Bacterial and viral infections among adults hospitalized with COVID-19, COVID-NET, 14 states, March 2020-April 2022.

Background: Bacterial and viral infections can occur with SARS-CoV-2 infection, but prevalence, risk factors, and associated clinical outcomes are not fully understood. Methods: We used the Coronavirus Disease 2019-Associated Hospitalization Surveillance Network (COVID-NET), a population-based surveillance system, to investigate the occurrence of bacterial and viral infections among hospitalized adults with laboratory-confirmed SARS-CoV-2 infection between March 2020 and April 2022. Clinician-driven testing for bacterial pathogens from sputum, deep respiratory, and sterile sites were included. The demographic and clinical features of those with and without bacterial infections were compared. We also describe the prevalence of viral pathogens including respiratory syncytial virus, rhinovirus/enterovirus, influenza, adenovirus, human metapneumovirus, parainfluenza viruses, and non-SARS-CoV-2 endemic coronaviruses. Results: Among 36 490 hospitalized adults with COVID-19, 53.3% had bacterial cultures taken within 7 days of admission and 6.0% of these had a clinically relevant bacterial pathogen. After adjustment for demographic factors and co-morbidities, bacterial infections in patients with COVID-19 within 7 days of admission were associated with an adjusted relative risk of death 2.3 times that of patients with negative bacterial testing. Staphylococcus aureus and Gram-negative rods were the most frequently isolated bacterial pathogens. Among hospitalized adults with COVID-19, 2766 (7.6%) were tested for seven virus groups. A non-SARS-CoV-2 virus was identified in 0.9% of tested patients. Conclusions: Among patients with clinician-driven testing, 6.0% of adults hospitalized with COVID-19 were identified to have bacterial coinfections and 0.9% were identified to have viral coinfections; identification of a bacterial coinfection within 7 days of admission was associated with increased mortality.

COVID-Net Network and Application on Support Diagnosis COVID-19 over X-ray Images

Early diagnosis through X-ray images is the diagnosis with low cost, often used in hospitals to assist doctors in making health treatment plans. This paper presents a new approach for supporting the diagnosis of Covid-19 based on chest X-ray images. Specifically, this paper proposes using the Covid-net model for classifying the damage as Covid-19 or other causes. Data augmentation using seam carving was also researched and evaluated with different energy functions. The experimented results done on different databases are promising. © 2022 IEEE.

Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study.

BACKGROUND: In the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important. OBJECTIVE: We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. METHODS: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. RESULTS: We estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states. CONCLUSIONS: Our novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.

Deep Learning Analysis for COVID-19 Using Neural Network Algorithms

The COVID-19 pandemic threatens to devastatingly impact the global population’s safety. A successful surveillance of contaminated patients is a crucial move in the battle against COVID-19, and radiological photographs via chest X-ray are one of the main screening strategies. Recent research showed that patients have abnormalities in photographs of chest X-ray that are characteristic of COVID-19 infects. This has inspired a set of deep learning artificial intelligence (AI) programs, and it has been seen that the precision of the identification of COVID-19 contaminated patients utilizing chest X-rays has been quite positive. However, these built AI schemes, to the extent of their author’s awareness, have become closed sources and not accessible for further learning and expansion by the scientific community, so they are not open to the general public. This thesis therefore implements COVID-Net to identify COVID-19 cases of chest X-rays images, an open source, accessible to the general public, a deep neural network architecture adapted to the detection. The COVID-Net data collection, which is referred to as COVIDx which includes 13,800 chest X-ray photographs of 13,725 patients from 3 open-access data sources, one of which we launched, are also addressed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review.

COVID-19 also referred to as Corona Virus disease is a communicable disease that is caused by a coronavirus. Significant number of people who are tainted with this infection will have to brave and encounter moderate to severe respiratory sickness. Aged persons, sick, convalescing people and all those having underlying health complications like diabetes, chronic breathing diseases and cardiovascular diseases are bound to contract this sickness if not taken proper care of. At the current scenario, there are neither definite treatments nor inoculations against COVID-19. Nevertheless, there are numerous continuing clinical trials assessing the impending treatments and vaccines. Sensing the threatening impacts of Covid-19, researchers of computer science have started using various techniques and approaches of Machine Learning and Deep Learning to detect the presence of the disease using X-rays and CT images. The biggest stumbling block here is that there are only a few datasets available. There is also less number of experts for marking the information explicit to this new strain of infection in people. Artificial Intelligence centred tools can be designed and developed quickly for adapting the existing AI models and for leveraging the ability to modify and associating them with the preliminary clinical understanding to address the new group of COVID-19 and the novel challenges associated with it. In this paper, we look into a few techniques of Machine Learning and Deep Learning that have been employed to analyse Corona Virus Data.

Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks.

The novel Coronavirus, COVID-19, pandemic is being considered the most crucial health calamity of the century. Many organizations have come together during this crisis and created various Deep Learning models for the effective diagnosis of COVID-19 from chest radiography images. For example, The University of Waterloo, along with Darwin AI-a start-up spin-off of this department, has designed the Deep Learning model 'COVID-Net' and created a dataset called 'COVIDx' consisting of 13,975 images across 13,870 patient cases. In this study, COGNEX's Deep Learning Software, VisionPro Deep Learning™,  is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state-of-the-art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, first, by selecting the entire image as the Region of Interest (ROI), and second, by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results: on the entire image as the ROI it achieves an overall F score of 94.0%, and on the segmented lungs, it gets an F score of 95.3%, which is better than COVID-Net and other state-of-the-art open-source Deep Learning models.

Characteristics of Adults Aged 18-49 Years Without Underlying Conditions Hospitalized With Laboratory-Confirmed Coronavirus Disease 2019 in the United States: COVID-NET-March-August 2020.

Among 513 adults aged 18-49 years without underlying medical conditions hospitalized with coronavirus disease 2019 (COVID-19) during March 2020-August 2020, 22% were admitted to an intensive care unit, 10% required mechanical ventilation, and 3 patients died (0.6%). These data demonstrate that healthy younger adults can develop severe COVID-19.

Demographic predictors of hospitalization and mortality in US children with COVID-19.

Understanding which children are at increased risk for poor outcome with COVID-19 is critical. In this study, we link pediatric population-based data from the US Center for Disease Control and Prevention to COVID-19 hospitalization and in-hospital death. In 27,045 US children with confirmed COVID-19, we demonstrate that African American [OR 2.28 (95% CI: 1.93, 2.70)] or mixed race [OR 2.95 (95% CI: 2.28, 3.82)] and an underlying medical condition [OR 3.55 (95% CI: 3.14, 4.01)] are strong predictors for hospitalization. Death occurred in 39 (0.19%) of 20,096 hospitalized children; children with a prior medical condition had an increased odd for death [OR 8.8 (95% CI: 3.7, 21.1)].Conclusion: Hospitalization and in-hospital death are rare in children diagnosed with COVID-19. However, children at higher risk for these outcomes include those with an underlying medical condition, as well as those of African American descent. What is Known: • Demographic factors are independent prognosticators of poor outcome in children with COVID-19. What is New: • Children with an underlying medical condition and those from an African American or mixed race/ethnicity are at high risk for COVID-19 hospitalization. • History of a comorbidity supersedes age, gender, and race/ethnicity as a risk factor for in-hospital pediatric COVID-19 death.