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Raw sequencing data, code and intermediate analysis files from "Antiviral activity of molnupiravir precursor NHC against SARS-CoV-2 Variants of Concern (VOCs) and implications for the therapeutic window and resistance" (Prince et al, 2023). Please see sample_metadata.xlsx for all metadata relating to the files contained in this repository. Code for data visualisation can be found in: mut_sub_all_pts_serial-pass.R. Specific paths to data will have to be changes to refer to where you have downloaded the data in this repository. Metadata for use with the R script is serial-pass-nimagen-metadata-forR.csv.

The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy rate.

A pandemic that is fast developing, the coronavirus epidemic is putting unprecedented pressure on healthcare systems. Children with renal disorders, including those undergoing renal transplantation, those with chronic kidney disease, and those with acute kidney damage necessitating dialysis, offer treatment issues due to COVID-19, particularly for those using long-term immunosuppressive medicines. The urgent requirement is for us to be ready to handle this vulnerable group of kids. This article's goals are to help caregivers and medical professionals manage children with renal illnesses, maintain patient well-being, and safeguard staff from infection. Setting: Participants and their data were drawn from an existing consent to contact database of the Infectious disease center of the Khorezm region. The candidates were 2 to 12 years of age, 20 women, and the ratio of male to female children was 67 % to 87 %. These individuals were receiving care (treatment and medication support) at different centers especially designated for Covid-19 infection in the Khorezm region and at different clinics in the Khorezm region. We retrieved the data from the infectious control center where all the data were collected from all centers of the Khorezm region. They can speak English or Russian, and agreed to be contacted for further research. Methods: A prospective study with Cohort study/guidelines from WHO for Covid-19 care and self-protection. A combined Cohort study of the COVID-19 survey was performed telephonically and personally for 15-20 minutes maximum, which included a discussion with doctors who attended and had any information about this syndrome. Research Focus: This research is focusing on the problems of (MIS-C), which is a rare complication of COVID-19, but it can be serious or dangerous. The symptoms can overlap with infections and other illnesses. What are the circumstances and conditions of this disease, and what steps we can take to address them?

The outbreak of the new coronavirus (COVID-19) had resulted in the creation of a disaster all over the world and it had become a highly acute and severe illness. The prevalence of this disease is increasing rapidly worldwide. The technology of deep learning (DL) became one of the hot topics in the computing context and it is widely implemented in a variety of the medical applications. Those techniques proved to be sufficient tools for the clinicians in automatic COVID-19 diagnosis. In the present study, a DL technology that is based on convolution neural networks (CNN) models had been suggested for the binary COVID-19 classification. In the initial step of the suggested model, COVID-19 data-set of chest X-ray (CXR) images have been obtained then preprocessed. Whereas in the second stage, a new CNN model has been built and trained for diagnosing COVID-19 data-set as (positive) infection or (negative) normal cases. The suggested architecture had a success in classifying COVID-19 with the training model accuracy that had reached 96.57% for the training data-set and 92.29% for validating data-set and could reach the target point with a minimal learning rate for training this model with promising results.

The outbreak of coronavirus disease 2019 (COVID-19) drives most higher education systems in many countries to stop face-to-face learning. Accordingly, many universities, including Jordan University of Science and Technology (JUST), changed the teaching method from face-to-face education to electronic learning from a distance. This research paper investigated the impact of the e-learning experience on the students during the spring semester of 2020 at JUST. It also explored how to predict students’ academic performances using e-learning data. Consequently, we collected students’ datasets from two resources: the center for e-learning and open educational resources and the admission and registration unit at the university. Five courses in the spring semester of 2020 were targeted. In addition, four regression machine learning algorithms had been used in this study to generate the predictions: random forest (RF), Bayesian ridge (BR), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The results showed that the ensemble model for RF and XGBoost yielded the best performance. Finally, it is worth mentioning that among all the e-learning components and events, quiz events had a significant impact on predicting the student’s academic performance. Moreover, the paper shows that the activities between weeks 9 and 12 influenced students’ performances during the semester.

Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.

<p>The sudden arrival of COVID-19 called for new technologies to manage the healthcare system and to reduce the burden of patients in the hospitals. Artificial intelligence (AI) which involved using computers to model intelligent behavior became an important choice. Various AI applications helped a lot in the management of healthcare and delivering quick medical consultations and various services to a wide variety of patients. These new technological developments had significant roles in detecting the COVID-19 cases, monitoring them, and forecasting for the future. Artificial intelligence is applied to mimic the functional system of human intelligence. AI techniques and applications are also applied in proper examinations, prediction, analyzing, and tracking of the whereabouts of patients and the projected results. It also played a significant role in recognizing and proposing the generation of vaccines to prevent COVID-19. This study is therefore an attempt to understand the major role and use of AI in healthcare institutions by providing urgent decision-making techniques that greatly helped to manage and control the spread of the COVID-19 disease.</p>

In the past two years, the world witnessed the spread of the coronavirus (COVID-19) pandemic that disrupted the entire world, the only solution to this epidemic was health isolation, and with it everything stopped. When announcing the availability of a vaccine, the world was divided over the effectiveness and harms of this vaccine. This article provides an analysis of vaccinators and analysis of people's opinions of the vaccine's efficacy and whether negative or positive. Then a model is built to predict the future numbers of vaccinators and a model that predicts the number of negative opinions or tweets. The model consists of three stages: first, converting data sets into a synchronized time series, that is, the same place and time for vaccination and tweets. The second stage is building a prediction model and the third stage was descripting analysis of the prediction results. The autoregressive integrated moving averages (ARIMA) method was used after decomposing the components of ARIMA and choosing the optimal model, the best results obtained from seasonal ARIMA (SARIMA) for both predictions, the last stage is the descriptive analysis of the results and linking them together to obtain an analysis describing the change in the number of vaccinators and the number of negative tweets.

Im Datensatz 'COVID-19-Hospitalisierungen' werden die aktuellen Zahlen der nach den Vorgaben des Infektionsschutzgesetzes - IfSG - erfassten hospitalisierten COVID-19-Fälle bereitgestellt. Um den Trend der Anzahl von Hospitalisierungen und der 7-Tage-Hospitalisierungsinzidenz besser bewerten zu können, wird die berichtete Hospitalisierungsinzidenz um eine Schätzung der zu erwartenden Anzahl an verzögert berichteten Hospitalisierungen ergänzt. Neben den Daten der gemeldeten COVID-19-Hospitalisierungen auf Bundes- und Länderebene wird daher ein Nowcasting der Anzahl hospitalisierter Fälle und der 7-Tage-Hospitalisierungsinzidenz auf Bundesebene durchgeführt. Ziel ist die Schätzung der Anzahl von hospitalisierten COVID-19-Fällen mit Meldedatum innerhalb der sieben vorhergehenden Tage - inklusive der noch nicht an das RKI berichteten Hospitalisierungen. Aufbauend auf dem Nowcasting wird eine Schätzung der adjustierten 7-Tage-Hospitalisierungsinzidenz durchgeführt.

Die COVID-19-Impfung kann einen Wendepunkt in der Kontrolle der COVID-19-Pandemie darstellen und erfährt daher hohes Maß an öffentlicher Aufmerksamkeit. Einführung und Umsetzung der COVID-19-Impfung gehen mit besonderen Herausforderungen einher, die bei der Impfdatenerfassung zu berücksichtigen sind. In diesem Kontext ist es Ziel des Projekts 'Digitales Impfquoten-Monitoring' (DIM), tagesaktuell, bundesweit die Impfquote zu erfassen und folgend aufbereitet darzustellen, um zeitnah den Verlauf der COVID-19-Impfkampanne zu analysieren, bei Bedarf nach zusteuern, und logistisch bzw. organisatorische Konsequenzen zu ziehen.Der durch das DIM-Projekt bereitgestellte Datensatz enthält Daten über den Verlauf der COVID-19 Impfungen in Deutschland. Die hier veröffentlichten Impfdaten aggregieren Daten aus drei Datenquellen:Die DIM-Daten enthalten Angaben der Impfzentren, mobilen Impfteams, Krankenhäuser und der Betriebsärzte_innen, die über die DIM-Webanwendung übermittelt werdenDer täglich aggregierte Kerndatensatz der impfenden Ärzt_innen über die Kassenärztliche Bundesvereinigung (KBV)Der täglich aggregierte Kerndatensatz der impfenden Ärzt_innen über die Privatärztliche Bundesvereinigung (PBV)