Investigation on the Control of COVID-19 in Wuhan: Number of Infections Outside Hospitals and the Reproduction Number.

COVID-19 is erupting globally, and Wuhan successfully controlled it within a month. Infections arose from infectious persons outside hospitals. After data revision, data-based and model-based analyses were implemented, and the conclusions are as follows. The incubation period of most infected people may be 6-7 days. The number of infectious persons outside hospitals in Wuhan on January 20, 2020 was about 10000 and reached more than 20000 on the day of Lockdown; it exceeded 72000 on February 4. Both data-based and model-based analyses gave out the evolution of the reproduction number, which was over 2.5 in early January, went down to 1.62 in late January and 1.20 in early February, with a sudden drop to less than 0.5 due to the strict Stay-at-home management after February 11. Strategies of Stay-at-home, Safe-protective measures, and Ark hospitals were the main contributions to control COVID-19 in Wuhan. In Wuhan, 2 inflection points of COVID-19, exactly correspond to February 5 and February 15, the 2 days when Ark hospitals were introduced, and the complete implementation of Stay-at-home. Based on the expression of the reproduction number, group immunity is also discussed. It shows that only when the group immunization rate is over 75% can COVID-19 be under control; group immunity would be full infection and the total deaths will be 220000 for a city as big as Wuhan. Sensitivity analysis suggests that 30% of people staying at home in combination with better behavior changes, such as social-distancing and frequent handwashing, can effectively contain COVID-19. However, only when this proportion is over 60% can the controlled effect and efficiency like Wuhan be obtained.

Determinants and mechanisms driving energy-saving behaviours of long-stay hotel guests: Comparison of leisure, business and extended-stay residential cases

The global hospitality industry is fast-turning sustainable and environmentally friendly. Behaviour-driven energy conservation is an emerging green hotel operation strategy to support this change. The long-stay accommodation services have gained momentum in the hospitality sector since the COVID-19 pandemic. However, the characteristics of long-stay hotel guests are often overlooked in sustainable interventions. Based on an empirical survey in China, this study aims to explore the factors driving energy-saving behaviours of long-stay hotel guests and to compare their effects on guests for different visiting purposes (leisure, business, and extended-stay resident). The analysis indicates that attitude, personal norm and place attachment present a direct contribution to energy-saving behaviour. Besides, the results support that attitude and personal norm connect environmental values and energy-saving behaviour. Both altruistic and biospheric values have positive effects, while egoistic values seem to play a negative role. Biospheric values have stronger impact on attitude and personal norm of business guests. Place attachment has a stronger influence on extended-stay residents while its contribution to energy-saving behaviours of business guests is smaller than other guests. Besides, leisure guests are more sensitive to moral obligations. This research sheds novel lights on the psychological perspectives of the observed heterogeneity of energy-saving behaviours of hotel guests with different visiting purposes. The findings provide hotel operators with a novel theoretical reference for targeted energy-saving interventions to promote energy-saving actions of long-term hotel guests. The study, therefore, can contribute to sustainable tourism policymaking and behaviour-driven hotel energy management.

Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: From the perspective of upstream and downstream wastewater-based epidemiology (WBE) (preprint)

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants . Wastewater-based epidemiology (WBE) is considered as a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman' r = 0.23 - 0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTPs influent (> 90 %). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hot spot model combined with geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from sewer system. In addition, socio-economic factors namely population density, land-use, and tax-income were successfully identified as the potentials drivers which substantially affect the onset of COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also other epidemic issues in the future.

Antiviral Properties of Pennisetum purpureum Extract against Coronaviruses and Enteroviruses.

Many severe epidemics are caused by enteroviruses (EVs) and coronaviruses (CoVs), including feline coronavirus (FCoV) in cats, epidemic diarrhea disease virus (PEDV) in pigs, infectious bronchitis virus (IBV) in chickens, and EV71 in human. Vaccines and antiviral drugs are used to prevent and treat the infection of EVs and CoVs, but the effectiveness is affected due to rapidly changing RNA viruses. Many plant extracts have been proven to have antiviral properties despite the continuous mutations of viruses. Napier grass (Pennisetum purpureum) has high phenolic content and has been used as healthy food materials, livestock feed, biofuels, and more. This study tested the antiviral properties of P. purpureum extract against FCoV, PEDV, IBV, and EV71 by in vitro cytotoxicity assay, TCID50 virus infection assay, and chicken embryo infection assay. The findings showed that P. purpureum extract has the potential of being disinfectant to limit the spread of CoVs and EVs because the extract can inhibit the infection of EV71, FCoV, and PEDV in cells, and significantly reduce the severity of symptoms caused by IBV in chicken embryos.

Analyzing an Epidemic of Human Infections with Two Strains of Zoonotic Virus

Due to the existence and variation of various viruses, an epidemic in which different strains spread at the same time will occur. here, an avian–human epidemic model with two strain viruses are established and analyzed. Both theoretical and simulation results reveal that the mixed infections intensify the epidemic and the dynamics become more complex and sensitive. There are six equilibria. The trivial equilibrium point is a high-order singular point and will undergo the transcritical bifurcations to bifurcate three equilibria. The existence and stability of equilibria mainly depend on five thresholds. A bifurcation portrait for the existence and stability of equilibria is presented. Simulations suggest that the key control measure is to develop the identification technology to eliminate the poultry infected with a high pathogenic virus preferentially, then the infected poultry with a low pathogenic virus in the recruitment and on farms. Controlling contact between human and poultry can effectively restrain the epidemic and controlling contagions in poultry can avoid great infection in humans.

Systematic Screening for SARS-CoV-2 to Detect Asymptomatic Infections: An Epitome of Taiwan's Outbreak

Background Increased studies have revealed that asymptomatic carriers substantially impact the epidemic and that asymptomatic transmission is very common. Therefore, the asymptomatic transmission threat to the spread of the pandemic should not be neglected. Methods The local outbreak in Taiwan, especially in Taipei City, is unprecedented and paramount and has claimed hundreds of lives, tens of thousands of cases, and enormous economic costs. As care providers and gatekeepers of infectious diseases, Taipei City Hospital has to perform regular polymerase chain reaction (PCR) results of admitted patients and healthcare workers (HCWs) to achieve these goals. Results In this study, the results revealed a low positive rate of less than 1%, but the asymptomatic proportions could range from 42% to 46%, which bolsters that systematic screening was effective in controlling coronavirus disease-19 (COVID-19) of Novel Coronavirus or Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) and might be an exemplar to other similar scenarios. Universal screening of admitted patients may be important and necessary, especially in asymptomatic patients. Conclusions Regular screening for healthcare providers is also important during this pandemic, and it is recommended that admitted patients and healthcare providers undergo systemic PCR testing.

Human urinary kallidinogenase in acute ischemic stroke: A single-arm, multicenter, phase IV study (RESK study).

AIMS: Human urinary kallidinogenase (HUK) has shown favorable efficacies in acute ischemic stroke (AIS) treatment. We sought confirmation of the safety and efficacy of HUK for AIS in a large population. METHODS: RESK study enrolled patients with AIS of anterior circulation to receive HUK infusion. The primary endpoint was the incidence of treatment-emergent adverse events (AEs). Secondary endpoints assessed neurological and functional improvements and stroke recurrent rate. RESULTS: Of 1206 eligible patients, 1202 patients received at least one dose of HUK infusion and 983 (81.5%) completed the study. The incidence of treatment-emergent AEs and serious AEs were 55.99% and 2.41%, respectively. Pre-specified AEs of special interest occurred in 21.71% of patients, but the majority were mild and unrelated to therapy. Hypertension, age, treatment time, and drug combination were identified to be associated with drug-related blood pressure reduction. Neurological and functional evaluations revealed favorable outcomes from baseline to post-treatment assessment. The cumulative recurrence rate of stroke was 2.50% during the 90-day assessment. CONCLUSION: HUK had an acceptable safety and tolerability profile in AIS patients. Besides, HUK demonstrated the neurological and functional improvements in AIS, further confirming its clinical efficacy in a real-world large population.

Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Autopsy and statistical evidence of disturbed hemostasis progress in COVID-19: medical records from 407 patients.

BACKGROUND: The progression of coagulation in COVID-19 patients with confirmed discharge status and the combination of autopsy with complete hemostasis parameters have not been well studied. OBJECTIVE: To clarify the thrombotic phenomena and hemostasis state in COVID-19 patients based on epidemiological statistics combining autopsy and statistical analysis. METHODS: Using autopsy results from 9 patients with COVID-19 pneumonia and the medical records of 407 patients, including 39 deceased patients whose discharge status was certain, time-sequential changes in 11 relevant indices within mild, severe and critical infection throughout hospitalization according to the Chinese National Health Commission (NHC) guidelines were evaluated. Statistical tools were applied to calculate the importance of 11 indices and the correlation between those indices and the severity of COVID-19. RESULTS: At the beginning of hospitalization, platelet (PLT) counts were significantly reduced in critically ill patients compared with severely or mildly ill patients. Blood glucose (GLU), prothrombin time (PT), activated partial thromboplastin time (APTT), and D-dimer levels in critical patients were increased compared with mild and severe patients during the entire admission period. The International Society on Thrombosis and Haemostasis (ISTH) disseminated intravascular coagulation (DIC) score was also high in critical patients. In the relatively late stage of nonsurvivors, the temporal changes in PLT count, PT, and D-dimer levels were significantly different from those in survivors. A random forest model indicated that the most important feature was PT followed by D-dimer, indicating their positive associations with disease severity. Autopsy of deceased patients fulfilling diagnostic criteria for DIC revealed microthromboses in multiple organs. CONCLUSIONS: Combining autopsy data, time-sequential changes and statistical methods to explore hemostasis-relevant indices among the different severities of the disease helps guide therapy and detect prognosis in COVID-19 infection.

Perioperative Mortality and Complications in Patients with COVID-19 Undergoing Surgery: A Meta-Regression and Meta-Analysis of 8 Cohort Studies (preprint)

Objectives: The novel coronavirus pneumonia (COVID-19)，spread rapidly world wide, was first reported in December 2019. Meanwhile, there are still a large number of patients who need to undergo various surgical treatments. However, the consensus on whether patients with COVID-19 receive emergency or elective surgery will influence their perioperative mortality and complications still cannot be reached. Therefore, we used meta-analysis to explore the impact of patients with COVID-19 perioperative mortality and complications, aiming to provide evidence for clinical decision-making.Methods: We searched PubMed, Embase, Web of Science, Wan Fang database, date from December 2019 to July 2020 for collecting clinical trail on the impact of patients with COVID-19 perioperative mortality and complications. According to the Cochrane system evaluation method, the data is meta-analyzed with RevMan5.3 software.Results: Eight studies involving 2037 patients, 261 (12.81%) patients with COVID-19 and 1776(87.19%) without COVID-19, were included. The results of meta-analysis showed: the COVID-19 group vs Non-COVID-19 group , perioperative mortality and postoperative pneumonia syndrome increased in COVID-19 group（OR:3.84，95%CI：2.10-7.02，I2 =46%， P <0.0001）, (OR: 33.42，95%CI：15.49-72.07，I 2 =0%， P <0.00001), The number of postoperative fever were significantly higher in COVID-19 , There were no significant difference in postoperative complications and ICU admission between the two groups.Conclusions: In our study, The risk of perioperative death and postoperative pulmonary is significantly increased in patients with COVID-19. These data suggested that consideration should be taken for postponing non-critical procedures and promoting nonoperative treatment to delay or avoid the need for surgery during the pandemic of COVID-19.Funding Statement: Natural Science Foundation of China, Grant number: 31760327/ 81760191Declaration of Interests: The authors declare no competing interests.

A prediction model based on machine learning for diagnosing the early COVID-19 patients (preprint)

With the dramatically fast spread of COVID-9, real-time reverse transcription polymerase chain reaction (RT-PCR) test has become the gold standard method for confirmation of COVID-19 infection. However, RT-PCR tests are complicated in operation andIt usually takes 5-6 hours or even longer to get the result. Additionally, due to the low virus loads in early COVID-19 patients, RT-PCR tests display false negative results in a number of cases. Analyzing complex medical datasets based on machine learning provides health care workers excellent opportunities for developing a simple and efficient COVID-19 diagnostic system. This paper aims at extracting risk factors from clinical data of early COVID-19 infected patients and utilizing four types of traditional machine learning approaches including logistic regression(LR), support vector machine(SVM), decision tree(DT), random forest(RF) and a deep learning-based method for diagnosis of early COVID-19. The results show that the LR predictive model presents a higher specificity rate of 0.95, an area under the receiver operating curve (AUC) of 0.971 and an improved sensitivity rate of 0.82, which makes it optimal for the screening of early COVID-19 infection. We also perform the verification for generality of the best model (LR predictive model) among Zhejiang population, and analyze the contribution of the factors to the predictive models. Our manuscript describes and highlights the ability of machine learning methods for improving the accuracy and timeliness of early COVID-19 infection diagnosis. The higher AUC of our LR-base predictive model makes it a more conducive method for assisting COVID-19 diagnosis. The optimal model has been encapsulated as a mobile application (APP) and implemented in some hospitals in Zhejiang Province.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: A Multicenter Study (preprint)

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from Jan 17 to Feb 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920 (95% confidence interval : 0.902-0.938; AUROC=0.915, and its standard deviation of 0.028, as evaluated in 5-fold cross-validation). At a value of whether the predicted score >4.0, the model could detect NCP with a specificity of 98.3%; at a cut-off value of < -0.5, the model could rule out NCP with a sensitivity of 97.9%. The study demonstrated that the rapid screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Estimating the case fatality ratio of the COVID-19 epidemic in China (preprint)

Background: Corona Virus Disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan city and rapidly spread throughout China since late December 2019. Crude case fatality ratio (CFR) with dividing the number of known deaths by the number of confirmed cases does not represent the true CFR and might be off by orders of magnitude. We aim to provide a precise estimate of the CFR of COVID-19 using statistical models at the early stage of the epidemic. Methods: We extracted data from the daily released epidemic report published by the National Health Commission P. R. China from 20 Jan 2020, to 1 March 2020. Competing risk model was used to obtain the cumulative hazards for death, cure, and cure-death hazard ratio. Then the CFR was estimated based on the slope of the last piece in joinpoint regression model, which reflected the most recent trend of the epidemic. Results: As of 1 March 2020, totally 80,369 cases were diagnosed as COVID-19 in China. The CFR of COVID-19 were estimated to be 70.9% (95% CI: 66.8%-75.6%) during Jan 20-Feb 2, 20.2% (18.6%-22.1%) during Feb 3-14, 6.9% (6.4%-7.4%) during Feb 15-23, 1.5% (1.4%-1.6%) during Feb 24-March 1 in Hubei province, and 20.3% (17.0%-25.3%) during Jan 20-28, 1.9% (1.8%-2.1%) during Jan 29-Feb 12, 0.9% (0.8%-1.1%) during Feb 13-18, 0.4% (0.4%-0.5%) during Feb 19-March 1 in other areas of China, respectively. Conclusions: Based on analyses of public data, we found that the CFR in Hubei was much higher than that of other regions in China, over 3 times in all estimation. The CFR would follow a downwards trend based on our estimation from recently released data. Nevertheless, at early stage of outbreak, CFR estimates should be viewed cautiously because of limited data source on true onset and recovery time.

Epidemic size of novel coronavirus-infected pneumonia in the Epicenter Wuhan: using data of five-countries' evacuation action (preprint)

Background: Since late December 2019, novel coronavirus-infected pneumonia (NCP) emerged in Wuhan, Hubei province, China. Meanwhile, NCP rapidly spread from China to other countries, and several countries' government rush to evacuate their citizens from Wuhan. We analyzed the infection rate of the evacuees and extrapolated the results in Wuhan's NCP incidence estimation. Methods: We collected the total number and confirmed cases of 2019-nCov infection in the evacuation of Korea, Japan, Germany, Singapore, and France and estimated the infection rate of the 2019 novel coronavirus (2019-nCov) among people who were evacuated from Wuhan with a meta-analysis. NCP incidence of Wuhan was indirectly estimated based on data of evacuation. Results: From Jan 29 to Feb 2, 2020, 1916 people have been evacuated from Wuhan, among them 17 have been confirmed 2019-nCov infected. The infection rate is estimated to be 1.1% (95% CI 0.4%-3.1%) using one group meta-analysis method with random effect model. We then estimated that almost 110,000 (95% CI: 40,000-310,000) people were infected with 2019-nCov in Wuhan around Feb 2, 2020, assuming the infection risk of evacuees is close to Chinese citizens in Wuhan. Conclusions: At the beginning of the outbreak, incidence of NCP may be vastly underestimated. Our result emphasizes that 2019-nCov has proposed a huge public health threats in Wuhan. We need to respond more rapidly, take large-scale public health interventions and draconian measures to limiting population mobility and control the epidemic.