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Aims Understanding the transmission mode of syphilis is essential to prevent and predict its future prevalence and to develop effective control measures. This study aimed to develop a network suspected infectious disease model to simulate the syphilis transmission. Methods and results The number of syphilis cases in Wuhan's Fourth Hospital, Hubei province, China, from October 2015 to July 2021 was collected. The simulation was carried out by interpersonal network-SI (suspected infectious) model based on temporal exponential family random graph models. Late latent syphilis and tertiary syphilis are predicted by December 2025. The validity of simulated value and real data was tested, including determination coefficient (R 2), root means square error (RMSE), and means relative error (MRE). Moreover, we developed an online app that can more easily predict the number of syphilis infections in different scenarios by setting different parameters. Results showed that R 2, RMSE, and MRE were 0.995, 36.19, and 6.31, respectively. Speed from latent infection to primary syphilis, primary syphilis to secondary syphilis, and susceptible group to latent infection decreased rapidly. The speed of transformation from secondary syphilis to early incubation period and early latent to late latent experienced a process from increase to decreased. Late latent to tertiary syphilis patients increased steadily. The number of late latent patients, early latent, invisible infection, primary syphilis, and secondary syphilis all increased at first and turn to decreased. However, tertiary syphilis continuously kept rising in the whole process. To better make use of the transmission model, an online application was developed (https://alanwu.shinyapps.io/MD-shiny/). Conclusions Based on the simulation that late latent and tertiary syphilis were steadily increasing, the prevention and treatment for syphilis were imperative. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that rapidly spreads worldwide and co-infection of COVID-19 and influenza may occur in some cases. We aimed to describe clinical features and outcomes of severe COVID-19 patients with co-infection of influenza virus.Methods: Retrospective cohort study was performed and a total of 140 patients with severe COVID-19 were enrolled in designated wards of Sino-French New City Branch of Tongji Hospital between Feb 8th and March 15th in Wuhan city, Hubei province, China. The demographic, clinical features, laboratory indices, treatment and outcomes of these patients were collected.Results: Of 140 severe COVID-19 hospitalized patients, including 73 patients (52.14%) with median age 62 years were influenza virus IgM-positive and 67 patients (47.86%) with median age 66 years were influenza virus IgM-negative. 76 (54.4%) of severe COVID-19 patients were males. Chronic comorbidities consisting mainly of hypertension (45.3%), diabetes (15.8%), chronic respiratory disease (7.2%), cardiovascular disease (5.8%), malignancy (4.3%) and chronic kidney disease (2.2%). Clinical features, including fever (≥38 °C), chill, cough, chest pain, dyspnea, diarrhea and fatigue or myalgia were collected. Fatigue or myalgia was less found in COVID-19 patients with IgM-positive (33.3% vs 50/7%, P = 0.0375). Higher proportion of prolonged activated partial thromboplastin time (APTT) > 42 s was observed in COVID-19 patients with influenza virus IgM-negative (43.8% vs 23.6%, P = 0.0127). Severe COVID-19 Patients with influenza virus IgM positive have a higher cumulative survivor rate than that of patients with influenza virus IgM negative (Log-rank P = 0.0308). Considering age is a potential confounding variable, difference in age was adjusted between different influenza virus IgM status groups, the HR was 0.29 (95% CI, 0.081-1.100). Similarly, difference in gender was adjusted as above, the HR was 0.262 (95% CI, 0.072-0.952) in the COX regression model.Conclusions: Influenza virus IgM positive may be associated with decreasing in-hospital death. [ABSTRACT FROM AUTHOR]

Poverty elimination poses a great challenge to all countries around the world, and the Chinese government is consistently concerned with poverty issues. Rural land consolidation (RLC) is the important measure to alleviate poverty in rural China by improving the efficiency of land use. From the perspective of future poverty, the study analyzes the mechanism by which different RLC models, the government-dominant model and the Public-Private-Partnership (PPP) model, decrease poverty vulnerability by enhancing capabilities and endowing rights under the framework of the theory of capacity poverty proposed by Amartya Sen. Based on survey data of 562 rural households from Hubei and Guizhou Provinces, this study explores the role of and differences between two RLC models in decreasing the poverty vulnerability of registered and non-registered households through the PSM-DID estimator. The main findings are as follows: (1) The proportions of poor households and poverty vulnerability households in both registered and non-registered households have declined after the implementation of RLC, revealing that poverty vulnerability occurs along with poverty; (2) Both RLC models can significantly reduce the poverty vulnerability of both types of rural households. Furthermore, the effect of reducing the poverty vulnerability of non-registered households is greater than that of registered households; (3) The PPP RLC model reduces the poverty vulnerability much more effectively than the government-led model. Based on the above findings, the paper proposes corresponding policy recommendations for improving stability of RLC for poverty alleviation, thereby decreasing the probability of future poverty. Additionally, this study provides references for the developing areas and countries to make long-acting RLC policies for poverty alleviation. • Theoretically reveal the mechanisms of different models of RLC to alleviate rural household poverty vulnerability. • Examine the effect of different RLC models on poverty alleviation from the perspective of poverty vulnerability. • Put forward anti-poverty RLC policies to curb the occurrence of poverty in the future. [ABSTRACT FROM AUTHOR]

• It evaluates the effect of China's ETS on non-fossil energy development. • It analyzes ETS's effects from energy type, pilot, and pilot-energy type aspects. • The ETS has the most significant promoting effect on hydropower and photovoltaics. • Carbon price plays a positive role in moderating the effect of China's ETS. Emissions Trading Scheme (ETS) can internalize the environmental costs of fossil energy consumption and encourage enterprises to consume non-fossil energy, which is crucial for People's Republic of China (PRC) to achieve the contribution target of the Paris Agreement cost-effectively. However, whether the ETS in PRC can promote non-fossil energy development remains to be answered. This paper uses a difference-in-differences model to evaluate the effect of the ETS on non-fossil energy development in PRC. Using provincial monthly panel data from January 2004 to December 2019, we find that (1) the ETS has significantly promoted non-fossil energy development in PRC, increasing the monthly average share of non-fossil energy power generation in total power generation by 2.326 percentage points, which has been verified by a series of robustness tests. (2) The effect of the ETS on non-fossil energy development is heterogeneous by energy type, pilot region, and pilot-energy type. The ETS has significantly increased the share of hydropower and photovoltaic power generation, and has significantly promoted non-fossil energy development in Guangdong; meanwhile, the ETS has significantly increased the share of hydropower and nuclear power in Guangdong, and the share of wind power and photovoltaic power in Hubei. (3) The higher the carbon price, the greater the role of the ETS in promoting non fossil energy development. [ABSTRACT FROM AUTHOR]

Background: At present, the epidemic of the novel coronavirus disease 2019 (COVID-19) has quickly engulfed the world. Inflammatory cytokines are associated with the severity and outcomes of patients with COVID-19. However, the prognostic value of pro-inflammatory factors in cancer patients with COVID-19 are unknown. Methods: A multi-center, retrospective, cross-sectional study, based on five designated tertiary hospitals for the treatment of COVID-19 in Hubei Province, China. 112 cancer patients with COVID-19, and 105 COVID-19 patients without cancer were enrolled in the study between January 1st, 2020 and April 30th, 2020. The risk assessment of pro-inflammatory factors for disease severity and clinical adverse outcomes was identified by univariable and multivariable logistic regression models. Results: Of the 112 cancer patients with COVID-19, 40 (35.7%) patients were in critical condition and 18 (16.1%) patients died unfortunately. Univariate and multivariate analysis demonstrated that hemoglobin level and pro-inflammatory neutrophils and C-reactive protein (CRP), can be used as independent factors affecting the severity of COVID-19; Meanwhile, pro-inflammatory neutrophils and CRP can be used as an independent influencing factor for adverse clinical outcome of death. Moreover, the dynamic changes of neutrophils and CRP were also presented, and compared with COVID-19 patients without cancer, cancer patients with COVID-19 showed higher neutrophil counts and CRP levels. Conclusion: In cancer patients with COVID-19, the significant increase in pro-inflammatory neutrophils and CRP indicated a more critical illness and adverse clinical outcome, and pro-inflammatory neutrophils and CRP played a greater adverse role compare with COVID-19 patients without cancer, which may be the cause of critical illness and adverse clinical outcomes of cancer patients with COVID-19. [ABSTRACT FROM AUTHOR]