ABSTRACT
Novel coronavirus pneumonia (COVID-19) epidemic outbreak at the end of 2019 and threaten global public health, social stability, and economic development, which is characterized by highly contagious and asymptomatic infections. At present, governments around the world are taking decisive action to limit the human and economic impact of COVID-19, but very few interventions have been made to target the transmission of asymptomatic infected individuals. Thus, it is a quite crucial and complex problem to make accurate forecasts of epidemic trends, which many types of research dedicated to deal with it. In this article, we set up a novel COVID-19 transmission model by introducing traditional SEIR (susceptible-exposed-infected-removed) disease transmission models into complex network and propose an effective prediction algorithm based on the traditional machine learning algorithm TrustRank, which can predict asymptomatic infected individuals in a population contact network. Our simulation results show that our method largely outperforms the graph neural network algorithm for new coronary pneumonia prediction and our method is also robust and gives good results even if the network information is incomplete.
ABSTRACT
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
ABSTRACT
Background: During an epidemic, both frontline and non-frontline medical staff endure stressful work circumstances that render their mental health a major public health concern. This study aims at investigating and comparing the prevalence and severity of mental health symptoms (i.e., anxiety, depression and insomnia) between frontline medical staff and non-frontline medical staff during the coronavirus disease 2019 (COVID-19) outbreak. It also seeks to evaluate the association of their mental health with occupational stress. Methods: A cross-sectional study was conducted in Wenzhou, China from 2020 February 16th to 2020 March 2th. A total of 524 medical staff responded to the Generalized Anxiety Disorder Scale, the Patient Health Questionnaire, the Insomnia Severity Index, the Occupational stress Questionnaire, and a demographic data form. Data were principally analyzed with logistic regression. Results: Of the 524 participants, 31.3% reported depression, 41.2% reported anxiety, and 39.3% reported insomnia. Compared with the citizens during the COVID-19 epidemic, medical staff experienced higher level of anxiety, depression and insomnia, especially the frontline medical staff. Furthermore, male, married medical staff with poorer physical health reported lower mental health. Frontline medical staff endorsed higher self-reported occupational stress, especially higher occupational hazards, than non-frontline medical staff. In addition, four indicators on occupational stress (working intensity, working time, working difficulty and working risk) were correlated positively with mental health symptoms. Regression analyses found a significant association between occupational stress and mental health symptoms in both frontline and non-frontline medical staff during COVID-19 outbreak. Conclusion: The results indicated that during the COVID-19 epidemic, medical staff experienced higher levels of anxiety, depression and insomnia than citizens, and their occupational stress had positive effects on their psychological distress. These findings emphasize the importance of occupational stress management interventions to decrease the risk of developing mental health problems among the medical staff during a biological disaster.
ABSTRACT
BACKGROUND: It has been reported that a fraction of recovered coronavirus disease 2019(COVID-19) patients have retested positive for SARS-CoV-2. Clinical characteristics and risk factors for retesting positive have not been studied extensively. METHODS: In this retrospective, single-center cohort study, we included adult patients (≥ 18 years old) diagnosed as COVID-19 in Affiliated Yueqing Hospital, Wenzhou Medical University, Zhejiang, China. All the patients were discharged before March 31, 2020, and were re-tested for SARS-CoV-2 RNA by real-time reverse-transcriptase polymerase-chain-reaction (RT-PCR) after meeting the discharge criteria. We retrospectively analyzed this cohort of 117 discharged patients and analyzed the differences between retest positive and negative patients in terms of demographics, clinical characteristics, laboratory findings, chest computed tomography (CT) features and treatment procedures. FINDINGS: Compared with the negative group, the positive group had a higher proportion of patients with comorbidities (Odds Ratio(OR) =2·12, 95% Confidence Interval(CI) 0·48-9·46; p = 0·029), longer hospital stay (OR=1·21, 95% CI 1·07-1·36; p = 0·008), a higher proportion of patients with lymphocytopenia (p = 0·036), a higher proportion of antibiotics treatment (p = 0·008) and glucocorticoids treatment (p = 0·003). Multivariable regression showed increasing odds of positive SARS-CoV-2 retest after discharge associated with longer hospital stay (OR=1·22, 95% CI 1·08-1·38; p = 0·001), and lymphocytopenia (OR=7·74, 95% CI 1·70-35·21; p = 0·008) on admission. INTERPRETATION: Patients with COVID-19 who met discharge criteria could still test positive for SARS-CoV-2 RNA. Longer hospital stay and lymphopenia could be potential risk factors for positive SARS-CoV-2 retest in COVID-19 patients after hospital discharge. FUNDING: Natural Science Foundation of Zhejiang Province, Medical Scientific Research Fund of Zhejiang Province, Wenzhou science and technology project.
ABSTRACT
BACKGROUND: No data is available about in-flight transmission of SARS-CoV-2. Here, we report an in-flight transmission cluster of COVID-19 and describe the clinical characteristics of these patients. METHODS: After a flight, laboratory-confirmed COVID-19 was reported in 12 patients. Ten patients were admitted to the designated hospital. Data was collected from 25th January to 28th February 2020. Clinical information was retrospectively collected. RESULTS: All patients were passengers, and none were flight attendants. The median age was 33 years, and 70% were females. None was admitted to intensive care unit, and no patients died up to 28th February. The median incubation period was 3.0 days and time from onset of illness to hospital admission was 2 days. The most common symptom was fever. Two patients were asymptomatic and had normal chest CT scan during hospital stay. On admission, initial RT-PCR was positive in 9 patients, and initial chest CT was positive in half of the patients. The median lung 'total severity score' of chest CT was 6. 'Crazy-paving' pattern, pleural effusion, and ground-glass nodules were seen. CONCLUSION: There is potential for COVID-19 transmission in aeroplanes, but the symptoms were mild in our patients. Passengers and attendants must be protected during flights.