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Objectives In a dangerous future pandemic without effective vaccines and medicines, a reliable screening-and-isolation strategy can be the last opportunity to keep critical facilities and communities running and avoid a complete shutdown. Methods In this study, we introduced an epidemiological model that included essential parameters of infection transmission and screening. With varying parameters, we studied the dynamics of viral infection in the semi-isolated communities. Results In the scenario with a periodic infection screening once per 3 days and a viral basic reproduction number 3.0, more than 85% of the infection waves have a duration less than 7 days and the infection size in each of the waves is generally less than 4 individuals when the efficiency of infection discovery is 0.9 in the screening. When the period of screening was elongated to once per 7 days, the cases of infection dramatically increased to 5 folds of that mentioned previously. Further, with a weak discovery efficiency of 0.7 and the aforementioned low screening frequency, the spread of infection would be out of control. Conclusions Our study suggests that frequent periodic screening is capable of controlling a future epidemic in a semi-isolated community without vaccines and medicines.
Subject(s)
Virus DiseasesABSTRACT
The low-carbon development of air transport industry is of great significance for China to achieve the commitment of carbon peak and carbon neutrality goals. In order to improve the basic data of aviation CO2 emissions, this study continuously collected full flight information in China from January 2017 to December 2020, and established a flight information database and an aircraft-engine parameter database. On the basis of IPCC's Tier 3B accounting method, this study established a long-term aviation CO2 emissions inventory of China from 2017 to 2020 by calculating and accumulating CO2 emissions of each flight. And aviation CO2 emissions of various provinces and cities in China were calculated combined with spatial allocation method. The results showed that aviation CO2 emissions in China was 104.1, 120.1, 136.9, and 88.3 Mt in 2017, 2018, 2019, and 2020, respectively, with annual growth rates of 15.4%, 14.0%, and −35.3% in 2018, 2019, and 2020, respectively. Affected by the COVID-19 pandemic, aviation CO2 emissions in all 31 provinces and 93% of cities decreased in 2020 compared with 2019. China is in the stage of rapid development of air transport industry, and aviation fossil energy consumption and CO2 emissions have continued to grow in recent years.
ABSTRACT
BACKGROUND: The long-term functional outcome of discharged patients with coronavirus disease 2019 (COVID-19) remains unresolved. We aimed to describe a 6-month follow-up of functional status of COVID-19 survivors. METHODS: We reviewed the data of COVID-19 patients who had been consecutively admitted to the Tumor Center of Union Hospital (Wuhan, China) between 15 February and 14 March 2020. We quantified a 6-month functional outcome reflecting symptoms and disability in COVID-19 survivors using a post-COVID-19 functional status scale ranging from 0 to 4 (PCFS). We examined the risk factors for the incomplete functional status defined as a PCFS > 0 at a 6-month follow-up after discharge. RESULTS: We included a total of 95 COVID-19 survivors with a median age of 62 (IQR 53-69) who had a complete functional status (PCFS grade 0) at baseline in this retrospective observational study. At 6-month follow-up, 67 (70.5%) patients had a complete functional outcome (grade 0), 9 (9.5%) had a negligible limited function (grade 1), 12 (12.6%) had a mild limited function (grade 2), 7 (7.4%) had moderate limited function (grade 3). Univariable logistic regression analysis showed a significant association between the onset symptoms of muscle or joint pain and an increased risk of incomplete function (unadjusted OR 4.06, 95% CI 1.33-12.37). This association remained after adjustment for age and admission delay (adjusted OR 3.39, 95% CI 1.06-10.81, p = 0.039). CONCLUSIONS: A small proportion of discharged COVID-19 patients may have an incomplete functional outcome at a 6-month follow-up; intervention strategies are required.
Subject(s)
COVID-19 , Patient Discharge , Follow-Up Studies , Functional Status , Humans , SARS-CoV-2ABSTRACT
We shed light on the importance of government disclosure in public emergency management. During the outbreak of COVID-19, provinces in China entered a government disclosure regime, which mandated the disclosure of the number of people infected with the virus on a daily basis. Each province also voluntarily disclosed its own virus situation. We find that various forms of province-level government disclosure generally reduced the number of trips made by the infected and sped up their diagnosis. They also raised attention paid to the virus and self-protection awareness as well as reduced mobility among the susceptible. Finally, government voluntary disclosure helped to reduce the duration of local epidemics. We conclude that government disclosure can be effective in instilling the correct human behaviors that are conducive to fighting the pandemic.
ABSTRACT
The present study aimed to establish a prognostic nomogram to stratify high-risk patients with Coronavirus Disease 2019 (COVID-19) who progressed from the nonsevere condition on admission to severe during hospitalization. This multicenter retrospective study included patients with nonsevere COVID-19 on admission from Jan 10, 2020 to Feb 7, 2020. In the training cohort, independent risk factors associated with disease progression were identified by univariate and multivariate analyses. The prognostic nomogram was established and then validated externally using C-index. The study included 351 patients (293 and 58 in the training and validation cohorts, respectively), with 27 (9.2%) and 5 (8.6%) patients progressed, respectively. In the training cohort, older age (OR 1.036, 95% CI 1.000-1.073), more lobes involved on chest CT (OR 1.841, 95% CI 1.117-3.035), comorbidity present (OR 2.478, 95% CI 1.020-6.018), and lower lymphocyte count (OR 0.081, 95% CI 0.019-0.349) were identified as independent risk factors. The prognostic nomogram was established in the training cohort with satisfied external prognostic performance (C-index 0.906, 95% CI 0.806-1.000). In conclusion, older age, comorbidity present, more lobes involved on chest CT, and lower lymphocyte count are independent risk factors associated with disease progression during hospitalization for patients with nonsevere COVID-19.
ABSTRACT
Background The long-term functional outcome of discharged patients with coronavirus disease 2019 (COVID-19) remains unresolved. We aimed to describe a six-month follow-up of functional status of COVID-19 survivors.Methods We reviewed the data of COVID-19 patients who had been consecutively admitted to the Tumor Center of Union Hospital (Wuhan, China) between 15 February and 14 March 2020. We quantified a six-month functional outcome reflecting symptoms and disability in COVID-19 survivors using a post-COVID-19 functional status scale ranging from 0 to 5 (PCFS). We examined the risk factors for the incomplete functional status defined as a PCFS > 0 at a six-month follow-up after discharge.Results We included a total of 95 COVID-19 survivors with a median age of 62 (IQR 53-69) who had a complete functional status (PCFS grade 0) at baseline in this retrospective observational study. At six-month follow-up, 67 (70.5%) patients had a complete functional outcome (grade 0), 9 (9.5%) had a negligible limited function (grade 1), 12 (12.6%) had a mild limited function (grade 2), 7 (7.4%) had moderate limited function (grade 3). Univariable logistic regression analysis showed a significant association between the onset symptoms of muscle or joint pain and an increased risk of incomplete function (unadjusted OR 4.06, 95%CI 1.33 - 12.37). This association remained after adjustment for age and admission delay (adjusted OR 3.39, 95%CI 1.06 - 10.81, p = 0.039).Conclusions A small proportion of discharged COVID-19 patients may have an incomplete functional outcome at a six-month follow-up; intervention strategies are required.
Subject(s)
COVID-19 , MyalgiaABSTRACT
Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.Methods Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared to explore risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.Results A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age, N/L, CRP and D-dimer. We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. In the training sets, the negative and positive predictive values were 93.80% and 41.32%.Conclusions A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.
Subject(s)
COVID-19 , Pneumonia , Severe Acute Respiratory SyndromeABSTRACT
Background: The world is under serious threat with the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19). However, there is no effective drug for the treatment of COVID-19. Based on analyses of available data, we deduced that the excessive prostaglandins E2 (PGE2) accumulation mediated by cyclooxygenase-2 (COX-2) was the key pathological basis of COVID-19. Methods: The urine PGE2 levels were measured by mass spectrometry. An experimental study about Celebrex to treat COVID-19 was conducted based on routine treatment. A total of 44 confirmed COVID-19 patients were enrolled (Experimental group n=37, Control group n=7). Patients in experimental group were given Celebrex once or twice a day (0.2 g/time) for 7-14 days. The dosage or duration was modified for individuals. Clinical outcomes of Celebrex adjuvant therapy were evaluated by vital signs, laboratory tests, and computed tomography upon the discontinuance of Celebrex. Results: We found that the concentrations of PGE2 in urine samples of COVID-19 patients were significantly higher than that of healthy individuals (mean value is 170 ng/ml vs 18.8 ng/ml, p<0.01) and positively correlated with the progression of COVID-19. Among the experimental group (ordinary n=29, severe n=7, critical n=1), 25 cases were treated with full dose and 11 cases with half dose of Celebrex, and 1 case with Ibuprofen. The remission rate were 100%, 82% and 57% in full dose, half dose and control group respectively. Celebrex significantly reduced the PGE2 levels and promoted recovery of ordinary or severe COVID-19. Conclusion: Our study suggests that Celebrex adjuvant treatment may be helpful for the therapy of COVID-19.
Subject(s)
COVID-19ABSTRACT
ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese Medicine (TCM) has been widely used as an approach worldwide. Chinese Medicines (CMs) had been used to treat and prevent viral infection pneumonia diseases for thousands of years and had accumulated a large number of clinical experiences and effective prescriptions. AIM OF THE STUDY: This research aimed to systematically excavate the classical prescriptions of Chinese Medicine (CM), which have been used to prevent and treat Pestilence (Wenbing, Wenyi, Shiyi or Yibing) for long history in China, to obtain the potential prescriptions and ingredients to alternatively treat COVID-19. MATERIALS AND METHODS: We developed the screening system based on data mining, molecular docking and network pharmacology. Data mining and association network were used to mine the high-frequency herbs and formulas from ancient prescriptions. Virtual screening for the effective components of high frequency CMs and compatibility Chinese Medicine was explored by a molecular docking approach. Furthermore, network pharmacology method was used to preliminarily uncover the molecule mechanism. RESULTS: 574 prescriptions were obtained from 96,606 classical prescriptions with the key words to treat "Warm diseases (Wenbing)", "Pestilence (Wenyi or Yibing)" or "Epidemic diseases (Shiyi)". Meanwhile, 40 kinds of CMs, 36 CMs-pairs, 6 triple-CMs-groups existed with high frequency among the 574 prescriptions. Additionally, the key targets of SARS-COV-2, namely 3CL hydrolase (Mpro) and angiotensin-converting enzyme 2(ACE2), were used to dock the main ingredients from the 40 kinds by the LigandFitDock method. A total of 66 compounds components with higher frequency were docked with the COVID-19 targets, which were distributed in 26 kinds of CMs, among which Gancao (Glycyrrhizae Radix Et Rhizoma), HuangQin (Scutellariae Radix), Dahuang (Rhei Radix Et Rhizome) and Chaihu (Bupleuri Radix) contain more potential compounds. Network pharmacology results showed that Gancao (Glycyrrhizae Radix Et Rhizoma) and HuangQin (Scutellariae Radix) CMs-pairs could also interact with the targets involving in immune and inflammation diseases. CONCLUSIONS: These results we obtained probably provided potential candidate CMs formulas or active ingredients to overcome COVID-19. Prospectively, animal experiment and rigorous clinic studies are needed to confirm the potential preventive and treat effect of these CMs and compounds.
Subject(s)
Betacoronavirus/drug effects , Coronavirus Infections/drug therapy , Drugs, Chinese Herbal/pharmacology , Drugs, Chinese Herbal/therapeutic use , Medicine, Chinese Traditional , Pneumonia, Viral/drug therapy , COVID-19 , Coronavirus Infections/virology , Data Mining , Humans , Models, Molecular , Pandemics , Plant Extracts , Pneumonia, Viral/virology , Protein Conformation , SARS-CoV-2 , Viral ProteinsABSTRACT
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2. Methods: Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared using the Statistical Package for the Social Sciences (IBM Corp., Armonk, NY, USA) to explore clinical characteristics and risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation. Results: A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age [odds ratio (OR): 1.059, 95% confidence interval (CI): 1.036-1.082; p < 0.001], N/L (OR: 1.322, 95% CI: 1.180-1.481; p < 0.001), CRP (OR: 1.231, 95% CI: 1.129-1.341; p = 0.002), and D-dimer (OR: 1.059, 95% CI: 1.013-1.107; p = 0.011). We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. The sensitivity and specificity were 89.47% and 67.42%. In the training sets, the negative and positive predictive values were 93.80% and 41.32%, respectively, with a specificity of 70.76% and a sensitivity of 89.87%. Conclusions: A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.
Subject(s)
COVID-19 , Pneumonia , Severe Acute Respiratory SyndromeABSTRACT
Abstract Background: A new virus broke out in Wuhan, Hubei, China, and was later named 2019 novel coronavirus (2019-nCoV). The clinical characteristics of severe pneumonia caused by 2019-nCoV are still not clear. Objectives: The aim of this study was to explore the clinical characteristics and risk factors of the severe pneumonia caused by the 2019-nCoV in Wuhan, China. Method: The study included patients hospitalized at the central hospital of Wuhan who had been diagnosed with a pneumonia caused by the novel coronavirus. Clinical features, chronic co-morbidities, demographic data, laboratory examinations, and chest computed tomography (CT) scans were reviewed through electronic medical records. SPSS was used for data analysis to explore the clinical characteristics and risk factors of the patients with the severe pneumonia. Results: A total of 110 patients diagnosed with 2019 novel coronavirus pneumonia were included in the study, including 38 with severe pneumonia and 72 with non-severe pneumonia. Statistical analysis showed that advanced age, an increase of D-dimer, and a decrease of lymphocytes were characteristics of the patients with severe pneumonia. Moreover, in the early stage of the disease, chest CT scans of patients with the severe pneumonia showed the illness can progress rapidly. Conclusions: Advanced age, lymphocyte decline, and D-dimer elevation are important characteristics of patients with severe pneumonia. Clinicians should focus on these characteristics to identify high-risk patients at an early stage.