Lessons learned 1 year after SARS-CoV-2 emergence leading to COVID-19 pandemic.

Without modern medical management and vaccines, the severity of the Coronavirus Disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) might approach the magnitude of 1894-plague (12 million deaths) and 1918-A(H1N1) influenza (50 million deaths) pandemics. The COVID-19 pandemic was heralded by the 2003 SARS epidemic which led to the discovery of human and civet SARS-CoV-1, bat SARS-related-CoVs, Middle East respiratory syndrome (MERS)-related bat CoV HKU4 and HKU5, and other novel animal coronaviruses. The suspected animal-to-human jumping of 4 betacoronaviruses including the human coronaviruses OC43(1890), SARS-CoV-1(2003), MERS-CoV(2012), and SARS-CoV-2(2019) indicates their significant pandemic potential. The presence of a large reservoir of coronaviruses in bats and other wild mammals, culture of mixing and selling them in urban markets with suboptimal hygiene, habit of eating exotic mammals in highly populated areas, and the rapid and frequent air travels from these areas are perfect ingredients for brewing rapidly exploding epidemics. The possibility of emergence of a hypothetical SARS-CoV-3 or other novel viruses from animals or laboratories, and therefore needs for global preparedness should not be ignored. We reviewed representative publications on the epidemiology, virology, clinical manifestations, pathology, laboratory diagnostics, treatment, vaccination, and infection control of COVID-19 as of 20 January 2021, which is 1 year after person-to-person transmission of SARS-CoV-2 was announced. The difficulties of mass testing, labour-intensive contact tracing, importance of compliance to universal masking, low efficacy of antiviral treatment for severe disease, possibilities of vaccine or antiviral-resistant virus variants and SARS-CoV-2 becoming another common cold coronavirus are discussed.

Machine Learning-Based Decision Model to Distinguish between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study (preprint)

Background: Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens. Methods: Laboratory confirmed COVID-19 and influenza patients from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) between December 1, 2019 and February 29, 2020, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms. The specificity, sensitivity, positive and negative predictive values (PPV/NPV), accuracy and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the model performance. Findings: The data from 357 COVID-19 and 1893 influenza patients from ZHWU were divided into a training and a testing set in the ratio 7:3. The external test used the data of 308 COVID-19 and 312 influenza patients from WNH. In the training and testing sets, the model achieved good performance in identifying COVID-19 from influenza with an accuracy of 0.968 (AUC, 0.943 (95%CI 0.925, 0.962)) and 0.960 (AUC, 0.928 (95%CI 0.897, 0.959)), respectively. Our decision tree suggested that older age (>16 years), higher hsCRP (>14.2 mg/L) and lower monocyte (≤0.68×109/L) drive the prediction towards COVID-19. In addition, the external test determined a COVID-19 prediction accuracy of 0.839 (AUC, 0.839 (95%CI: 0.811, 0.868). Interpretation: Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions having massive COVID-19 and influenza cases while limited resources for laboratory test of specific pathogens. Funding: National Natural Science Foundation of China (81900097) and the Emergency Response Project of Hubei Science and Technology Department (2020FCA002, 2020FCA023).Declaration of Interests: None reported.Ethics Approval Statement: This study was approved by the Medical Ethics Committee, Zhongnan Hospital of Wuhan University (Clinical Ethical Approval No. 2020020).

Mental Disorders of Healthcare Workers Fighting COVID-19: Little Relieved After One Month (preprint)

Background: More than 210,000 medical workers have fought against the Coronavirus Disease 2019 (COVID-19) in Hubei of China since December 2019. However, it was unknown if the mental health disorders for frontline medical staff was relieved one month later. Methods: Medical workers in Wuhan and other cities in Hubei Province was requested to fill out an online survey, which assessed their degrees of anxiety, insomnia, depression, and post-traumatic stress disorder (PTSD). Outcomes: A total of 1,091 respondents (32·63% male, 67·37% female) were valid for statistical analysis. The prevalence was anxiety (52·98% with male 50·84% and female 54·01%), insomnia (78·83% with male 78·09% and female 79·18%), depression (56·10% with male 55·34% and female 56·46%) and PTSD (11·09% with male 10·11% and female 11·56%). For educational attainment, those with doctoral and masters’ degrees (D/M) may suffer from more anxiety (median 7·0 [IQR 2·0-8·5] vs. median 5·0 [IQR 5·0-8·0], P =0·02) and PTSD (median 26·0 [IQR 19·5-33·0] vs. median 23·0 [IQR 19·0-31·0], P =0·04) than those with lower educational degrees. Interpretation: Mental disorders of healthcare workers were little relieved one month after they had ended fighting COVID-19, and potential mitigating factors and interventions is necessary.Funding Statement: The study was financially supported by the National Natural Science Foundation of China (8174356); the Open Project of Hubei Key Laboratory of Wudang Local Chinese Medicine Research (Hubei University of Medicine) (WDCM2018002); the Key Discipline Project of Hubei University of Medicine and the Foundation for Innovative Research Team of Hubei University of Medicine (2018YHKT01).Declaration of Interests: The authors declared no interest conflict in this study.Ethics Approval Statement: This study was approved by the Ethic Committee of Renmin Hospital of Hubei University of Medicine.

Clinical features of 162 fatal cases of COVID-19: a multi-center, retrospective study (preprint)

Understanding the epidemiological and clinical characteristics of fatal cases infected with SARS-CoV-2 is import to develop appropriate preventable intervention programs in hospitals. Demographic data, clinical symptoms, clinical course, co-morbidities, laboratory findings, CT scans, treatments and complications of 162 fatal cases were retrieved from electric medical records in 5 hospitals of Wuhan, China. The median age was 69.5 years old (IQR: 63.0-77.25; range: 29-96). 112 (69.1%) cases were men. Hypertension (45.1%) was the most common co-morbidity, but 59 (36.4%) cases had no co-morbidity. At admission, 131 (81.9%) cases were assessed as severe or critical. However, 39 (18.1%) were assessed as moderate. Moderate cases had a higher prevalence of hypertension and chronic lung disease comparing with severe or critical cases (P<0.05, respectively). 126 (77.8%) and 132 (81.5%) cases received antiviral treatment and glucocorticoids, respectively. 116 (71.6%) cases were admitted to ICU and 137 (85.1%) cases received mechanical ventilation. Respiratory failure or acute respiratory distress syndrome (93.2%) was the most common complication. The young cases of COVID-19, without co-morbidity and in a moderate condition at admission could develop fatal outcome. We need to be more cautious in case management of COVID-19 for preventing the fatal outcomes.

The urgency of severity and mortality risk assessments for COVID-19: A summary from death cases in Wuhan, China (preprint)

Background To investigate the clinical characteristics of 21 death cases and evaluate potential factors of disease severity and mortality risk in COVID-19. Methods Retrospective analysis was used to study the clinical data of 21 death cases with COVID-19. The assessment of disease severity and mortality risk were conducted by APACHE II, SOFA, MuLBSTA and PSI scores. Results The age was 66±14 years-old and 15 (71.4%) were men. 16 (76.2%) patients had chronic medical illnesses. 12 (57.1%) patients were overweight. Decreased lymphocytes were observed in 17 (81.0%) patients on admission. Elevated D-dimer levels were noticed in 11 (52.4%) patients and increased much more when pneumonia deteriorated. The initial APACHE II and SOFA scores demonstrated 18 (85.7%) and 13 (61.9%) patients in middle-risk levels, respectively. MuLBSTA and PSI scores after admission showed high-risk mortality in 13 (61.9%) patients. Most patients developed sequent organ failure and finally caused death. Conclusion Older, male, overweight patients, combined with chronic medical histories, continuous decreased lymphocyte proportion and increased D-dimer might have a higher risk of death. The combination of general scoring (SOFA) and pneumonia specific scoring (MuLBSTA and PSI) after admission might be more sensitive to assess the mortality risk for critical patients in COVID-19.