The COVEG score to predict severity and mortality among hospitalized patients with COVID-19.

INTRODUCTION: COVID-19 severity and mortality predictors could determine admission criteria and reduce mortality. We aimed to evaluate the clinical-laboratory features of hospitalized patients with COVID-19 to develop a novel score of severity and mortality. METHODOLOGY: This retrospective cohort study was conducted using data from patients with COVID-19 who were admitted to five Egyptian university hospitals. Demographics, comorbidities, clinical manifestations, laboratory parameters, the duration of hospitalization, and disease outcome were analyzed, and a score to predict severity and mortality was developed. RESULTS: A total of 1308 patients with COVID-19, with 996 (76.1%) being moderate and 312 (23.9%) being severe cases, were included. The mean age was 46.5 ± 17.1 years, and 61.6% were males. The overall mortality was 12.6%. Regression analysis determined significant predictors, and a ROC curve defined cut-off values. The COVEG severity score was defined by age ≥ 54, D-dimer ≥ 0.795, serum ferritin ≥ 406, C-reactive protein ≥ 30.1, and neutrophil: lymphocyte ratio ≥ 2.88. The COVEG mortality score was based on COVEG severity and the presence of cardiac diseases. Both COVEG scores had high predictive values (area under the curve 0.882 and 0.883, respectively). CONCLUSIONS: COVEG score predicts the severity and mortality of patients with COVID-19 accurately.

Sofosbuvir/ledipasvir in combination or nitazoxanide alone are safe and efficient treatments for COVID-19 infection: A randomized controlled trial for repurposing antivirals.

BACKGROUND AND STUDY AIMS: Currently, there is no therapy approved for COVID-19. We evaluated the efficacy and safety of sofosbuvir/ledipasvir and nitazoxanide for the treatment of patients with COVID-19 infection. PATIENTS AND METHODS: A multicenter, open-label randomized controlled trial included one hundred and ninety patients with non-severe COVID-19 infection. Patients were randomized into three groups. All groups received standard care treatment (SCT). In addition, group 1 received sofosbuvir/ledipasvir, and group 2 received nitazoxanide. Follow-up by reverse-transcriptase polymerase chain reaction (RT-PCR) was done at intervals of 5, 8, 11, and 14 days. The primary endpoint was viral clearance. RESULTS: Viral clearance was significantly higher in the sofosbuvir/ledipasvir and nitazoxanide groups compared to the SCT group in all follow-up intervals (p < 0.001). In the sofosbuvir/ledipasvir arm, 36.9% showed early viral clearance by day 5. By day 14, 83.1% of the sofosbuvir/ledipasvir group, 39.7% of the nitazoxanide group, and 19.4% of the SCT group tested negative for SARS-CoV-2. Sofosbuvir/ledipasvir and nitazoxanide treatment were the only significant factors in Cox regression of negative RT-PCR with the highest OR (17.88, 95% CI: 6.66-47.98 and 2.59, 95% CI: 1.11-6.07, respectively). No mortality or serious adverse events were recorded. CONCLUSION: The addition of sofosbuvir/ledipasvir or nitazoxanide to the SCT results in an early and high viral clearance rate in mild and moderate patients with COVID-19. These drugs represent a safe and affordable treatment for COVID-19.

Genome sequencing reveals existence of SARS-CoV-2 B.1.1.529 variant in Egypt.

BACKGROUND: Several SARS-CoV-2 variants with increased transmissibility and/or potential immune escape have emerged and rapidly spread worldwide. Ongoing surveillance analyses are performed worldwide to designate new variants of concern (VOC) of coronavirus. MAIN TEXT: This report identifies the first Egyptian patient with a confirmed SARS-CoV-2 omicron variant. The patient showed positivity on reverse transcriptase-polymerase chain reaction and full genome sequencing was performed to confirm the variant. The mutations found in the variant were compared with the GISAID reference strain hCoV-19/Wuhan/WIV04/2019. Genome BLAST showed the highest similarity to omicron variants isolated in South Africa. Phylogenetic analysis revealed that the variant belongs to the 21K clade. CONCLUSIONS: The study indicates the importance of information-sharing among global public health partners. Moreover the importance of implementation of full genome sequencing to rapidly identify and track the new SARS-CoV-2 variants.

Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control.

This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between 21% and 41%, total delay ranging between 40% and 55%, and emission levels/fuel consumption ranging between 12% and 20%. The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers.

Estimation of Traffic Stream Density Using Connected Vehicle Data: Linear and Nonlinear Filtering Approaches.

The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates-with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application.

Developing a Neural-Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data.

This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count estimates, considering real-time estimates of the system noise characteristics. The AKF utilizes only real-time probe vehicle data. The AKF is demonstrated to outperform the traditional Kalman filter, reducing the prediction error by up to 29%. In addition, the paper introduces a novel approach that combines the AKF with a neural network (AKFNN) to enhance the vehicle count estimates, where the neural network is employed to estimate the probe vehicles' market penetration rate. Results indicate that the accuracy of vehicle count estimates is significantly improved using the AKFNN approach (by up to 26%) over the AKF. Moreover, the paper investigates the sensitivity of the proposed AKF model to the initial conditions, such as the initial estimate of vehicle counts, initial mean estimate of the state system, and the initial covariance of the state estimate. The results demonstrate that the AKF is sensitive to the initial conditions. More accurate estimates could be achieved if the initial conditions are appropriately selected. In conclusion, the proposed AKF is more accurate than the traditional Kalman filter. Finally, the AKFNN approach is more accurate than the AKF and the traditional Kalman filter since the AKFNN uses more accurate values of the probe vehicle market penetration rate.

A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing.

This paper presents a novel de-centralized flexible phasing scheme, cycle-free, adaptive traffic signal controller using a Nash bargaining game-theoretic framework. The Nash bargaining algorithm optimizes the traffic signal timings at each signalized intersection by modeling each phase as a player in a game, where players cooperate to reach a mutually agreeable outcome. The controller is implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software, comparing its performance to that of a traditional decentralized adaptive cycle length and phase split traffic signal controller and a centralized fully-coordinated adaptive phase split, cycle length, and offset optimization controller. The comparisons are conducted in the town of Blacksburg, Virginia (38 traffic signalized intersections) and in downtown Los Angeles, California (457 signalized intersections). The results for the downtown Blacksburg evaluation show significant network-wide efficiency improvements. Specifically, there is a 23.6 % reduction in travel time, a 37.6 % reduction in queue lengths, and a 10.4 % reduction in CO 2 emissions relative to traditional adaptive traffic signal controllers. In addition, the testing on the downtown Los Angeles network produces a 35.1 % reduction in travel time on the intersection approaches, a 54.7 % reduction in queue lengths, and a 10 % reduction in CO 2 emissions compared to traditional adaptive traffic signal controllers. The results demonstrate significant potential benefits of using the proposed controller over other state-of-the-art centralized and de-centralized adaptive traffic signal controllers on large-scale networks both during uncongested and congested conditions.