Since December 2019, the seriousness of coronavirus disease 2019 (COVID‐19) pandemic has been on escalation (Lu, et al 2020). Coagulopathy is common in critically ill patients with COVID‐19 (Tang, et al 2020b). Systemic microvascular thrombosismay occur in most deaths, which was further identified by a recent autopsy (Luo 2020). However, less has been known about the coagulation parameter D‐dimer in the progression of COVID‐19. In this study, we describe 279 COVID‐19 patients recruited from three hospitals in Hubei Province, China, and then investigate the dynamic relationship between D‐dimer level and the progression of COVID‐19.

We present a timely evaluation of the Chinese 2019-nCov epidemic in its initial phase, where 2019-nCov demonstrates comparable transmissibility but lower fatality rates than SARS and MERS. A quick diagnosis that leads to case isolation and integrated interventions will have a major impact on its future trend. Nevertheless, as China is facing its Spring Festival travel rush and the epidemic has spread beyond its borders, further investigation on its potential spatiotemporal transmission pattern and novel intervention strategies are warranted.

The coronavirus disease 2019 (COVID-19) emerged by end of 2019, and became a serious public health threat globally in less than half a year. The generation interval and latent period, though both are of importance in understanding the features of COVID-19 transmission, are difficult to observe, and thus they can rarely be learnt from surveillance data empirically. In this study, we develop a likelihood framework to estimate the generation interval and incubation period simultaneously by using the contact tracing data of COVID-19 cases, and infer the pre-symptomatic transmission proportion and latent period thereafter. We estimate the mean of incubation period at 6.8 days (95 %CI: 6.2, 7.5) and SD at 4.1 days (95 %CI: 3.7, 4.8), and the mean of generation interval at 6.7 days (95 %CI: 5.4, 7.6) and SD at 1.8 days (95 %CI: 0.3, 3.8). The basic reproduction number is estimated ranging from 1.9 to 3.6, and there are 49.8 % (95 %CI: 33.3, 71.5) of the secondary COVID-19 infections likely due to pre-symptomatic transmission. Using the best estimates of model parameters, we further infer the mean latent period at 3.3 days (95 %CI: 0.2, 7.9). Our findings highlight the importance of both isolation for symptomatic cases, and for the pre-symptomatic and asymptomatic cases.

The effects of acupuncture on in vitro fertilization (IVF) outcomes remain controversial. And the variation in participant, interventions, outcomes studied, and trial design may relate to the efficacy of adjuvant acupuncture. We searched digital databases for relevant studies, including Embase, PubMed, Cochrane Library and some Chinese databases up to December 2018, for randomized controlled trials (RCTs) evaluating the effects of acupuncture on women undergoing IVF. We included studies with intervention groups using needling, and control groups consisting of no acupuncture or sham (placebo) acupuncture. Primary outcomes were clinical pregnancy rate (CPR) and live birth rate (LBR). Meta-regression and subgroup analysis were conducted on the basis of eight pre-specified covariates to investigate the variances of the effects of adjuvant acupuncture on pregnancy rates and the sources of heterogeneity. Twenty-seven studies with 6116 participants were included. The pooled clinical pregnancy rate (CPR) from all of acupuncture groups was significantly greater than that of control groups (RR 1.21, 95% CI: 1.07–1.38), whereas the pooled live birth rate (LBR) was not. Meta-regression subgroup analysis showed a more significant benefit of acupuncture for repeated IVF cycle proportion (number of women with a history of prior unsuccessful IVF attempt divided by number of women included in each trial) ≥ 50% group (CPR: RR 1.60, 95% CI: 1.28–2.00; LBR: RR 1.42, 95% CI: 1.05–1.92), and this covariate explained most of the heterogeneity (CPR and LBR: adjusted R2 = 100 and 87.90%). Similar results were found between CPR and number of acupuncture treatments (CPR: p = 0.002, adjusted R2 = 51.90%), but not LBR. Our analysis finds a benefit of acupuncture for IVF outcomes in women with a history of unsuccessful IVF attempt, and number of acupuncture treatments is a potential influential factor. Given the poor reporting and methodological flaws of existing studies, studies with larger scales and better methodologies are needed to verify these findings.

Background: Coronavirus disease 2019 (COVID-19) has caused a serious epidemic around the world, but it has been effectively controlled in the mainland of China. The Chinese government limited the migration of people almost from all walks of life. Medical workers have rushed into Hubei province to fight against the epidemic. Any activity that can increase infection is prohibited. The aim of this study was to confirm that timely lockdown, large-scale case-screening and other control measures proposed by the Chinese government were effective to contain the spread of the virus in the mainland of China. Methods: Based on disease transmission-related parameters, this study was designed to predict the trend of COVID-19 epidemic in the mainland of China and provide theoretical basis for current prevention and control. An SEIQR epidemiological model incorporating asymptomatic transmission, short term immunity and imperfect isolation was constructed to evaluate the transmission dynamics of COVID-19 inside and outside of Hubei province. With COVID-19 cases confirmed by the National Health Commission (NHC), the optimal parameters of the model were set by calculating the minimum Chi-square value. Results: Before the migration to and from Wuhan was cut off, the basic reproduction number in China was 5.6015. From 23 January to 26 January 2020, the basic reproduction number in China was 6.6037. From 27 January to 11 February 2020, the basic reproduction number outside Hubei province dropped below 1, but that in Hubei province remained 3.7732. Because of stricter controlling measures, especially after the initiation of the large-scale case-screening, the epidemic rampancy in Hubei has also been contained. The average basic reproduction number in Hubei province was 3.4094 as of 25 February 2020. We estimated the cumulative number of confirmed cases nationwide was 82 186, and 69 230 in Hubei province on 9 April 2020. Conclusions: The lockdown of Hubei province significantly reduced the basic reproduction number. The large-scale case-screening also showed the effectiveness in the epidemic control. This study provided experiences that could be replicated in other countries suffering from the epidemic. Although the epidemic is subsiding in China, the controlling efforts should not be terminated before May.

The cluster of pneumonia cases linked to coronavirus disease 2019 (Covid-19), first reported in China in late December 2019 raised global concern, particularly as the cumulative number of cases reported between 10 January and 5 March 2020 reached 80,711. In order to better understand the spread of this new virus, we characterized the spatial patterns of Covid-19 cumulative cases using ArcGIS v.10.4.1 based on spatial autocorrelation and cluster analysis using Global Moran’s I (Moran, 1950), Local Moran’s I and Getis-Ord General G (Ord and Getis, 2001). Up to 5 March 2020, Hubei Province, the origin of the Covid-19 epidemic, had reported 67,592 Covid-19 cases, while the confirmed cases in the surrounding provinces Guangdong, Henan, Zhejiang and Hunan were 1351, 1272, 1215 and 1018, respectively. The top five regions with respect to incidence were the following provinces: Hubei (11.423/10,000), Zhejiang (0.212/10,000), Jiangxi (0.201/10,000), Beijing (0.196/10,000) and Chongqing (0.186/10,000). Global Moran’s I analysis results showed that the incidence of Covid-19 is not negatively correlated in space (p=0.407413>0.05) and the High-Low cluster analysis demonstrated that there were no high-value incidence clusters (p=0.076098>0.05), while Local Moran’s I analysis indicated that Hubei is the only province with High-Low aggregation (p<0.0001).