Analysis of risk factors for seizures and short-term reoccurrence of seizures in febrile children with SARS-CoV-2 infection: an observational study (preprint)

Background:With the epidemic of the Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) variant Omicron, its accompanying neurological manifestations have gradually attracted attention.The main objective of this study was to compare seizures in febrile children with and without coronavirus disease 2019(COVID-19) and to conduct a short-term follow-up in the COVID-19 positive group to investigate the risk factors for short-term recurrence of seizures in children with febrile seizures(FS). Methods: Retrospective analysis of patients admitted to the Children's Hospital of Chongqing Medical University for fever and seizures between October 1 and December 30, 2022.Based on the results of SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR), the patients were divided into a COVID-19 positive group and a COVID-19 negative group.Moreover,we followed up patients in the COVID-19-positive group for 3 months using outpatient or telephone follow-up, and the main content of follow-up included whether the patients had seizures after discharge and whether there were neurological abnormalities. Results:Compared with the COVID-19-negative group, the COVID-19-positive group had a higher proportion of seizure duration ≥ 15 minutes(18.7%VS5.1%;P=0.001), seizure ≥ 2 time(54.4%VS41.0%;P=0.024), status epilepticus(15.4%VS5.1%;P=0.005), and Electroencephalogram (EEG) abnormalities(29.4%VS13.6%;P=0.016).Seizures ≥2 time[P=0.015,OR(95% CI)=4.632(1.347-15.928)], peak temperature ≤39°C[P=0.001,OR(95% CI)=6.296(2.059-19.254)], and history of convulsions[P=0.005,OR(95% CI)=5.628(1.707-18.550)] were risk factors for recurrence of seizures within a short period of time in children with covid-19 infected febrile convulsions.In the COVID-19 positive group, three patients died and four patients had residual cognitive or motor dysfunction. Conclusions:The seizures were more severe in the COVID-19 positive group compared to the COVID-19 negative group.In addition, patients with COVID-19 who present with seizures and persistent impaired consciousness need to be alerted to serious neurological disorders such as acute necrotizing encephalopathy.

Searching for a safe haven to crude oil: Green bond or precious metals?

In this paper, we explore whether the green bond, a kind of rarely discussed fixed-income asset, acts as a hedge or safe haven to crude oil in extreme market conditions, by comparing the results with precious metals. The green bond has negative correlations with crude oil when the COVID-19 pandemic and the Russia-Ukraine conflict outbreak, and the highest risk reduction effectiveness among all assets, indicating that investors benefit the most from adding the green bond to their portfolios. Applying the model purposed by Baur and McDermott (2010), we further find that the green bond is both a strong safe haven and a strong hedge for the crude oil market, gold is a weak safe haven and a strong hedge, silver only acts as a weak hedge, and other precious metals are neither safe haven nor hedge. Our results reveal that crude oil market investors can hedge the risk during the extreme rise and fall periods by including the green bond in their portfolios.

Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning (preprint)

Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise,they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to demonstrate the efficiency and effectiveness of the proposed algorithm. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.

Dissecting Bond Volatility (preprint)

This paper documents a positive cross-sectional relation between returns and lagged idiosyncratic volatility (IVOL) in the corporate bond market. The relation is stronger following periods of low funding liquidity due to a funding liquidity driven decrease in returns and its subsequent reversal. Three exogenous shocks – (i) the Volcker Rule which restricted the participation of dealers in the corporate bond market in 2014, (ii) the Global Financial Crisis of 2008, and (iii) the COVID-19 crisis of 2020, are used to establish causality between funding liquidity and the positive IVOL-return relation.

Mitigating COVID-19 Infection Disease Transmission in Indoor Environment Using Physical Barriers (preprint)

During the normalized phase of COVID-19, droplets or aerosol particles produced by infected personnel are considered as the potential source of infection with uncertain exposure risk. As such, in densely populated open spaces, it is necessary to adopt strategies to mitigate the risk of infection disease transmission while providing sufficient ventilation air. An example of such strategies is use of physical barriers. In this study, the impact of barrier heights on the spread of aerosol particles is investigated in an open office environment with the well-designed ventilation mode and supply air rate. The risk of infection disease transmission is evaluated using simulation of particle concentration in different locations and subject to a number of source scenarios. It was found that a barrier height of at least 60cm above the desk surface is needed to effectively prevent the transmission of viruses. For workstations within 4m from the outlet, a 70cm height is considered, and with a proper ventilation mode, it is shown that the barriers can reduce the risk of infection by 72%. However, for the workstations further away from the outlet (beyond 4m), the effect of physical barrier cannot be that significant. In summary, this study provides a theoretical analysis for implementing physical barriers, as a low-cost mitigation strategy, subject to various height scenarios and investigation of their effectiveness in reducing the infection transmission probability.

Outcomes of Recovered COVID-19 Patients: 6 Months of Sequential Observations at a Designated Tertiary Center (preprint)

Objectives: An ongoing global pandemic of coronavirus disease 2019 (COVID-19) has affecting almost 100,000,000 cases with 2,100,000 deaths worldwide. However, the long-term outcomes of recovered patients remain to be defined.Methods: This is a prospective observational study of patients with COVID-19 using sequential assessments after hospital discharge from a designated tertiary center in Hefei, China. We examined clinical symptom, chest CT imaging, pulmonary function, and 6-min walk distance (6-MWD).Results: There were 62, 61 and 51 discharged patients enrolled the 1-month, 3-month and 6-month observations, respectively. Symptoms persisted in 24 (39%), 25 (41%) and 5 (10%) patients, mainly cough in 31%, 15% and 8% of them, respectively. Mild restrictive pulmonary impairment was detected in 11%, 10%, 12% of patients at 1, 3, 6-month follow-up. Although chest CT scores were overall gradually improved at 1 month (5.0±5.1), 3 months (3.0±4.5) and 6 months (2.0±3.3) compared with that during hospitalization (11.0±6.8), residual CT abnormalities were seen in 73%, 54% and 43% of them at 1, 3, 6 months. At 6-month follow-up, the 6MWD was 541±59 m in these recovered patients, which was significantly lower compared to healthy controls (589±75 m，p<0.01). Only the steroid treatment during hospitalization (p=0.009, OR 12.091, 95% CI 1.882 to 77.678) was associated with abnormal CT score at 6 months.Conclusions: At 6 months after hospital discharge, respiratory symptoms and pulmonary function were improved in most COVID-19 patients while residual impairments were still present in both chest CT images and exercise capacity. 

Contact Tracing Enhances the Efficiency of COVID-19 Group Testing (preprint)

Group testing can save testing resources in the context of the ongoing COVID-19 pandemic. In group testing, we are given $n$ samples, one per individual, and arrange them into $m < n$ pooled samples, where each pool is obtained by mixing a subset of the $n$ individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we use side information (SI) collected from contact tracing (CT) within non-adaptive/single-stage group testing algorithms. We generate data by incorporating CT SI and characteristics of disease spread between individuals. These data are fed into two signal and measurement models for group testing, where numerical results show that our algorithms provide improved sensitivity and specificity. While Nikolopoulos et al. utilized family structure to improve non-adaptive group testing, ours is the first work to explore and demonstrate how CT SI can further improve group testing performance.

Risk Factors of Secondary Infections in Severe and Critical Patients Hospitalized with COVID-19: A Case-Control Study (preprint)

Background: Severe patients hospitalized with COVID-19 suffered secondary infections which greatly increased the length of hospital stay and the mortality. We aimed to explore risk factors of secondary infections that can help clinicians early implement preventive measures to dispose of severe and critical inpatients with COVID-19.Methods: A case-control study enrolled 238 severe and critical patients with COVID-19. Characteristics of cases and controls were compared.Results: Severity of illness on admission, ICU admission, ventilator, central venous catheterization were common in the cases, however almost none of these factors was observed in the controls. Multivariable regression showed risk factors of secondary infections included male (OR 4.08; 95% CI 1.58-10.50), age 65 or older (OR 3.11; 95% CI 1.25-7.76), heart diseases (OR 3.96; 95% CI 1.40-11.27), hypoproteinemia on admission (OR 6.41; 95% CI 1.65-24.92) and corticosteroids (OR 19.83; 95% CI 7.3-53.55) and proton-pump inhibitors (OR 3.96; 95% CI 1.51-10.37).Conclusions: male, older age, heart diseases, hypoproteinemia, corticosteroid and proton-pump inhibitors were independent risk factors of secondary infections. Inpatients needing ICU admission and invasive devices still need to be given optimal cares and to be minimized the duration.

Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients (preprint)

IntroductionThe Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the COVID-19 pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. MethodsWe studied adult patients admitted with COVID-19 to non-ICU care at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of ICU-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. ResultsAmong 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. Median age of the cohort was 64 (IQR 53-75) with 168 (43%) African Americans and 169 (43%) women. Area under the receiver-operating-characteristic curve (AUC) of the EDI was 0.79 (95% CI 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically-relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. ConclusionWe found the EDI identifies small subsets of high- and low-risk COVID-19 patients with fair discrimination. We did not find evidence of bias by race or sex. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among COVID-19 patients.

The added value of chest high-resolution CT findings of Corona Virus Disease 2019 in predicting severity of disease (preprint)

The aim of this study was to retrospectively analyze chest thin-section high-resolution CT (HRCT) findings for 32 patients with Corona Virus Disease 2019 (COVID-19) and clarify the correlation between CT data and laboratory results. 30 patients presented with abnormal initial CT scans. Of 30 patients, COVID-19 showed the involvement of bilateral lungs in 24 (80%), involvement of more than two lobes in 24 (80%), ground-glass opacities without consolidation in 27 (90%), ground-glass opacities with consolidation in 23 (76.7%), opacities with irregular intralobular lines in 26 (86.7%), opacities with round morphology in 25 (83.3%), and peripheral distribution in 30 (100%). Pleural effusion or mediastinal lymphadenopathy was relatively rare manifestations. Rapidly progression of the disease demonstrated by increasing number and range of ground glass opacities and appearance of consolidations at follow-up CT images in two patients. The CT lung severity score and No. of lobes involved were negatively correlated with lymphocyte count(r=-0.363, P=0.041; r=-0.367, P=0.039, respectively). Chest HRCT of COVID-19 predominantly manifests multiple, round, ground glass opacities with irregular intralobular lines, and peripheral distribution of bilateral lungs. HRCT is a potential tool for early screening, assessing progress, and predicting disease severity of COVID-19.Authors Jie Zhou and Jie Cao contributed equally to this work and are co-first authors.