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Patients narratives are being recorded increasingly frequently and spontaneously in short user produced content (UGC) films, which may have an impact on the vlogger's health as well as the public's comprehension of the relevant health concerns. This paper addressed three research questions regarding the population characteristics of UGC video publishers, the narrative theme of the videos, and the emotional orientation of the commenters. This study aimed to deepen our understanding of COVID-19 patients' narrative intentions and emotional needs through the theoretical frameworks of theory of planned behavior (TPB) and negative dominance theory (NDT). We collected 335 videos from 28 COVID-19 patients and 572,052 comments as samples on Douyin platform, the largest short-video website in China. Using Latent Semantic Analysis, we analyzed the descriptive information of the video blogs, the narrative textual information of the videos, and the emotional orientation of the comments. Our findings revealled seven categories of narrative themes, with 52.1% of video comments exhibiting a positive emotional orientation. Within a framework integrating TPB and NDT theories, we analyzed the behavioral intentions of vloggers and viewers during COVID-19 epidemic, and interpreted the persistent posting of videos and the active posting of comments as positive actions that counteracted the multiple effects of negative messages. This study contributes to the understanding of individual narratives in macro-risk communication, both theoretically and empirically, and offers policy recommendations in relevant fields. [ABSTRACT FROM AUTHOR]

A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models. [ABSTRACT FROM AUTHOR]

A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 and its variants are widely prevalent worldwide. With frequent secondary and breakthrough infections, immune dysfunction in RA patients, and long-term use of immune preparations, SARS-CoV-2 infection poses a significant challenge to patients and rheumatologists. Whether SARS-CoV-2 infection causes RA flares and what factors aggravate RA flares are poorly studied. A questionnaire survey was conducted on RA patients infected with SARS-CoV-2 after December 7, 2022, in China through a multicenter and inter-network platform regarding general personal condition, primary disease, comorbidity, SARS-CoV-2 vaccination, viral infection, and impact on the primary disease. A total of 306 RA patients were included in this study, and the patient data were analyzed, in which the general condition of RA patients, medication use before SARS-CoV-2 infection and post-infection typing and manifestations, and medication adjustment did not affect the Flare of RA patients after SARS-CoV-2 infection. The control of disease before SARS-CoV-2 infection (OR = 2.10), RA involving pulmonary lesions (OR = 2.28), and the recovery time of COVID-19 (OR = 2.50) were risk factors for RA flare. RA involving pulmonary lesions, control status of disease before infection, and recovery time of COVID-19 disease are risk factors for RA flare after SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

Spinal cord injuries are incredibly disabling and can have fatal consequences. At present, there is a lack of available information regarding the epidemiological characteristics of patients who have experienced spinal cord injury (SCI) in China. This retrospective hospital-based study was conducted in the Rehabilitation department of Wuhan's Tongji Hospital between 2016 and 2022. A total of 649 individuals diagnosed with SCI (both traumatic and non-traumatic) were admitted during this period. Data regarding various epidemiological features were gathered, including sex, age, etiology, occupation, neurological level of injury, the American Spinal Injury Association Impairment Scale at the time of admission, and information on any accompanying injuries. Out of the 649 cases of SCI, there were 539 cases of traumatic SCI and 110 cases of non-traumatic SCI. The mean age at the time of injury was 45.6 ± 14.8 years. The ratio of male to female patients was higher in traumatic SCI at 2.82:1compared to non-traumatic SCI at 1.68:1. Traffic accidents were the most common cause of TSCI, accounting for (n = 207/539; 38.40%) of cases. On the other hand, neoplasm was the most common cause of NTSCI, accounting for (n = 38/110; 34.54%) of cases. The findings indicated a higher proportion of males, with traffic accidents being the main cause of injury among TSCI patients. It is crucial to prioritize the risk of falling among older adults and allocate more attention to this issue. These results emphasize the need for tailored preventive strategies that consider the unique characteristics of different types of SCI patients. [ABSTRACT FROM AUTHOR]

To quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China. The number of international flights from March to September 2019 simulated the number of flights from March to September 2020 without implementing aviation control measures. In addition, the proportion of asymptomatic persons and the delay in case reporting were adjusted to estimate the prevalence of each country during the same period and calculate the estimated imported cases. The estimated imported cases were assigned each day with weight, and the estimated daily reported cases were obtained based on the actual daily number of domestic cases in China. Effective Reproduction Number ( R t ) was calculated based on delayed distribution, Basic Reproductive Number ( R 0 ) distribution, and generation time distribution were reported in previous studies. Gaussian Process was used to estimate the effect of time-varying on R t , and the estimated R t was compared with the actual R t . The estimated imported cases increased significantly compared with the actual number of imported cases. The estimated imported cases were mainly concentrated in North America and Europe from March to April and gradually increased in many East Asian countries from May to September. The difference between predicted R t and actual R t was statistically significant. The estimated imported cases and the estimated R t have increased compared to the actual situation. This paper quantitatively proves that Chinese aviation control measures significantly suppress the COVID-19 epidemic, which is conducive to promoting and applying this measure. [ABSTRACT FROM AUTHOR]

There is little empirical data on the heightened risk of loneliness among individuals residing in the COVID-19 epicenter or those who have recently left. This study compared the risk of loneliness in individuals residing in Wuhan, the COVID-19 epicenter in China, and those who had recently left during the initial outbreak period to those living in non-epicenter regions. During the COVID-19 outbreak in China in 2020, three samples were obtained using snowball sampling. The samples included 2882 epicenter residents, 1028 left residents, and 2963 non-epicenter residents. Loneliness was assessed using the six-item De Jong Gierveld Loneliness Scale, with a score of two or more indicating the presence of loneliness. 53.5% and 55.2% of epicenter and recently left residents, respectively, experienced loneliness, which was significantly higher than the 43.9% observed in non-epicenter residents. After adjusting for covariates, the risk of loneliness remained statistically significant for both epicenter and left residents (OR = 1.35, P < 0.001 and OR = 1.20, P = 0.017, respectively), when compared to non-epicenter residents. Individuals residing in the COVID-19 epicenter, as well as those who have recently left, are at a heightened risk of loneliness during the outbreak. These individuals need psychosocial support to mitigate their risk of loneliness and promote their psychological wellbeing. [ABSTRACT FROM AUTHOR]

Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises' production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic across Chinese mainland was gradually stabilized at a low level with sporadic outbreaks, before the emergence of Omicron variant. Apart from non-pharmacological interventions (NPIs), COVID-19 vaccine has also been implemented to prevent and control the pandemic since early 2021. Although many aspects have been focused, the change of the spatiotemporal distribution of COVID-19 epidemic across Chinese mainland responding to the change of prevention and control measures were less concerned. Here, we collected the confirmed case data (including domestic cases and overseas imported cases) across Chinese mainland during both 2020/04–2020/08 and 2021/04–2021/08, and then conducted a preliminary data comparison on the spatiotemporal distribution of confirmed cases during the identical period between the two years. Distribution patterns were evaluated both qualitatively by classification method and quantitatively through employing coefficient of variation. Results revealed significant differences in the homogeneity of spatiotemporal distributions of imported or domestic cases between the two years, indicating that the important effect of the adjustment of prevention and control measures on the epidemic evolution. The findings here enriched our practical experience of COVID-19 prevention and control. And, the collected data here might be helpful for improving or verifying spatiotemporally dynamic models of infectious diseases. [ABSTRACT FROM AUTHOR]

This paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases and the recovered cases from COVID-19 in China at the early stage, the proposed forecasting model is developed. The computational results demonstrate that the new model has higher precision than the other existing prediction models, which show the grey model has high accuracy in the forecasting of COVID-19. [ABSTRACT FROM AUTHOR]

A consensus species tree is reconstructed from 11 gene trees for human, bat, and pangolin beta coronaviruses from samples taken early in the pandemic (prior to April 1, 2020). Using coalescent theory, the shallow (short branches relative to the hosts) consensus species tree provides evidence of recent gene flow events between bat and pangolin beta coronaviruses predating the zoonotic transfer to humans. The consensus species tree was also used to reconstruct the ancestral sequence of human SARS-CoV-2, which was 2 nucleotides different from the Wuhan sequence. The time to most recent common ancestor was estimated to be Dec 8, 2019 with a bat origin. Some human, bat, and pangolin coronavirus lineages found in China are phylogenetically distinct, a rare example of a class II phylogeography pattern (Avise et al. in Ann Rev Eco Syst 18:489–422, 1987). The consensus species tree is a product of evolutionary factors, providing evidence of repeated zoonotic transfers between bat and pangolin as a reservoir for future zoonotic transfers to humans. [ABSTRACT FROM AUTHOR]

The spread of COVID-19 is closely related to the structure of human social networks. Based on 237 cases, by using epidemiological retrospective statistics, data visualization, and social network analysis methods, this paper summarized characteristics of patients with COVID-19 in Shaanxi, China, and analyzed these patients' dynamic contact network structure. The study found that there are many clustered infections through strong ties, about one-third of cases are caused by relatives' infection. In early stages of the epidemic, imported cases were the most, and in the later stages, local infection cases were the most. The infected people were mostly middle-aged men. Symptoms of imported cases occurred on average of 3 days after they arrived, and medical measures were taken 5 days later on average. All cases showed symptoms in less than 2 days on average and were then taken to medical treatment. The contact network can be divided into multiple disconnected components. The largest component has 12 patients. The average degree centrality in the network is 0.987, average betweenness degree is 0, average closeness degree is 0.452, and average PageRank index is 0.0042. The number of contacts of patients is unevenly distributed in the network. [ABSTRACT FROM AUTHOR]

In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China's non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = − 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics. [ABSTRACT FROM AUTHOR]

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of 'Urban population ratio', 'Retail sales of consumer goods', 'Number of tourists', 'Tourism Income', 'Ratio of the elderly population (> 60 year old) in this city', 'population density', 'Mobility scale (move in/inbound) during the spring festival', 'Ratio of Population and Health facilities', 'Jobless rate (%)', 'The straight-line distance from original epicenter Wuhan to this city', 'urban per capita GDP', and 'the prevalence of the COVID-19'. The study's results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic provides an opportunity to investigate how a new and long-lasting threat affects public risk perception and social distancing behavior, which is important for pandemic risk management and recovery of the tertiary industry. We have found that the mechanism that perception decides behavior changes over time. At the beginning of the pandemic, risk directly shapes people's willingness of going out. But under a persistent threat, perception no longer plays the direct role of shape people's willingness. Instead, perception indirectly influences the willingness by shaping people's judgment about the necessity of traveling. Switching from direct to indirect influence, perception's effect is enlarged, which partially prevents people from returning to normal life even if the governmental ban is removed in a zero-COVID community. [ABSTRACT FROM AUTHOR]

In the United States, anti-Asian sentiment has pervaded the Coronavirus 2019 (COVID-19) pandemic. Could Americans' fear of contracting the virus relate to prejudice against Asian individuals? According to intergroup threat theory, prejudice increases toward groups of people when they are perceived as a likely cause of symbolic and/or real threat, including disease threat. We tested this perspective in the context of the COVID-19 pandemic by investigating the relationship between Americans' concern about contracting COVID-19 and their feelings toward individuals from multiple countries. Between May 12–14 2020, participants residing in the United States (N = 932) completed an online survey assessing their (1) perceived threat of COVID-19 infection, (2) feelings of warmth and coldness toward people in America, China, Italy, Japan, and Greece, and (3) trait-level prejudice. Perceived threat of COVID-19 infection differentially related to feelings toward American and Chinese nationals and was unrelated to feelings toward people from other countries assessed. Specifically, greater threat of infection was associated with less warmth toward individuals from China, an effect moderated by trait-level prejudice. That is, participants high (but not medium or low) in trait prejudice showed a significant relationship between threat of COVID-19 infection and reduced warmth toward Chinese individuals. Threat of infection also related to greater warmth and less coldness toward American nationals, consistent with prior work indicating that disease threats amplify ethnocentrism. Collectively, results suggest that perceived threat of COVID-19 infection may correspond with prejudice toward the national outgroup associated with the disease's origin (i.e., China), as well as national ingroup favoritism, among Americans prone to prejudice. [ABSTRACT FROM AUTHOR]

A range of public health measures have been implemented to suppress local transmission of coronavirus disease 2019 (COVID-19) in Shenzhen. We examined the effect of these measures on the prevalence of respiratory pathogens in children. Clinical and respiratory pathogen data were collected for routine care from hospitalized children with acute respiratory infections in Shenzhen Children's Hospital from July 2018 to January 2022. Nasopharyngeal swabs were collected and respiratory pathogens were detected using standardized clinical diagnostics as part of routine care. Data were analyzed to describe the effects of COVID-19 prevention procedures on other common pathogens. A total of 56,325 children under 14 years of age were hospitalized with an acute respiratory infection during the study period, 33,909 were tested from July 2018 to January 2020 (pre-lockdown), 1168 from February 2020 to May 2020 (lockdown) and 21,248 from July 2020 to January 2022 (post-lockdown). We observed a 37.3% decline of routine care in respiratory infection associated hospital admission in the 19 months' post-lockdown vs. the 19 months' pre-lockdown. There were 99.4%, 16.0% and 1.26% reductions measured for Mycoplasma pneumoniae, influenza virus A and adenovirus, respectively. However, a 118.7% and 75.8% rise was found for respiratory syncytial virus (RSV) and human para-influenza virus (HPIV) during the 19 months' post-lockdown in comparison to the pre-pandemic period. The detection of RSV especially increased in toddlers after the lockdown. Lockdown measures during the COVID-19 pandemic led to a significant reduction of Mycoplasma pneumoniae, influenza virus A and adenovirus infection. In contrast, RSV and HPIV infection increased. [ABSTRACT FROM AUTHOR]

COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/− 21.5 (mean +/− SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment. [ABSTRACT FROM AUTHOR]

Lung cancer (LC) is the leading cause of cancer death in China and Australia, the countries with different socioenvironmental contexts in the Western Pacific Region. Comparing the age-period-cohort effect on LC mortality (LCM) between the two countries can help plan interventions and draw lessons for countries in the region. We collected LCM estimates between 1990 and 2019 from the GBD 2019. Age-period-cohort modelling was applied to compute the net drift, local drift, cross-sectional age curve, longitudinal age curve, and the rate ratios (RRs) of period and cohort. China had a higher LC age-standardized mortality rate than Australia in 2019 (men: 58.10 [95% uncertainty interval (UI): 46.53, 70.89] vs. 30.13 [95% UI: 27.88, 32.31]/100,000 population; women: 22.86 [95% UI: 18.52, 27.52] vs. 17.80 [95% UI: 15.93, 19.34]/100,000 population). Period and cohort effects on LCM improved more markedly among Australian men (RR for period effect, from 1.47 [95% confidence interval (CI) 1.41, 1.53] to 0.79 [95% CI 0.75, 0.84]; RR for cohort effect, from 2.56 [95% CI 2.44, 2.68] to 0.36 [95% CI 0.11, 1.18]) and Chinese women (RR for period effect, from 1.06 [95% CI 1.01, 1.11] to 0.85 [95% CI 0.82, 0.89]; RR for cohort effect, from 0.71 [95% CI 0.65, 0.78] to 0.51 [95% CI 0.26, 1.03]) during the study period and birth cohort. The LCM in Chinese population aged 65 to 79 and Australian women aged 75 to 79 increased. Smoking and particulate matter (PM) contributed most to LCM in China, while smoking and occupational carcinogens contributed most in Australia. Decreasing period and cohort risks for LCM attributable to smoking and PM were more remarkable in Australia than in China. The LCM attributable to occupational carcinogens was higher in Australia than in China, particularly for those aged 60 to 79. Vigorous tobacco and PM control, which brought a substantial decline in LCM in Australia, may help reduce LCM in China. Australia should highlight LC prevention among people with occupational exposure. Chinese aged ≥ 65 and Australian women aged ≥ 75 should be the priorities for LC interventions. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein–protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures. [ABSTRACT FROM AUTHOR]

To analyze the spatio-temporal aggregation of COVID-19 in mainland China within 20 days after the closure of Wuhan city, and provide a theoretical basis for formulating scientific prevention measures in similar major public health events in the future. Draw a distribution map of the cumulative number of COVID-19 by inverse distance weighted interpolation; analyze the spatio-temporal characteristics of the daily number of COVID-19 in mainland China by spatio-temporal autocorrelation analysis; use the spatio-temporal scanning statistics to detect the spatio-temporal clustering area of the daily number of new diagnosed cases. The cumulative number of diagnosed cases obeyed the characteristics of geographical proximity and network proximity to Hubei. Hubei and its neighboring provinces were most affected, and the impact in the eastern China was more dramatic than the impact in the western; the global spatio-temporal Moran's I index showed an overall downward trend. Since the 10th day of the closure of Wuhan, the epidemic in China had been under effective control, and more provinces had shifted into low-incidence areas. The number of new diagnosed cases had gradually decreased, showing a random distribution in time and space (P< 0.1), and no clusters were formed. Conclusion: the spread of COVID-19 had obvious spatial-temporal aggregation. China's experience shows that isolation city strategy can greatly contain the spread of the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Low pathogenic avian influenza viruses (LPAIVs) have been widespread in poultry and wild birds throughout the world for many decades. LPAIV infections are usually asymptomatic or cause subclinical symptoms. However, the genetic reassortment of LPAIVs may generate novel viruses with increased virulence and cross-species transmission, posing potential risks to public health. To evaluate the epidemic potential and infection landscape of LPAIVs in Guangxi Province, China, we collected and analyzed throat and cloacal swab samples from chickens, ducks and geese from the live poultry markets on a regular basis from 2016 to 2019. Among the 7,567 samples, 974 (12.87%) were LPAIVs-positive, with 890 single and 84 mixed infections. Higher yearly isolation rates were observed in 2017 and 2018. Additionally, geese had the highest isolation rate, followed by ducks and chickens. Seasonally, spring had the highest isolation rate. Subtype H3, H4, H6 and H9 viruses were detected over prolonged periods, while H1 and H11 viruses were detected transiently. The predominant subtypes in chickens, ducks and geese were H9, H3, and H6, respectively. The 84 mixed infection samples contained 22 combinations. Most mixed infections involved two subtypes, with H3 + H4 as the most common combination. Our study provides important epidemiological data regarding the isolation rates, distributions of prevalent subtypes and mixed infections of LPAIVs. These results will improve our knowledge and ability to control epidemics, guide disease management strategies and provide early awareness of newly emerged AIV reassortants with pandemic potential. [ABSTRACT FROM AUTHOR]

Outbreak of global pandemic Coronavirus disease 2019 (COVID-19) has so far caused countless morbidity and mortality. However, a detailed report on the impact of COVID-19 on hypertension (HTN) and ensuing cardiac injury is unknown. Herein, we have evaluated the association between HTN and cardiac injury in 388 COVID-19 (47.5 ± 15.2 years) including 75 HTN and 313 normotension. Demographic data, cardiac injury markers, other laboratory findings, and comorbidity details were collected and analyzed. Compared to patients without HTN, hypertensive-COVID-19 patients were older, exhibited higher C-reactive protein (CRP), erythrocyte sedimentation rate, and comorbidities such as diabetes, coronary heart disease, cerebrovascular disease and chronic kidney disease. Further, these hypertensive-COVID-19 patients presented more severe disease with longer hospitalization time, and a concomitant higher rate of bilateral pneumonia, electrolyte disorder, hypoproteinemia and acute respiratory distress syndrome. In addition, cardiac injury markers such as creatine kinase (CK), myoglobin, lactic dehydrogenase (LDH), and N-terminal pro brain natriuretic peptide were significantly increased in these patients. Correlation analysis revealed that systolic blood pressure correlated significantly with the levels of CK, and LDH. Further, HTN was associated with increased LDH and CK-MB in COVID- 19 after adjusting essential variables. We also noticed that patients with elevated either high sensitivity-CRP or CRP demonstrated a significant high level of LDH along with a moderate increase in CK (p = 0.07) and CK-MB (p = 0.09). Our investigation suggested that hypertensive patients presented higher risk of cardiac injury and severe disease phenotype in COVID-19, effectively control blood pressure in HTN patients might improve the prognosis of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P repayment. In our study, we applied four machine learning methods (random forest (RF), extreme gradient boosting tree (XGBT), gradient boosting model (GBM), and neural network (NN)) to predict important factors affecting repayment by utilizing data from Renrendai.com in China from Thursday, January 1, 2015, to Tuesday, June 30, 2015. The results showed that borrowers who have passed video, mobile phone, job, residence or education level verification are more likely to default on loan repayment, whereas those who have passed identity and asset certification are less likely to default on loans. The accuracy and kappa value of the four methods all exceed 90%, and RF is superior to the other classification models. Our findings demonstrate important techniques for borrower screening by P2P companies and risk regulation by regulatory agencies. Our methodology and findings will help regulators, banks and creditors combat current financial disasters caused by the coronavirus disease 2019 (COVID-19) pandemic by addressing various financial risks and translating credit scoring improvements. [ABSTRACT FROM AUTHOR]

Since the first report of SARS-CoV-2 in China in 2019, there has been a huge debate about the origin. In this work, using a different method we aimed to strengthen the observation that no evidence of genetic manipulation has been found by (1) detecting classical restriction site (RS) sequence in human SARS-CoV-2 genomes and (2) comparing them with other recombinant SARS-CoV-like virus created for experimental purposes. Finally, we propose a novel approach consisting in the generation of a restriction endonucleases site map of SARS-CoV-2 and other related coronavirus genomes to be used as a fingerprint to trace the virus evolution. [ABSTRACT FROM AUTHOR]

We examined characteristics of chest CT across different time periods for patients with COVID-19 pneumonia in Huizhou, China. This study included 56 COVID-19 patients with abnormal CT acquired between January 22 and March 3, 2020. The 141 scans of 56 patients were classified into four groups (Groups 1–4) based on dates on which scans were obtained at the 1st, 2nd, 3rd week or longer than three weeks after illness onset. Forty-five patients with follow-up scans were categorized into four groups (Groups A–D) according to extent that lesions reduced (≥ 75%, 50–75%, 25–50% and < 25%). Ground-glass opacities (GGO) was prevalent in Groups 1–4 (58.1–82.6%), while percentages of consolidation ranged between 9.7% in Group 4 and 26.2% in Group 2. The highest frequency of fibrous stripes occurred in Group 3 (46.7%). Total CT scores were on average higher in Groups 2–3. Among 45 follow-up patients, 11 (24.4%) of them recovered with lesions reducing ≥ 75%, with the lowest median age and total CT scores on admission. There are temporal patterns of lung abnormalities in COVID-19 patients, with higher extent of lesion involvement occurring in the 2nd and 3rd week. Persisting lung changes indicate some patients may need isolation after discharge from hospital. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is an important and urgent threat to global health. Inflammation factors are important for COVID-19 mortality, and we aim to explore whether the baseline levels of procalcitonin (PCT), C-reaction protein (CRP) and neutrophil-to-lymphocyte ratio (NLR) are associated with an increased risk of mortality in patients with COVID-19. A retrospective study was conducted and a total of 76 patients with confirmed COVID-19 were included between January 17, 2020 to March 2, 2020, of these cases, 17 patients were dead. After adjusting covariates, PCT (≥ 0.10 ng/mL) and CRP (≥ 52.14 mg/L) exhibited independent increasing risks of mortality were used hazard ratio (HR) of 52.68 (95% confidence interval [CI]: 1.77–1571.66) and 5.47 (95% CI: 1.04–28.72), respectively. However, NRL (≥ 3.59) was not found to be an independent risk factor for death in our study. Furthermore, the elevated PCT levels were still associated with increasing risk of mortality in the old age group (age ≥ 60 y), and in the critically severe and severe patients after adjustment for complications. Thu Baseline levels of PCT and CRP have been addressed as independent predictors of mortality in patients with COVID-19. [ABSTRACT FROM AUTHOR]

A novel coronavirus (SARS-CoV-2) emerged from China in late 2019 and rapidly spread across the globe, infecting millions of people and generating societal disruption on a level not seen since the 1918 influenza pandemic. A safe and effective vaccine is desperately needed to prevent the continued spread of SARS-CoV-2; yet, rational vaccine design efforts are currently hampered by the lack of knowledge regarding viral epitopes targeted during an immune response, and the need for more in-depth knowledge on betacoronavirus immunology. To that end, we developed a computational workflow using a series of open-source algorithms and webtools to analyze the proteome of SARS-CoV-2 and identify putative T cell and B cell epitopes. Utilizing a set of stringent selection criteria to filter peptide epitopes, we identified 41 T cell epitopes (5 HLA class I, 36 HLA class II) and 6 B cell epitopes that could serve as promising targets for peptide-based vaccine development against this emerging global pathogen. To our knowledge, this is the first study to comprehensively analyze all 10 (structural, non-structural and accessory) proteins from SARS-CoV-2 using predictive algorithms to identify potential targets for vaccine development. [ABSTRACT FROM AUTHOR]