Combined Nasal, Oropharyngeal Povidone Iodine Plus Glycyrrhizic Acid Sprays, Accelerate Clinical and Laboratory Recovery and Reduces Household Transmission of SARS-CoV-2: A Randomized Placebo-Controlled Clinical Trial.

The COVID-19 pandemic is still posing challenging health and economic problems. Effective broad-spectrum antiviral therapy is urgently needed for the control of early SARS-CoV-2 infection to limit its spread and mutations. In this randomized placebo-controlled clinical study, we tested the effects of intranasal and oropharyngeal delivery of a compound of povidone-iodine 0.5% and glycyrrhizic acid 2.5 mg/ml on the laboratory (PCR) and clinical recovery from SARS-CoV-2 patients and their household contacts. 353 patients suspected of having COVID-19 infection were screened by chest CT and nasopharyngeal swab tests (PCR). 200 patients were randomly allocated to two equal groups: treatment and placebo groups. Treatment accelerated the recovery of PCR on days 4, 7, and 10, as evidenced by PCR-positive patients (70, vs. 99%, 20 vs. 65%, 1 vs. 10%) in both the treated and placebo groups, respectively. Treatment enhanced the early recovery of symptoms [day 7.6 ± 2 (CI 7:8.3) vs. 8.9 ± 2 (CI 8.3:9.6)]. Treatment promoted early recovery of anosmia and ageusia [5.6 ± 1 (CI, 4.8:6.4) vs. 11 ± 3 days, (CI, 10.8:12)] in both the treated and control groups (P < 0.0001). There was a notable reduction in transmission of the virus among the household close contacts in the treatment group (4%) vs. 76% in the placebo group. Combined PVI-GA nasal and oropharyngeal spray accelerates both laboratory and clinical recovery of SARS-CoV-2 infected patients in the early phases of the disease and reduces the household spread of the virus; thus, it may play an important role in controlling coronavirus outbreaks. Clinical Trial Registration: https://pactr.samrc.ac.za, PACTR202101875903773.

Robo-Nurse Healthcare Complete System Using Artificial Intelligence

Significant with COVID-19 pandemic breakout, and the high risk of acquiring this infection that is facing the Healthcare Workers (HCWs), a safe alternative was needed. As a result, robotics, artificial intelligence (AI) and internet of things (IoT) usage rose significantly to assist HCWs in their missions. This paper aims to represent a humanoid robot capable of performing HCWs’ repetitive scheduled tasks such as monitoring vital signs, transferring medicine and food, or even connecting the doctor and patient remotely, is an ideal option for reducing direct contact between patients and HCWs, lowering the risk of infection for both parties. Humanoid robots can be employed in a variety of settings in hospitals, including cardiology, post-anesthesia care, and infection control. The creation of a humanoid robot that supports medical personnel by detecting the patient's body temperature and cardiac vital signs automatically and often and autonomously informs the HCWs of any irregularities is described in this study. It accomplishes this objective thanks to its integrated mobile vital signs unit, cloud database, image processing, and Artificial Intelligence (AI) capabilities, which enable it to recognize the patient and his situation, analyze the measured values, and alert the user to any potentially worrisome signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Intelligent Model for Rapid Diagnosis of Patients with COVID-19 Based on ANFIS

Early classification of coronavirus disease (COVID-19) has a vital role in controlling the rapid spread of this disease and saving patients’ lives. The fast spread of COVID-19 increased the diagnostic encumbrance of radiologists. Computed tomography (CT) imaging is an effective tool to detect COVID-19 but requires a radiology expert and takes a large time. The machine learning (ML) based models are considered as one of the important ways to analyze the CT images to detect the COVID-19 cases. Therefore, this paper has been focused on finding a suitable machine learning algorithm that can automatically analyze CT images to extract and detect COVID-19 pneumonia features with higher accuracy. This research used classifiers as the Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision tree (DT) to classify CT images into COVID-19 and NonCOVID-19. This paper also designed an Adaptive Neuro-Fuzzy Inference System (ANFIS) based model to achieve a fast and accurate diagnosis of Covid-19. The CT exams of other lung diseases were included in the dataset to improve the model performance. So, the NonCOVID-19 results of the proposed models include the other lung diseases. Confusion matrices and ROC analyses of the proposed models are analyzed using 5-fold cross-validation. During the study and testing of several proposed models, the experimental results showed that the performance of the ANFIS proposed model achieved the best performance with an accuracy of 98.63% and 0.02 s testing time. In the main purpose of this study is to shed light on the ML-based COVID-19 detection models for researchers working with ML techniques and help avoid proven failures, especially for small imprecise datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Proposed Modified SEIQR Epidemic Model to Analyze the COVID-19 Spreading in Saudi Arabia

The key aim of this paper is to construct a modified version of the SEIQR essential disease dynamics model for the COVID-19 emergence. The modified SEIQR pandemic model takes a groundbreaking approach to evaluate and monitor the COVID-19 epidemic. The complex studies presented in this paper are based on real-world data from Saudi Arabia. A reproduction number and a systematic stability analysis are included in the new version of SEIQR model dynamics. Using the Jacobian linearization process, we can obtain the domain of the solution and the state of equilibrium based on the modified SEIQR model. The equilibrium and its importance have been identified, and the disease-free stability of the equilibrium has been investigated. The reproduction number was calculated using internal metrics, and the global stability of the current model's equilibrium was demonstrated using Lyapunov's stability theorem. To see how well the SEIQR proposed model went, it was compared to real COVID-19 spread data in Saudi Arabia. According to the results, the new SEIQR proposed model is a good match for researching the spread of epidemics like COVID-19. In the end, we presented an optimal protocol to prevent the dissemination of COVID-19. Staying at home and transporting sick people as far as possible to a safe region is the most effective strategy to prevent COVID-19 spread. It is critical to offer infected people safe and effective treatment, as well as antibiotics and nutrients to non-affected people. To detect confirmed infections, we must provide more effective and reliable diagnostic methods. Furthermore, increasing understanding of how to recognize the disease, its symptoms, and how to confirm the infection.

Comparative evaluation of semi-quantitative CT-severity scoring versus serum lactate dehydrogenase as prognostic biomarkers for disease severity and clinical outcome of COVID-19 patients

Background: Coronavirus disease 2019 pandemic causes significant strain on healthcare infrastructure and medical resources. So, it becomes crucial to identify reliable predictor biomarkers for COVID-19 disease severity and short term mortality. Many biomarkers are currently investigated for their prognostic role in COVID-19 patients. Our study is retrospective and aims to evaluate role of semi-quantitative CT-severity scoring versus LDH as prognostic biomarkers for COVID-19 disease severity and short-term clinical outcome. Results: Two hundred sixty-six patients between April 2020 and November 2020 with positive RT-PCR results underwent non-enhanced CT scan chest in our hospital and were retrospectively evaluated for CT severity scoring and serum LDH level measurement. Data were correlated with clinical disease severity. CT severity score and LDH were significantly higher in severe and critical cases compared to mild cases (P value < 0.001). High predictive significance of CT severity score for COVID-19 disease course noted, with cut-off value ≥ 13 highly predictive of severe disease (96.96% accuracy);cut-off value ≥ 16 highly predictive of critical disease (94.21% accuracy);and cut-off value ≥ 19 highly predictive of short-term mortality (92.56% accuracy). CT severity score has higher sensitivity, specificity, positive, and negative predictive values as well as overall accuracy compared to LDH level in predicting severe, critical cases, and short-term mortality. Conclusion: Semi-quantitative CT severity scoring has high predictive significance for COVID-19 disease severity and short-term mortality with higher sensitivity, specificity, and overall accuracy compared to LDH. Our study strongly supports the use of CT severity scoring as a powerful prognostic biomarker for COVID-19 disease severity and short-term clinical outcome to allow triage of need for hospital admission, earlier medical interference, and to effectively prioritize medical resources for cases with high mortality risk for better decision making and clinical outcome. © 2021, The Author(s).

Study on the SEIQR model and applying the epidemiological rates of COVID-19 epidemic spread in Saudi Arabia.

This article attempts to establish a mathematical epidemic model for the outbreak of the new COVID-19 coronavirus. A new consideration for evaluating and controlling the COVID-19 outbreak will be constructed based on the SEIQR Pandemic Model. In this paper, the real data of COVID-19 spread in Saudi Arabia has been used for the mathematical model and dynamic analyses. Including the new reproductive number and detailed stability analysis, the dynamics of the proposed SEIQR model have been applied. The local sensitivity of the reproduction number has been analyzed. The domain of solution and equilibrium based on the SEIQR model have been proved using a Jacobian linearization process. The state of equilibrium and its significance have been proved, and a study of the integrity of the disease-free equilibrium has been carried out. The Lyapunov stability theorem demonstrated the global stability of the current model equilibrium. The SEIQR model has been numerically validated and projected by contrasting the results from the SEIQR model with the actual COVID-19 spread data in Saudi Arabia. The result of this paper shows that the SEIQR model is a model that is effective in analyzing epidemic spread, such as COVID-19. At the end of the study, we have implemented the protocol which helped the Saudi population to stop the spread of COVID-19 rapidly.

SARS-CoV-2 infection in an infant with non-respiratory manifestations: a case report

Background Coronavirus disease (COVID-19) presents in children usually with less severe manifestations than in adults. Although fever and cough were reported as the most common symptoms, children can have non-specific symptoms. We describe an infant with aplastic anemia as the main manifestation. Case presentation We describe a case of SARS-CoV-2 infection in an infant without any respiratory symptoms or signs while manifesting principally with pallor and purpura. Pancytopenia with reticulocytopenia was the predominant feature in the initial laboratory investigations, pointing to aplastic anemia. Chest computed tomography surprisingly showed typical findings suggestive of SARS-CoV-2 infection. Infection was later confirmed by positive real-time reverse transcription polymerase chain reaction assay (RT-PCR) for SARS-CoV-2. Conclusions Infants with COVID-19 can have non-specific manifestations and a high index of suspicion should be kept in mind especially in regions with a high incidence of the disease. Chest computed tomography (CT) and testing for SARS-CoV-2 infection by RT-PCR may be considered even in the absence of respiratory manifestations.

On Improving Toll Accuracy for COVID-like Epidemics in Underserved Communities Using User-generated Data

This paper envisions using user-generated data as a cheap way to improve accuracy of epidemic tolls in underserved communities. The global widespread of COVID-19 pandemic has imposed several unprecedented challenges. One of these challenges is constantly monitoring the unprecedented epidemic widespread at a fine-granular spatial scale, so experts can model, understand, and prevent disease transmission and field personnel can reach and treat infected people. Unfortunately, the limited resources compared to the pandemic widespread has led to a significant number of unreported cases in underserved communities and developing countries, including a large number of severe cases. We propose in this paper enhancing epidemic case reporting in underserved communities through exploiting the power of data that are posted by people on web. Our vision is building a data analysis pipeline that filters and categories use-generated data objects to provide informal estimates for tolls in unreachable regions and enhance estimates in other regions. The pipeline consist of five stages, that starts with filtering epidemic-specific data to visualize advanced aggregates to end users. We also discuss several technical challenges that face different stages of the pipeline. © 2020 ACM.

A modified SEIR model applied to the data of COVID-19 spread in Saudi Arabia.

The Susceptible-Exposed-Infectious-Recovered (SEIR) model is an established and appropriate approach in many countries to ascertain the spread of the coronavirus disease 2019 (COVID-19) epidemic. We wished to create a new COVID-19 model to be suitable for patients in any country. In this work, a modified SEIR model was constructed. We used the real data of COVID-19 spread in Saudi Arabia for statistical analyses and complex analyses. The reproduction number and detailed review of stability demonstrated the complexities of our proposed SEIR model. The solution and equilibrium condition were explored based on Jacobian's linearization approach to the proposed SEIR model. The state of equilibrium was demonstrated, and a stability study was conducted in the disease-free environment. The reproduction number was measured sensitively against its internal parameters. Using the Lyapunov principle of equilibrium, the overall consistency of balance of our model was demonstrated. Findings using the SEIR model and observed outcomes due to COVID-19 spread in Saudi Arabia were compared. The modified SEIR model could enable successful analyses of the spread of epidemics such as COVID-19. An "ideal protocol" comprised essential steps to help Saudi Arabia decelerate COVID-19 spread. The most important aspects are to stay at home as much as possible and for infected people to remain in an isolated zone or secure area.

A new dynamical modeling SEIR with global analysis applied to the real data of spreading COVID-19 in Saudi Arabia.

SEIR model is a widely used and acceptable model to distinguish the outbreak of the COVID-19 epidemic in many countries. In the current work, a new proposed SEIR model as a mathematical model for the outbreak of novel coronaviruses COVID-19 will be constructed. The new proposed SEIR pandemic model provides a new vision for evaluations and management of the epidemic of COVID-19 infection. For mathematical modeling and dynamic analyses, this paper uses the real data of spreading COVID-19 in Saudi Arabia. The dynamics of the proposed SEIR model are presented with the reproduction number and the extensive stability analysis. We discussed the domain of the solution and equilibrium situation based on the proposed SEIR model by using Jacobian's method of linearization. The condition of equilibrium and its uniqueness has been proved, and the stability analysis of disease-free equilibrium has been introduced. A sensitivity analysis of the reproduction number against its internal parameters has been done. The global stability of the equilibrium of this model has been proved by using Lyapunov's Stability theorem. A numerical verification and predictions of the proposed SEIR model have been made with comparing the results based on the SEIR model and the real data due to the spreading of the COVID-19 in Saudi Arabia. The proposed SEIR model is a successful model to analyze the spreading of epidemics like COVID-19. This work introduces the ideal protocol, which can help the Saudi population to breakdown spreading COVID-19 in a fast way.

An Integrated IoT System to Control the Spread of COVID-19 in Egypt

Coronavirus disease 2019 (COVID-19) is one of the most dangerous respiratory illness through the last one hundred years. Its dangerous is returned to its ability to spread quickly between people. This paper proposes a smart real solution to help Egyptian government to track and control the spread of COVID-19. In this paper, we suggest an integrated system that can ingest big data from different sources using Micro-Electro-Mechanical System (MEMS) IR sensors and display results in an interactive map, or dashboard, of Egypt. The proposed system consists of three subsystems, which are: Embedded Microcontroller (EM), Internet of Things (IoT) and Artificial Intelligent (AI) subsystems. The EM subsystem includes accurate temperature measuring device using IR sensors and other detection components. The EM subsystem can be used in the entrance of places like universities, schools, and subways to screen and check temperature of people from a distance within seconds and get data about suspected cases. Then, the IoT subsystem will transmit the collected data from individuals such as temperature, ID, age, gender, location, phone number. etc., to the specific places and organizations. Finally, a software based on AI analysis will be applied to execute statistics and forecast how and to what extent the virus will spread. Due to the important role of Geographic Information Systems (GIS) and interactive maps, or dashboards, in tracking COVID-19, this paper introduces an advanced dashboard of Egypt. This dashboard has been introduced to locate and tally confirmed infections, fatalities, recoveries and present the statistical results of AI model. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.