Social support and perceived uncertainties during COVID-19: Consequences for employees' wellbeing.

The COVID-19 crisis has drastically affected organizations worldwide, thereby influencing the employees' psychological wellbeing. Since it is a new pandemic, research is sparse in the domain of employees' psychological wellbeing in relation to the phenomenon. Drawing on social support and job demand-resource perspectives, this research adds to the factors affecting employees' wellbeing due to the coronavirus outbreak. Specifically, this study is an investigation of co-workers' instrumental support in predicting employees' emotional exhaustion via employees' perceived uncertainties experienced due to the COVID-19 pandemic. Further, we tested for the contextual specificity of family support on uncertainties and its link with employees' emotional exhaustion. With data drawn from two universities (n = 275), the findings reveal a negative association between co-worker task support and an employee's emotional exhaustion, and an employee's perceived uncertainties mediate this relationship. Moreover, the moderating analysis exhibits that family support mitigates the negative effect of uncertainty perception on emotional exhaustion. Our study reveals that coworker and family support are extremely important during the COVID-19 pandemic. These findings are equally valuable for organizations and society to mitigate the detrimental effects of the COVID-19 pandemic on employees' wellbeing.

Discovery of Novel Chinese Medicine Compounds Targeting 3CL Protease by Virtual Screening and Molecular Dynamics Simulation.

The transmission and infectivity of COVID-19 have caused a pandemic that has lasted for several years. This is due to the constantly changing variants and subvariants that have evolved rapidly from SARS-CoV-2. To discover drugs with therapeutic potential for COVID-19, we focused on the 3CL protease (3CLpro) of SARS-CoV-2, which has been proven to be an important target for COVID-19 infection. Computational prediction techniques are quick and accurate enough to facilitate the discovery of drugs against the 3CLpro of SARS-CoV-2. In this paper, we used both ligand-based virtual screening and structure-based virtual screening to screen the traditional Chinese medicine small molecules that have the potential to target the 3CLpro of SARS-CoV-2. MD simulations were used to confirm these results for future in vitro testing. MCCS was then used to calculate the normalized free energy of each ligand and the residue energy contribution. As a result, we found ZINC15676170, ZINC09033700, and ZINC12530139 to be the most promising antiviral therapies against the 3CLpro of SARS-CoV-2.

Association of neutralizing breadth against SARS-CoV-2 with inoculation orders of heterologous prime-boost vaccines.

BACKGROUND: Emerging evidence suggests heterologous prime-boost COVID-19 vaccination as a superior strategy than homologous schedules. Animal experiments and clinical observations have shown enhanced antibody response against influenza variants after heterologous vaccination; however, whether the inoculation order of COVID-19 vaccines in a prime-boost schedule affects antibody response against SARS-CoV-2 variants is not clear. METHODS: We conducted immunological analyses in a cohort of health care workers (n = 486) recently vaccinated by three types of inactivated COVID-19 vaccines under homologous or heterologous prime-boost schedules. Antibody response against ancestral SARS-CoV-2 (Wuhan-Hu-1) was assessed by total antibody measurements, surrogate virus neutralization tests, and pseudovirus neutralization assays (PNA). Furthermore, serum neutralization activity against SARS-CoV-2 variants of concern was also measured by PNA. FINDINGS: We observed strongest serum neutralization activity against the widely circulating SARS-CoV-2 variant B.1.617.2 among recipients of heterologous BBIBP-CorV/CoronaVac and WIBP-CorV/CoronaVac. In contrast, recipients of CoronaVac/BBIBP-CorV and CoronaVac/WIBP-CorV showed significantly lower B.1.617.2 neutralization titers than recipients of reverse schedules. Laboratory tests revealed that neutralizing activity against common variants but not the ancestral SARS-CoV-2 was associated with the inoculation order of heterologous prime-boost vaccines. Multivariable regression analyses confirmed this association after adjusting for known confounders. CONCLUSIONS: Our data provide clinical evidence of inoculation order-dependent expansion of neutralizing breadth against SARS-CoV-2 in recipients of heterologous prime-boost vaccination and call for further studies into its underlying mechanism. FUNDING: National Key R&D Program of China, National Development and Re-form Commission of China, National Natural Science Foundation of China, Shenzhen Science and Technology Innovation Commission, and US Department of Veterans Affairs.

The Alleviation of LPS-Induced Murine Acute Lung Injury by GSH-Mediated PEGylated Artesunate Prodrugs

Acute lung injury (ALI) or its aggravated stage acute respiratory distress syndrome (ARDS) is a common severe clinical syndrome in intensive care unit, may lead to a life-threatening form of respiratory failure, resulting in high mortality up to 30–40% in most studies. Nanotechnology-mediated anti-inflammatory therapy is an emerging novel strategy for the treatment of ALI, has been demonstrated with unique advantages in solving the dilemma of ALI drug therapy. Artesunate (ART), a derivative of artemisinin, has been reported to have anti-inflammatory effects. Therefore, in the present study, we designed and synthesized PEGylated ART prodrugs and assessed whether ART prodrugs could attenuate lipopolysaccharide (LPS) induced ALI in vitro and in vivo. All treatment groups were conditioned with ART prodrugs 1 h before challenge with LPS. Significant increased inflammatory cytokines production and decreased GSH levels were observed in the LPS stimulated mouse macrophage cell line RAW264.7. Lung histopathological changes, lung W/D ratio, MPO activity and total neutrophil counts were increased in the LPS-induced murine model of ALI via nasal administration. However, these results can be reversed to some extent by treatment of ART prodrugs. The effectiveness of mPEG2k-SS-ART in inhibition of ALI induced by LPS was confirmed. In conclusion, our results demonstrated that the ART prodrugs could attenuate LPS-induced ALI effectively, and mPEG2k-SS-ART may serve as a novel strategy for treatment of inflammation induced lung injury.

The companion effect on adventure tourists’ satisfaction and subjective well-being: the moderating role of gender

Purpose>In an adventure tourism context (i.e. sky diving, bungee jumping) the effect of the absence or presence of a travel companion;companion relative ability (i.e. perception of a companion’s possessed resources useful for the achievement of travel goals);and tourist gender on the impact of companion relative ability on tourists’ satisfaction and subjective well-being is examined. This paper aims to investigate the mediating role of satisfaction that combines companion relative ability, tourist gender, tourist satisfaction and subjective well-being.Design/methodology/approach>This research uses three situational experiments. A one-factor between-subjects experimental design was used for Study 1. Studies 2 and 3 used a one-factor between-subjects and a 2 × 3 factorial between-subjects design. Participants included tourists visiting a national park in China assigned to scenarios using an anonymous intercept approach and an online survey.Findings>Having a companion with greater/comparable relative ability produces a greater effect on tourist satisfaction and subjective well-being than having a companion with lower relative ability. Furthermore, the perceived relative ability of a travel companion results in a stronger positive effect on tourist satisfaction and subjective well-being for female tourists. Meanwhile, satisfaction fully mediates the impact of the interaction between companion relative ability and tourist gender on subjective well-being.Originality/value>The current research validates the companion effect on adventure tourists’ satisfaction and subjective well-being. An additional contribution is an investigation into the effect of companion relative ability. The study is the only one the authors are aware of that examines the moderating role of tourist gender on the effect of companion relative ability on tourist satisfaction and subjective well-being and identifies the mechanism that combines companion relative ability, tourist gender, tourist satisfaction and subjective well-being.

SARS-CoV-2 immunity and functional recovery of COVID-19 patients 1-year after infection.

The long-term immunity and functional recovery after SARS-CoV-2 infection have implications in preventive measures and patient quality of life. Here we analyzed a prospective cohort of 121 recovered COVID-19 patients from Xiangyang, China at 1-year after diagnosis. Among them, chemiluminescence immunoassay-based screening showed 99% (95% CI, 98-100%) seroprevalence 10-12 months after infection, comparing to 0.8% (95% CI, 0.7-0.9%) in the general population. Total anti-receptor-binding domain (RBD) antibodies remained stable since discharge, while anti-RBD IgG and neutralization levels decreased over time. A predictive model estimates 17% (95% CI, 11-24%) and 87% (95% CI, 80-92%) participants were still 50% protected against detectable and severe re-infection of WT SARS-CoV-2, respectively, while neutralization levels against B.1.1.7 and B.1.351 variants were significantly reduced. All non-severe patients showed normal chest CT and 21% reported COVID-19-related symptoms. In contrast, 53% severe patients had abnormal chest CT, decreased pulmonary function or cardiac involvement and 79% were still symptomatic. Our findings suggest long-lasting immune protection after SARS-CoV-2 infection, while also highlight the risk of immune evasive variants and long-term consequences for COVID-19 survivors.

The trans-omics landscape of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.

Effect of synchronous online vs. face-to-face cardiopulmonary resuscitation training on chest compression quality: A pilot randomized manikin study.

OBJECTIVES: The aim of the study was to compare the effect of synchronous online and face-to-face cardiopulmonary resuscitation (CPR) training on chest compressions quality in a manikin model. METHODS: A total of 118 fourth-year medical students participated in this study. The participants were divided into two groups: the online synchronous teaching group and the face-to-face group. Then, the participants were further randomly distributed to 1 of 2 feedback groups: online synchronous teaching and training with feedback devices (TF, n = 30) or without feedback devices (TN, n = 29) and face-to-face teaching and training with feedback devices (FF, n = 30) or without feedback devices (FN, n = 29). In the FN group and FF group, instructors delivered a 45-min CPR training program and gave feedback and guidance during training on site. In the TN group and TF group, the participants were trained with an online lecture via Tencent Meeting live broadcasting. Finally, participants performed a 2-min continuous chest compression (CC) during a simulated cardiopulmonary arrest scene without the audiovisual feedback (AVF) device. The outcome measures included CC depth, CC rate, proportions of appropriate depth (50-60 mm) and CC rate (100-120/min), percentage of correct hand location position, and percentage of complete chest recoil. RESULTS: There was little difference in the CC quality between the synchronous online training groups and the face-to-face training groups. There was no statistically significant difference in CC quality between the TN group and FN group. There were also no statistically significant differences between the TF and FF groups in terms of correct hand position, CC depth, appropriate CC depth, complete chest recoil or CC rate. However, the FF group had a higher appropriate CC rate than the TF group (p = 0.045). In the face-to-face training groups, the AVF device group had a significantly greater CC depth, appropriate CC depth, CC rate, and appropriate CC rate. However, there was a lack of statistically significant differences in terms of correct hand position (p = 0.191) and appropriate CC depth (p = 0.123). In the synchronous online training groups, the AVF device had little effect on the CC rate (p = 0.851) and increased the appropriate CC rate, but the difference was not statistically significant (p = 0.178). CONCLUSIONS: Synchronous online training with an AVF device would be a potential alternative approach to face-to-face chest compression training. Synchronous online training with AVF devices seems to be a suitable replacement for face-to-face training to offer adequate bystander CPR chest compression training.

Does Participative Leadership Matters in Employees' Outcomes During COVID-19? Role of Leader Behavioral Integrity.

The coronavirus pandemic (COVID-19) has badly affected the social, physical, and emotional health of workers, especially those working in the healthcare sectors. Drawing on social exchange theory, we investigated the effects of participative leadership on employees' workplace thriving and helping behaviors among frontline workers during the COVID-19 pandemic. In addition, we examined the moderating role of a leader's behavioral integrity in strengthening the relationship between participative leadership, and employees' workplace thriving and helping behaviors. By using a two-wave time-lagged design and data collected from 244 healthcare workers, a moderated hierarchal regression was implemented to test the proposed hypotheses. As hypothesized, participative leadership predicted employees' workplace thriving and helping behaviors. The leader's behavioral integrity strengthened the relationship between participative leadership and employees' thriving and moderated the relationship between participative leadership helping behaviors. Implications for research, theory, and practice are discussed.

Using the health belief model to explore nursing students' relationships between COVID-19 knowledge, health beliefs, cues to action, self-efficacy, and behavioral intention: A cross-sectional survey study.

ABSTRACT: Nursing educators should equip nursing students with sufficient knowledge about coronavirus disease 2019 (COVID-19), perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, self-efficacy, and behavioral intention in order to prevent the spread of COVID-19.The purpose of this study was to use the health belief model to elucidate nursing students' relationships between knowledge about COVID-19, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, self-efficacy, and behavioral intention.A cross-sectional survey design was adopted and purposive sampling was utilized. A total of 361 nursing students participated in the study. Quantitative analysis was employed for all data analysis.The findings showed that the nursing students had the following mean scores on knowledge of COVID-19 9.43 [standard deviation (SD)1.19], perceived susceptibility 19.41 (SD2.68), perceived severity 20.31 (SD 4.09), perceived benefits 26.52 (SD 4.08), perceived barriers 15.17 (SD5.88), cues to action 3.30 (SD1.70), self-efficacy 17.68 (SD2.83), and behavioral intention 18.46 (SD2.33). Nursing students' demographic background, knowledge of COVID-19, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy explained 58.1% of the variance in behavioral intention (R2 = 0.581, F = 29.775, P < .001).Nursing educators can increase nursing students' knowledge of COVID-19, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy as effective means of health promotion to improve their behavioral intention to prevent the spread of COVID-19.

Voxel-level forecast system for lesion development in patients with COVID-19 (preprint)

The global spread of COVID-19 seriously endangers human health and even lives. By predicting patients' individualized disease development and further performing intervention in time, we may rationalize scarce medical resources and reduce mortality. Based on 1337 multi- stage ([≥]3) high-resolution chest computed tomography (CT) images of 417 infected patients from three centers in the epidemic area, we proposed a random forest + cellular automata (RF+CA) model to forecast voxel-level lesion development of patients with COVID-19. The model showed a promising prediction performance (Dice similarity coefficient [DSC] = 71.1%, Kappa coefficient = 0.612, Figure of Merit [FoM] = 0.257, positional accuracy [PA] = 3.63) on the multicenter dataset. Using this model, multiple driving factors for the development of lesions were determined, such as distance to various interstitials in the lung, distance to the pleura, etc. The driving processes of these driving factors were further dissected and explained in depth from the perspective of pathophysiology, to explore the mechanism of individualized development of COVID-19 disease. The complete codes of the forecast system are available at https://github.com/keyunj/VVForecast_covid19.

Multi-chain Fudan-CCDC model for COVID-19-a revisit to Singapore's case.

BACKGROUND: COVID-19 has been impacting on the whole world critically and constantly since late December 2019. Rapidly increasing infections has raised intense worldwide attention. How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control. METHODS: We propose the multi-chain Fudan-CCDC model based on the original single-chain model in [Shao et al. 2020] to describe the evolution of COVID-19 in Singapore. Multi-chains can be considered as the superposition of several single chains with different characteristics. We identify the parameters of models by minimizing the penalty function. RESULTS: The numerical simulation results exhibit the multi-chain model performs well on data fitting. Though unsteady the increments are, they could still fall within the range of _30% fluctuation from simulation results. CONCLUSION: The multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak. It can also explain the data from those countries where the single-chain model shows deviation from the data.

GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation (preprint)

Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients. Due to the complex shapes and varied appearances of lesions, a large number of voxel-level labeled samples are generally required to train a lesion segmentation network, which is a main bottleneck for developing deep learning based medical image segmentation algorithms. In this paper, we propose a weakly-supervised lesion segmentation framework by embedding the Generative Adversarial training process into the Segmentation Network, which is called GASNet. GASNet is optimized to segment the lesion areas of a COVID-19 CT by the segmenter, and to replace the abnormal appearance with a generated normal appearance by the generator, so that the restored CT volumes are indistinguishable from healthy CT volumes by the discriminator. GASNet is supervised by chest CT volumes of many healthy and COVID-19 subjects without voxel-level annotations. Experiments on three public databases show that when using as few as one voxel-level labeled sample, the performance of GASNet is comparable to fully-supervised segmentation algorithms trained on dozens of voxel-level labeled samples.

A time delay dynamical model for outbreak of 2019-nCoV and the parameter identification

In this paper, we propose a novel dynamical system with time delay to describe the outbreak of 2019-nCoV in China. One typical feature of this epidemic is that it can spread in the latent period, which can therefore be described by time delay process in the differential equations. The accumulated numbers of classified populations are employed as variables, which is consistent with the official data and facilitates the parameter identification. The numerical methods for the prediction of the outbreak of 2019-nCoV and parameter identification are provided, and the numerical results show that the novel dynamic system can well predict the outbreak trend so far. Based on the numerical simulations, we suggest that the transmission of individuals should be greatly controlled with high isolation rate by the government.

Impact of COVID-19 Pandemic on Intervals and Outcomes of Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma (preprint)

Background: Transarterial chemoembolization (TACE) may not be repeated “on-demand” timely for hepatocellular carcinoma (HCC) patients in the era of the novel coronavirus disease (COVID-19). We aim to evaluate the impact of the COVID-19 pandemic on the intervals and outcomes of TACE in HCC patients. Methods: This retrospective study included HCC patients who underwent TACE from Jan 1, 2020 to March 31, 2020 (study group) and Jan 1, 2019 to Mar 31, 2019 (control group) at two institutions in China. The endpoints included the TACE interval and the overall response rate (ORR). Uni- and multivariate logistic analyses were performed to identify independent risk factors associated with a worse ORR. The cut-off point was determined to divide repeated TACE time into long- and short- intervals. Findings: 154 patients (71 in the study group, 83 in the control group) were enrolled. The median TACE interval in the study group was 82·0 days (IQR, 61–109), longer than 66·0 days (IQR, 51–94) in the control group (p=0·004). The ORR was 23·9% in the study group, while 39·8% in the control group (p=0·037). The cut-off value was 95 days. The group (OR, 2·402; 95% CI, 1·040–5·546; p=0·040), the long interval (OR, 2·573; 95% CI, 1·022–6·478; p=0·045), and the stage system (OR, 2·500; 95% CI, 1·797–3·480; p<0·001) were independent predictors. Interpretation: For HCC patients, the COVID-19 pandemic results in a longer re-TACE schedule, which may further lead to a lower ORR. Patients with a TACE interval of more than 95 days may have a worse prognosis. Funding: This study was supported by the National Key Research and Development Project of China (2018YFA0704100), the National Natural Science Foundation of China (Major Scientific Research Instrument Development Program 81827805, 81441054, 81520108015, 81671796, 81901847), Jiangsu Provincial Medical Youth Talent Program (ZDRCA2016078), the Key Research and Development Project of Jiangsu Province (BE2019750), the Natural Science Foundation of Jiangsu Province (BK20190177), Innovation Platform of Jiangsu Provincial Medical Center (YXZXA2016005), and the Suzhou Science and Technology Youth Plan (KJXW2018003).Declaration of Interests: All authors declare no competing interests.Ethics Approval Statement: The study was approved by the institutional ethics review boards in two participating institutions and the requirement for written informed consent was waived due to its retrospective nature.

Impact of physical activity and lifestyle in pre-infection on the susceptibility and prognosis of infects with COVID-19 (preprint)

Background There are very few studies focusing on the relationship between COVID-19 and pre-infection lifestyle. In the absence of effective vaccines and special-effect medicines, it is very meaningful to actively respond to the disease pandemic by improving lifestyle habits.Methods This is a multicenter, retrospective cohort study enrolled 431 adult people including 228 normal people and 203 confirmed infects in Wubei, Henan and Shandong Provinces. Questionnaires were used to collect information on physical activity and lifestyle by competent doctors. The univariate logistic regression models and multiple regression models were used in risk factor analysis. Kruskal-Wallis H test were used to test the correlation.Results Lifestyle habits including exercise, smoking, sedentary behavior and physical activity intensity can significantly affect the probability of getting COVID-19 (P < 0.05). The MET (Metabolic Equivalent) intensity classification and sleep status are found to be the potential influencing factors of prognosis in both all infects and symptomatic patients. In all infects, taking the high MET intensity level as a reference, inpatient days would increase by 1.812 times (95% CI: 0.887–3.701) with no significance when the level is moderate (P > 0.05) and significantly increase by 6.674 times (95% CI: 1.613–27.613) when the level is low (P < 0.05). Kruskal-Wallis H test results showed moderate activity MET*min promoted shorter hospital stay (P < 0.05) mainly.Conclusions Sleep status and physical activity influenced the susceptibility and prognosis of COVID-19. Lack of sleep and low MET intensity level may prolong the hospital stay, which means a relatively slow recovery. This encourages the public to have moderate physical activity and adequate sleep to respond to the COVID-19 pandemic actively.

Impact of Physical Activity and Lifestyle in Pre-infection on the Susceptibility and Prognosis of Infects With COVID-19 (preprint)

Background There are very few studies focusing on the relationship between COVID-19 and pre-infection lifestyle. In the absence of effective vaccines and special-effect medicines, it is very meaningful to actively respond to the disease pandemic by improving lifestyle habits.Methods This is a multicenter, retrospective cohort study enrolled 431 adult people including 228 normal people and 203 confirmed infects in Wubei, Henan and Shandong Provinces. Questionnaires were used to collect information on physical activity and lifestyle by competent doctors. The univariate logistic regression models and multiple regression models were used in risk factor analysis. Kruskal-Wallis H test were used to test the correlation.Results Lifestyle habits including exercise, smoking, sleep and physical activity can significantly affect the probability of getting COVID-19(P < 0.05). The MET (Metabolic Equivalent) intensity classification and sleep status are found to be the potential influencing factors of prognosis in both all infects and symptomatic patients. In all infects, taking the high MET intensity level as a reference, inpatient days would increase by 1.812 times (95% CI: 0.887–3.701) with no significance when the level is moderate (P > 0.05) and significantly increase by 6.674 times (95% CI: 1.613–27.613) when the level is low (P < 0.05). Kruskal-Wallis H test results showed moderate activity MET*min promoted shorter hospital stay (P < 0.05) mainly.Conclusions Sleep status and physical activity influenced the susceptibility and prognosis of COVID-19. Lack of sleep and low MET intensity level may prolong the hospital stay, which means a relatively slow recovery. This encourages the public to have moderate physical activity and adequate sleep to respond to the COVID-19 pandemic actively.

[Thoughts and suggestions on arrangement, analysis and summary of medical data during COVID-19 epidemic].

The analysis and utilization of clinical scientific research data is an effective means to promote the progress of diagnosis and treatment, and a key step in the development of medical sciences. During the epidemic of coronavirus disease 2019(COVID-19), how to transform the limited diagnostic data into clinical research resources has attracted much attention. Based on the low efficiency of data collection and extraction, the inconsistency of data analysis, the irregularity of data report and the high timeliness of data update during the epidemic, this paper briefly analyzed the background and reasons of data application under the current situation, and then discusses the problems and feasible solutions of clinical data applications under the epidemic situation and, more importantly, for future medical clinical research methods. We put forward several methodological suggestions: ① gradually improve the medical big data model and establish the national medical health data center; ② improve the scientific research literacy of medical staff and popularize the basic skills and knowledge of GCP; ③ promote a scientific, networked and shared data collection and management mode; ④ use the mixed research method and collective analysis to improve the efficiency of clinical data analysis; ⑤ pay attention to narration of the medical feelings and emphasize the humanistic data of clinical medicine. It is expected to promote the standardized and reasonable use of clinical scientific research data, the rigorous integration of expert opinions, and ultimately the development of big data for national health care.

Dynamic models for Coronavirus Disease 2019 and data analysis.

In this letter, two time delay dynamic models, a Time Delay Dynamical-Novel Coronavirus Pneumonia (TDD-NCP) model and Fudan-Chinese Center for Disease Control and Prevention (CCDC) model, are introduced to track the data of Coronavirus Disease 2019 (COVID-19). The TDD-NCP model was developed recently by Chengars group in Fudan and Shanghai University of Finance and Economics (SUFE). The TDD-NCP model introduced the time delay process into the differential equations to describe the latent period of the epidemic. The Fudan-CDCC model was established when Wenbin Chen suggested to determine the kernel functions in the TDD-NCP model by the public data from CDCC. By the public data of the cumulative confirmed cases in different regions in China and different countries, these models can clearly illustrate that the containment of the epidemic highly depends on early and effective isolations.

Development and Evaluation of an AI System for COVID-19 (preprint)

Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. We proposed an artificial intelligence (AI) system for fast COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.17%, a sensitivity of 90.19%, and a specificity of 95.76% for COVID-19 on internal test cohort of 3,203 scans and AUC of 97.77% on the publicly available CC-CCII database with 1,943 test samples. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared. Detailed interpretation of deep network is also performed to relate AI results with CT findings. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.