Modeling SARS-CoV-2 True Infections in Catalonia through a Digital Twin

A dynamic view of the evolution of the infections of SARS-CoV-2 in Catalonia using a Digital Twin approach that forecasts the true infection curve is presented. The forecast model incorporates the vaccination process, the confinement, and the detection rate, and virtually allows to consider any nonpharmaceutical intervention, enabling to understand their effects on the disease's containment while forecasting the trend of the pandemic. A continuous validation process of the model is performed using real data and an optimization model that automatically provides information regarding the effects of the containment actions on the population. To simplify this validation process, a formal graphical language that simplifies the interaction with the different specialists and an easy modification of the model parameters are used. The Digital Twin of the pandemic in Catalonia provides a forecast of the future trend of the SARS-CoV-2 spread and information regarding the true cases and effectiveness of the NPIs to control the SARS-CoV-2 spread over the population. This approach can be applied easily to other regions and can become an excellent tool for decision-making. © 2023 The Authors. Advanced Theory and Simulations published by Wiley-VCH GmbH.

RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia

The Editor in Chief has retracted this article (1) because the data published here was previously presented by a different set of authors at arXiv pre-print server (2) and a large amount of text, some figures and tables have been re-used without appropriate citation or acknowledgment. The pre-print has been published (3). Author Benson Babu agrees to this retraction. Authors Charmine Butt, Jagpal Gill and David Chun did not respond to any correspondence regarding this retraction. © Springer Science+Business Media, LLC, part of Springer Nature 2020.

Face and Face Mask Detection Using Convolutional Neural Network

The COVID-19 outbreak has posed a severe healthcare concern in Malaysia. Wearing a mask is the most effective way to prevent infections. However, some Malaysians refuse to wear a face mask for a variety of reasons. This work proposes a real-time face and face mask detection method using image processing technique to promote wearing face mask. Haar Cascade is used for the face detection to extract the features of the human faces as a method of approach. On the other hand, the face mask detection utilizes convolutional neural network (CNN) to train a model using the MobileNetV2 training model designed using Python, Keras and Tensorflow. OpenCV package was used as the interface for the algorithms to be connected to a web camera. Based on the performance metric calculation of detection rate analysis of the experimental results, the face detection rate is at 90% true and 10% false detection, which shows very good detection rate. Furthermore, the training accuracy and validation accuracy for the face mask detector are efficiently near to 1.0, proving a steady accuracy over the time. Training loss and validation loss are almost near to zero and decreasing over time, reassuring the algorithm performance is accurate and efficient for a datasets of 4000 images. © 2022, Springer Nature Switzerland AG.

A Medical Decision Support System to Detect Covid-19 Pneumonia Using CNN

Due to the pandemic by the spread of the COVID virus, there has been a mandatory demand to screen patients. Predominantly RTPCR test is used to detect the virus. The RTPCR test is the most commonly used technique to detect COVID - 19 viruses. The test takes a minimum of 12 hours which is time-consuming and might put a patient's life at stake. This detection method for COVID screening is said to have a false detection rate. CT scans have been used for COVID-19 screening and using CT has several challenges especially since their radiation dose is considerably higher than x-rays. Hence, CXRs are a better choice for the initial assessment. Detection of COVID-19 pneumonia is a fine-grained problem as doctors cannot detect it just by looking at the x-ray images. Moreover, the radiologists visit many patients every day and the diagnosis process take significant time, which may increase errors in screening notably. Therefore, a medical decision support system for screening COVID-19 patients is of utmost importance. Our proposed system is a web application that helps to screen COVID-19 patients effectively. © 2021 IEEE.