship between social support and hopefulness. After adjusting for covariate variables, social support subscales (i.e., emotional/informational, tangible, affectionate, positive social interaction social support) and self-efficacy were significantly associated with hopefulness. The indirect effect of social support via self-efficacy was positive and statistically significant. CONCLUSION: Self-efficacy mediated the relationship between social support and hopefulness in marginalized older adults aged 50 and over. Further research is needed to identify the various facets of positive psychological well-being using longitudinal data and a larger sample size.

The effect of antifibrotic agents on acute respiratory failure in COVID-19 patients: a retrospective cohort study from TriNetX US collaborative networks.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on global health and economies, resulting in millions of infections and deaths. This retrospective cohort study aimed to investigate the effect of antifibrotic agents (nintedanib and pirfenidone) on 1-year mortality in COVID-19 patients with acute respiratory failure. METHODS: Data from 61 healthcare organizations in the TriNetX database were analyzed. Adult patients with COVID-19 and acute respiratory failure were included. Patients with a pre-existing diagnosis of idiopathic pulmonary fibrosis before their COVID-19 diagnosis were excluded. The study population was divided into an antifibrotic group and a control group. Propensity score matching was used to compare outcomes, and hazard ratios (HR) for 1-year mortality were calculated. RESULTS: The antifibrotic group exhibited a significantly lower 1-year mortality rate compared to the control group. The survival probability at the end of the study was 84.42% in the antifibrotic group and 69.87% in the control group. The Log-Rank test yielded a p-value of less than 0.001. The hazard ratio was 0.434 (95% CI: 0.264-0.712), indicating a significant reduction in 1-year mortality in the antifibrotic group. Subgroup analysis demonstrated significantly improved 1-year survival in patients receiving nintedanib treatment and during periods when the Wuhan strain was predominant. DISCUSSION: This study is the first to demonstrate a survival benefit of antifibrotic agents in COVID-19 patients with acute respiratory failure. Further research and clinical trials are needed to confirm the efficacy of these antifibrotic agents in the context of COVID-19 and acute respiratory failure.

Overview of Dermatological Manifestations Associated with the COVID-19 Infection.

COVID-19 is an infectious disease caused by SARS-CoV-2 that is characterized by respiratory symptoms, fever, and chills.[1] While these systemic symptoms are widely known and well understood, there have also been reports of dermatological manifestations in patients with COVID-19. These manifestations include chilblain-like lesions, maculopapular lesions, urticarial lesions, necrosis, and other varicella-like exanthems.[2] The pathogenesis of these lesions are not well understood, but the procoagulant and pro-inflammatory state induced by COVID-19 infections may be contributing to varied cutaneous manifestations.[3] Drug interactions and concurrent hypersensitivity reactions have also been postulated.[4] This review aims to compile and analyze various retrospective studies and case reports to summarize the clinical presentation of dermatological lesions associated with COVID-19 infections and suggest further areas of research.

Rapid Nucleic Acid Diagnostic Technology for Pandemic Diseases.

The recent global pandemic of coronavirus disease 2019 (COVID-19) has enormously promoted the development of diagnostic technology. To control the spread of pandemic diseases and achieve rapid screening of the population, ensuring that patients receive timely treatment, rapid diagnosis has become the top priority in the development of clinical technology. This review article aims to summarize the current rapid nucleic acid diagnostic technologies applied to pandemic disease diagnosis, from rapid extraction and rapid amplification to rapid detection. We also discuss future prospects in the development of rapid nucleic acid diagnostic technologies.

Comparison of the Clinical Outcomes Between Early and Delayed Transplantation After SARS-CoV-2 Infection.

Our study analyzed 95 solid organ transplant (SOT) and 78 hematopoietic stem cell transplant (HSCT) recipients with prior coronavirus disease 2019 (COVID-19). Patients who underwent transplantation within 30 days of COVID-19 infection comprised the early group, and those who underwent transplantation post-30 days of COVID-19 infection comprised the delayed group. In the early transplantation group, no patient, whether undergoing SOT and HSCT, experienced COVID-19-associated complications. In the delayed transplantation group, one patient each from SOT and HSCT experienced COVID-19-associated complications. Additionally, among early SOT and HSCT recipients, two and six patients underwent transplantation within seven days of COVID-19 diagnosis, respectively. However, no significant differences were observed in the clinical outcomes of these patients compared to those in other patients. Early transplantation following severe acute respiratory syndrome coronavirus 2 infection can be performed without increased risk of COVID-19-associated complications. Therefore, transplantation needs not be delayed by COVID-19 infection.

Impact of the COVID-19 pandemic on the real-world diagnostic infrastructure for tuberculosis-An ESGMYC collaborative study.

We determined the impact of the COVID-19 pandemic on mycobacterial diagnostic services. 40 laboratories from 22 countries completed an online questionnaire covering the redeployment of the laboratory infrastructure and/or staff for SARS-CoV-2 testing, staff shortages and supply chain disruptions. 28 laboratories reported monthly numbers of samples processed for mycobacterial investigations and monthly numbers of M. tuberculosis complex (MTBC) PCRs performed between October 1st 2018 and October 31st 2020. More than half (23/40) of the participating TB laboratories reported having performed COVID-19 diagnostics in the early phase of the pandemic, in part with negative impact on the mycobacterial service activities. All participating laboratories reported shortages of consumables and laboratory equipment due to supply chain issues. Average monthly sample numbers decreased by 24% between January 2020 and October 2020 compared to pre-pandemic averages. At the end of the study period, most participating laboratories had not returned to pre-pandemic average MTBC PCR throughput.

Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey.

The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics.

RNA-Based Sensor Systems for Affordable Diagnostics in the Age of Pandemics.

In the era of the COVID-19 pandemic, the significance of point-of-care (POC) diagnostic tools has become increasingly vital, driven by the need for quick and precise virus identification. RNA-based sensors, particularly toehold sensors, have emerged as promising candidates for POC detection systems due to their selectivity and sensitivity. Toehold sensors operate by employing an RNA switch that changes the conformation when it binds to a target RNA molecule, resulting in a detectable signal. This review focuses on the development and deployment of RNA-based sensors for POC viral RNA detection with a particular emphasis on toehold sensors. The benefits and limits of toehold sensors are explored, and obstacles and future directions for improving their performance within POC detection systems are presented. The use of RNA-based sensors as a technology for rapid and sensitive detection of viral RNA holds great potential for effectively managing (dealing/coping) with present and future pandemics in resource-constrained settings.

Availability and use of institutional support programs for emergency department healthcare personnel during the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic placed health care personnel (HCP) at risk for stress, anxiety, burnout, and post-traumatic stress disorder (PTSD). To address this, hospitals developed programs to mitigate risk. The objectives of the current study were to measure the availability and use of these programs in a cohort of academic emergency departments (EDs) in the United States early in the pandemic and identify factors associated with program use. METHODS: Cross-sectional survey of ED HCP in 21 academic EDs in 15 states between June and September 2020. Site investigators provided data on the availability of 28 programs grouped into 9 categories. Individual support programs included: financial, workload mitigation, individual COVID-19 testing, emotional (e.g., mental health hotline), and instrumental (e.g., childcare) Clinical work support programs included: COVID-19 team communication (e.g., debriefing critical incident), patient-family communication facilitation, patient services (e.g., social work, ethics consultation), and system-level exposure reduction. Participants provided corresponding data on whether they used the programs. We used generalized linear mixed models clustered on site to measure the association between demographic and facility characteristics and program use. RESULTS: We received 1,541 survey responses (96% response rate) from emergency physicians or advanced practice providers, nurses, and nonclinical staff. Program availability in each of the 9 categories was high (>95% of hospitals). Program use was variable, with clinical work support programs used more frequently (28-50% of eligible HCP across categories) than individual employee support programs (6-13% of eligible HCP across categories). Fifty-seven percent of respondents reported that the COVID-19 pandemic had affected their stress and anxiety, and 12% were at elevated risk for PTSD. Program use did not significantly differ for HCP who reported symptoms of anxiety and/or stress compared to those who did not. CONCLUSIONS: Early in the pandemic, support programs were widely available to ED HCP, but program use was low. Future work will focus on identifying barriers and facilitators to use and specific programs most likely to be effective during periods of highest occupational stress.