Convergence model of AI and IoT for virus disease control system.

Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.

The Infection of Healthcare Workers and the Reinfection of Patients by Omicron Variant - Jiangsu Province, China, December 2022 to January 2023.

What is already known about this topic?: Healthcare workers (HCWs) and previously infected patients (PIPs) may experience a wave of epidemic following the modification of the country's coronavirus disease (COVID)-zero policy in China. What is added by this report?: As of early January 2023, the initial wave of the COVID-19 pandemic among HCWs had effectively subsided, with no statistically significant differences observed in infection rates compared to those of their co-occupants. The proportion of reinfections among PIPs was relatively low, particularly in those with recent infections. What are the implications for public health practice?: Medical and health services have resumed normal operations. For patients who have recently experienced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, appropriate relaxation of policies may be considered.

A Kermack-McKendrick model with age of infection starting from a single or multiple cohorts of infected patients

The infectiousness of infected individuals is known to depend on the time since the individual was infected, called the age of infection. Here, we study the parameter identifiability of the Kermack-McKendrick model with age of infection which takes into account this dependency. By considering a single cohort of individuals, we show that the daily reproduction number can be obtained by solving a Volterra integral equation that depends on the flow of newly infected individuals. We test the consistency of the method by generating data from deterministic and stochastic numerical simulations. Finally, we apply our method to a dataset from SARS-CoV-1 with detailed information on a single cluster of patients. We stress the necessity of taking into account the initial data in the analysis to ensure the identifiability of the problem.

Machine Learning based COVID-19 Patients Monitoring System using CNN

Ever since an anonymous disease broke out in late 2019, the whole world seems to have own ceased functioning. COVID-19 patients are proliferating at an exponential rate, straining healthcare systems around the world. Traditional techniques of screening every patient with a respiratory disease is unfeasible due to the restricted number of testing kits available. We presented a method for recognizing COVID-19 infected patients utilizing data collected from chest X-ray scans to overcome this challenge. This attempt will benefit both patients and doctors significantly. It becomes even more critical in nations where the number of people affected far outnumbers the number of laboratory kits available to test the disease. When current systems are confused whether to retain the patient on the ward with other patients or isolate them in COVID-19 zones, this could be useful in an inpatient setting. Apart from that, it would aid in the identification of patients with a high risk of COVID-19 and a false negative RT-PCR who would require a repeat. Most of the COVID-19 detection methods use traditional image classification models. This has the issue of low detection accuracy and incorrect COVID-19 detection. This method starts with a chest x-ray enhancement procedure like this: Rotation, translation, random conversion. The survey's accuracy has considerably increased as a result of this. For the COVID-19 infection, our model has 97.5 percent accuracy and 100 percent sensitivity (recall). In addition, we used a visualization technique that distinguishes our model from the others by displaying contaminated areas in X-ray pictures. © 2022 IEEE.

A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

Emergency medical service planning considering dynamic and stochastic demands of infected and non-infected patients during epidemics

During a large-scale epidemic, a local healthcare system can be overwhelmed by a large number of infected and non-infected patients. To serve the infected and non-infected patients well with limited medical resources, effective emergency medical service planning should be conducted before the epidemic. In this study, we propose a two-stage stochastic programming model, which integrally deploys various types of emergency healthcare facilities before an epidemic and serves infected and non-infected patients dynamically at the deployed healthcare facilities during the epidemic. With the service equity of infected patients and various practical requirements of emergency medical services being explicitly considered, our model minimizes a weighted sum of the expected operation cost and the equity cost. We develop two comparison models and conduct a case study on Chengdu, a Chinese city influenced by the COVID-19 epidemic, to show the effectiveness and benefits of our proposed model. Sensitivity analyses are conducted to generate managerial insights and suggestions. Our study not only extends the existing emergency supply planning models but also can facilitate better practices of emergency medical service planning for large-scale epidemics. © Operational Research Society 2023.

Predicting ICU Admissions based on Preconditions and Comorbidity for SARS COV2 Infected Patients

Hospitals across the globe have severe constraints in regard to ICU facilities, beds, and other life support systems. However, in certain situations including natural calamities, epidemics or pandemics, large-scale accidents, and so on, the requirement for ICU beds and resources immediately gets augmented. During such times, there exists an impending need for an optimum apportioning of ICU admissions and resources so that those patients who need critical care are given at the right point of time. The onslaught of COVID-19 pandemic has exuded a high probability of virus transmissions and subsequent complications in patients with co-morbidities and relevant medical issues, resulting in the exploration and investigation of models that could forecast the need for ICU admissions with a higher degree of accuracy. In this research study, a patient's pre-condition dataset will be used that is categorical in nature. Feature selection and extractions are implemented and the modified descriptors are provided as input to the model, for evaluating them based on the metrics namely F1-score, accuracy, specificity, and sensitivity. The prime objective is to build a predictive algorithm that will predict prior to the necessity of ICU admissions based on the patient's comorbidity/ precondition specifically for SARS COV2 infection. © 2022 IEEE.

SD-GAN: A Style Distribution Transfer Generative Adversarial Network for Covid-19 Detection through X-ray Images

The Covid-19 pandemic is a prevalent health concern around the world in recent times. Therefore, it is essential to screen the infected patients at the primary stage to prevent secondary infections from person to person. The reverse transcription polymerase chain reaction (RT-PCR) test is commonly performed for Covid-19 diagnosis, while it requires significant effort from health professionals. Automated Covid-19 diagnosis using chest X-ray images is one of the promising directions to screen infected patients quickly and effectively. Automatic diagnostic approaches are used with the assumption that data originating from different sources have the same feature distributions. However, the X-ray images generated in different laboratories using different devices experience style variations e.g., intensity and contrast which contradict the above assumption. The prediction performance of deep models trained on such heterogeneous images of different distributions with different noises is affected. To address this issue, we have designed an automatic end-to-end adaptive normalization-based model called style distribution transfer generative adversarial network (SD-GAN). The designed model is equipped with the generative adversarial network (GAN) and task-specific classifier to transform the style distribution of images between different datasets belonging to different race people and carried out Covid-19 detection effectively. Evaluated results on four different X-ray datasets show the superiority of the proposed model to state-of-the-art methods in terms of the visual quality of style transferred images and the accuracy of Covid-19 infected patient detection. SD-GAN is publicly available at: https://github.com/tasleem-hello/SD-GAN/tree/SD-GAN. Author

Covid-19 Identification through Chest X-ray Images using Deep Learning Classifiers

In the context of the nidovirales order, the coronavirus (Covid-19) is a virus family i.e. extracted from Ribonucleic Acid (RNA) viruses. The pandemic ensued due to it has already infected 9,716,060 people across the globe and is still causing problems with mutations of concern. Because of the immense number of infected patients, and the resulting deficiency of testing kits in hospitals;a rapid, reliable, and automatic detection system is in extreme need to curb the numbers. SARS-Cov-2 is an influenza kind of virus that can be detected using imaging techniques. It is important to distinguish between Covid-19 (caused by SARS-Cov-2) disease against pneumonia disease infected patients and healthy person's chest x-ray scans respectively. Advanced computational techniques like ML (machine learning) and DL (deep learning) had proven to be extremely useful in image processing, especially for the processing of medical images. In this work, 2906 images were taken from the publically available datasets. Various transfer learning-based DL models are applied to these images. Resulting that the ML-based classifiers effectively categorizing the input images (normal/Covid-19/pneumonia). The model achieves 96.3% accuracy with the VGG19 model and Logistic Regression (LR) classifier. This model proves to be highly convenient in treating this pandemic disease Covid-19. © 2022 IEEE.

Diagnosis of Covid-19 using Chest X-ray Images using Ensemble Model

The Coronavirus pandemic devastatingly affects worldwide social prosperity, and general well-being, deadening the human way of life all around the world and undermining our security. Due to the increasing number of confirmed cases associated with COVID-19, it is more important to identify the healthy and infected patients so the control of spread and treatment of infected patients can be done effectively. This work aims to correlate the presence of Covid-19 with the help of both chest X-ray images and CT Scan Images. Deep ensemble learning models take advantage of the different deep learning models, combine them, and produce a model with better performance. The proposed system involves Data augmentation and preprocessing of CT scan images. The same process is applied for Chest X-ray Images, compares the evaluation metrics amongst the models, and suggests the best use of CT scan and Chest X-ray for better Results and accuracy. The features extracted from the Inception V3 model are combined with the features extracted from the Xception model. The inception model convolves the same input tensor with the help of multiple filters, and the results are concatenated. The pre-trained Xception model is capable of depth-wise separable convolutions. The proposed framework works in Covid-19 diagnosis with an accuracy of 96% in Xception and 98% while combining Xception and InceptionV3 models. The final results showed that the Convolutional Neural Network Classifier built with the ensemble of Inception and Xception models that use X-ray images efficiently collects the essential features related to the infections of COVID-19. © 2023 IETE.

COVID-19 Patient Recovery Prediction Using Efficient Logistic Regression Model

This research develops a COVID-19 patient recovery prediction model using machine learning. A publicly available data of infected patients is taken and pre-processed to prepare 450 patients' data for building a prediction model with 20.27% recovered cases and 79.73% not recovered/dead cases. An efficient logistic regression (ELR) model is built using the stacking of random forest (RF) and logistic regression (LR) classifiers. Further, the proposed model is compared with state-of-art models such as logistic regression (LR), support vector machine (SVM), decision tree (C5.0), and random forest (RF). All the models are evaluated with different metrics and statistical tests. The results show that the proposed ELR model is good in predicting not recovered/dead cases and handling imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Optimized PI Control with Tracking Differentiator for Negative Pressure Cabin Control

To block the epidemics like "Corona Virus Disease 2019(COVID-19)"spreading, an effective isolation of the infected patients during the transportation is an important issue, which makes the negative pressure cabin (NPC) become a key equipment. There exist some practical NPCs in service, whose pressures are mostly controlled using the conventional PID controller with parameters regulated by engineering methods. Until now, there is no report about the model of NPC system from the authors' best knowledge. In this paper, the model of the NPC system is reported, which is an inherent nonlinear system. Because of the nonlinear nature of the cabin pressure, the conventional PID controller cannot achieve desire performance to balance the transient and the steady state performance, even though the optimized PID parameters are chosen using the on-line optimization based on genetic algorithm. To solve such a problem, Tracking Differentiator (TD) and PI controller are combined to achieve the desire performance using the optimized parameters. The experiment results show the improvement of the proposed method. © 2022 IEEE.

Attention-based Multi-flow Network for COVID-19 Classification and Lesion Localization from Chest CT

COVID-19 has been rapidly spreading worldwide and infected more than 1 million people with over 690k deaths reported. It is urgent and crucial to identify COVID-19-infected patients by computed tomography (CT) accurately and rapidly. However, we found that two problems, weak supervision and lack of interpretability, hindered its development. To address these challenges, we propose an attention-based multi-flow network for COVID-19 classification and lesion localization from chest CT. In the proposed model, we built a Resnet-based multi-flow network to learn the local information and the longitudinal information from the full chest sequence slice. To assist doctors in decision-making, the attention mechanism integrated into the network, which can locate the key slices and key parts from a full chest CT sequence of patients. We have systematically evaluated our method on the CT images of 1031 cases, including 420 COVID-19 cases, 311CAP cases, and 300 non-pneumonia cases. Our method could obtain an average accuracy of 82.3%, with 85.7% sensitivity and 86.4 % specificity, which outperformed previous works. © 2022 IEEE.

Longitudinal observation of viral load in patients infected with Omicron variant and its relationship with clinical symptoms.

Objective: In 2022, a new coronavirus variant (Omicron) infection epidemic broke out in Shanghai, China. However, it is unclear whether the duration of this omicron variant is different from that of the prototype strain. Methods: We retrospectively analyzed 157 cases of Omicron variant infection in Taizhou Public Health Center from March 29, 2022, to April 18, 2022, and observed the dynamics of nucleic acid Ct values during the admission and discharge of patients. Clinical and laboratory indicators of these patients were also obtained. Results: Compared to the prototype strain, the Omicron variant showed a broad population susceptibility in infected individuals (regardless of age and presence of underlying disease) and had slight damage to the immune system and renal function; the viral loads peaked was 2-3 days from disease onset; the median duration of omicron variant was 15-18 days; the nucleic acid Ct value of nasopharyngeal swabs of infected patients is lower than that of throat swabs, and the Ct value of oropharyngeal swabs is unstable during the recovery period. Conclusion: Therefore, we found that the time to peak viral load of this Omicron variant was 2-3 days after the onset of the disease, and the duration was 15-18 days; symptomatic patients had higher viral load and longer hospitalization time. This finding will provide a basis for understanding omicron variants and formulating the national prevention and control strategy.

Radiological Diagnosis of SARS-CoV-2 Infected Patients by Automated Classification of Chest Radiographs using DTL

SARS-CoV-2 is a strain of coronavirus in the Orthocoronavirinae subfamily, which was first identified in Wuhan, China, in December 2019. COVID-19 is the name of the disease produced by SARS-CoV-2. Because SARS-CoV-2 is cytopathic to airway epithelial cells and alveolar cells, researchers showed that combining features extracted from radiographic images with laboratory results may significantly aid in the early detection of the infection. Standard features seen in radiographs of patients with coronavirus are infiltrated or patchy opacities, like several features of other forms of viral pneumonia. In the early stages of COVID-19 infection, no abnormalities can be observed on radiographic images. However, as the disease unfolds, COVID-19 progressively manifests as a typical unilateral inflammation involving the middle and upper or lower lungs, sometimes with consolidation. Almost every hospital has at least one radiographer routinely used for diagnosing pneumonia, lymph nodes, and other conditions. Unfortunately, acquiring radiographic images and analyzing COVID-19 analyzing by a radiologist is time-consuming. Therefore, this work aims to assess the detection capability of the viral respiratory disease caused by the SARSCoV-2 virus from radiographic images using a deep transfer learning model implemented in a development environment for numerical computation and statistical analysis called MATLAB. © 2022 IEEE.

SARS-CoV-2 Detection Using Chest X-Ray Images with Deep Learning Methods

With the coronavirus invading the world from China since December 2019, it has led every country into crisis. The World Health Organization (WHO) declared the coronavirus a pandemic on March 11, 2020. The search for solutions began all over the world. One of the solutions is applying artificial neural networks for classification methods on chest X-ray images. Chest X-ray (CXR) scan images can be considered a confirmatory approach as they are quick to obtain and easily accessible. When these images are used, transfer learning from deep learning methods is the most preferred method to detect infected patients. Three different datasets, varying in different sample sizes, were used for training our models and further detailed analysis. The outputs of the results are measured by looking at the F1 score and accuracy. With the comparative performance analysis, it was seen that the InceptionV3 and Xception models had the highest overall accuracy and F1 scores than the other models for our datasets. © 2022 IEEE.

COVID-19 Detection Through Deep Learning Algorithms using Chest X-ray Images

COVID-19 became a pandemic affecting the lives of every human globally by the end of 2019. The disease impaired the lungs of infected patients. Precise prediction and diagnosis of COVID-19 disease are challenging due to its resemblance to viral pneumonia. Using multiple deep learning approaches, the researchers used chest X-ray (CXR) imaging to diagnose COVID-19. The X-ray image dataset from Kaggle is used for the study by selecting the COVID-19 and normal class. InceptionV3, MobileNetV2, VGG19,VGG16 and ResNet50 are the five neural networks used for binary classification of COVID-19. The accuracy of MobileNetV2 surpasses that of the remainder of the model by 93.02%. However, it has a compilation time of 1836 seconds per epoch. Besides, VGG16 has an accuracy of 92.37%, with a compilation time of 603 seconds per epoch. Compared to these models, Inceptonv3, Resnet50 and VGG19 perform with an accuracy score of 86.42%, 68.34% and 91.79%. Applying deep learning techniques to COVID-19 radiological imaging holds great promise for enhancing the accuracy of diagnosis when in comparison to the gold standard RT-PCR test and assisting healthcare professionals in making decisions quickly © 2022 IEEE.

Early Usability Evaluation of a Relational Agent for the COVID-19 Pandemic

Relational agents (RAs) have shown effectiveness in various health interventions with and without healthcare professionals (HCPs) and hospital facilities. RAs have not been widely researched in COVID-19 context, although they can give health interventions during the pandemic. Addressing this gap, this work presents an early usability evaluation of a prototypical RA, which is iteratively designed and developed in collaboration with infected patients (n=21) and two groups of HCPs (n=19, n=16) to aid COVID-19 patients at various stages about four main tasks: testing guidance, support during self-isolation, handling emergency situations, and promoting post-infection mental well-being. The prototype obtained an average score of 58.82 on the system usability scale (SUS) after being evaluated by 98 people. This result implies that the suggested design still needs to be improved for greater usability and adoption. © 2022 Owner/Author.

Outcomes of COVID-19 in Adult Males With Hemophilia A: A Propensity Score-Matched Analysis.

Background Hypercoagulability is a major pathologic event in COVID-19. Factor VIII plays an important role in hemostasis, and high levels of factor VIII have been shown to be associated with an increased risk of thrombosis and severe disease. Little is known about the impact of COVID-19 on clinical outcomes in patients with hemophilia A. Methodology Retrospective data of adult male patients with COVID-19 with and without hemophilia A were retrieved from the TriNetX database (Cambridge, USA). The 1:1 propensity score-matching was performed to balance baseline characteristics. Patients were matched for age, race, body mass index, and medical comorbidities. Thirty-day outcomes were assessed. Results We identified 1,758 patients with pre-existing hemophilia A diagnosis prior to COVID-19 diagnosis and 5,191,908 comparators. After 1:1 propensity score matching, groups were balanced on demographics and comorbidities. All-cause mortality rates were similar between the two groups (HR 0.805; 95% CI 0.467-1.389). The frequency of severe infection, ICU admission, and composite thrombotic events did not differ between the groups. Patients with hemophilia A were hospitalized more frequently than those without a history of hemophilia A (19.2% vs. 14.4%; p<0.05). Additionally, gastrointestinal (GI) bleeding and composite bleeding events occurred more frequently in patients with hemophilia A (3.2% vs. 2.2%; p<0.05 and 4.0% vs. 2.8%; p<0.05, respectively). Conclusions The mortality of individuals with hemophilia A due to COVID-19 is comparable to the general population but with higher risks of hospitalization and bleeding.

The Comparison of Therapeutic With Prophylactic Anticoagulation on Mortality, Risk of Bleeding, and Thromboembolism in Hospitalized Patients With COVID-19: A Systematic Review.

Thromboembolism is one of the most severe manifestations of coronavirus disease 2019 (COVID-19). Thrombotic complications have been reported even with the administration of thromboprophylaxis. This has led many experts to have variable opinions on the most effective prophylactic strategy and to anticipate the discovery of the ideal dosing of anticoagulation to reduce thromboembolic events and related mortality. We performed a systematic review to evaluate whether therapeutic-dose anticoagulation is superior to prophylactic-dose anticoagulation by comparing mortality rates, bleeding risks, and rates of thromboembolism. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to create our systematic review. Twenty-two records were collected from PubMed, PubMed Central (PMC), and Medical Literature Analysis and Retrieval System Online (MEDLINE), after which they undertook quality appraisals. A total of 124 studies were analyzed in six systematic reviews and meta-analyses, one pooled analysis, two multicenter retrospective cohort studies, one observational study, one retrospective chart review, one evidence-based protocol, and four narrative reviews.