新型冠状病毒肺炎的传染动力学

2019年末在武汉出现新型冠状病毒肺炎(Coronavirus disease 2019,COVID-19)患者,此后两个月疫情进入暴发期。为了科学有效地对疫情发展趋势进行预判,本研究根据国家及各地方卫生健康委员会公布的官方数据建立了COVID-19确诊、死亡及治愈病例的Logistic分段模型。模型预计全国累计确诊病例将达到81 370～102 800人,累计死亡病例将达到3 177～3 960人,最终病死率约为3.9%。在现有的医疗条件和诊断标准下,预计到2020年5月上旬,全国确诊病例(除死亡外)将全部治愈出院。此模型能较好地拟合和预测COVID-19疫情的不同传播阶段和发展趋势,将为疫情防控提供科学依据。     

本期导读

自2019年12月以来,新型冠状病毒肺炎在全球蔓延,2020年1月30日,世界卫生组织(World Health Organization,WHO)将新型冠状病毒肺炎疫情列为"国际关注的突发公共卫生事件",我国也将该病作为急性呼吸道传染病纳入《中华人民共和国传染病防治法》规定的乙类传染病,并按照甲类传染病管理。2020年2月11日,WHO正式将新型冠状病毒肺炎命名为COVID-19(Corona Virus Disease 2019)。国际病毒分类委员会将该病毒命名为严重急性呼吸系统综合征冠状病毒(severe acute respiratory syndrome coromavirus 2,SARS-CoV-2)。截止2020年8月28日全球已累计确诊24356983人,累计死亡826791人。严重危害着人类的健康和生命安全。虽然中国政府采取了最强有力的措施,社会各界一起抗击疫情,取得了显著成效。但是由于目前COVID-19疫苗尚未上市,也无特效药物,COVID-19的全球快速蔓延势头仍没有得到有效遏制。世界各国的科学家、医学家们正在努力开发针对SARS-CoV-2和COVID-19的安全有效的预防疫苗和治疗方法。     

基于网络药理学的血必净注射液干预细胞因子风暴辅助治疗重症新型冠状病毒肺炎的潜在机制研究

2019年12月底于我国湖北省武汉市爆发新型冠状病毒肺炎(COVID-19),截止2020年4月30日,我国共确诊病例8万余,全球已确诊病例300余万,其传染性强,传播迅速广泛,引起了国内外高度关注。重症患者可因"细胞因子风暴"在中低热甚至无明显发热病程中快速进展为急性呼吸窘迫综合征、多器官衰竭等严重并发症。目前在《新型冠状病毒感染的肺炎诊疗方案》中要求加强中西医结合,中西医结合治疗也收到了积极效果。本文运用网络药理学方法探讨血必净注射液辅助治疗重症COVID-19与干预细胞因子风暴密切相关的作用路径和潜在的机制靶点,有望对现有的诊疗方案中药临床应用做有益补充,发挥抗击疫情的重要作用。     

新型冠状病毒实验室检测方法研究进展

世界范围内流行的SARS-CoV-2已造成大批新型冠状病毒肺炎(COVID-19)患者,严重威胁着全人类生命健康.新型冠状病毒肺炎尚没有特效药,也没有疫苗,实验室确诊新型冠状病毒肺炎,隔离传染源,尽早治愈患者对整个疫情防控起着非常重要的作用.目前实验室检测方法有病毒分离培养、实时荧光定量PCR、环介导等温扩增技术、CRISPR/Cas技术、测序技术、基因芯片和抗原抗体检测.本文就上述几种方法做一综述,为确诊COVID-19提供参考.     

3例COVID-19出院后核酸复检阳性患者的检测分析

新型冠状病毒(SARS-CoV-2)核酸检测是新型冠状病毒肺炎(COVID-19)病例确诊的金标准,血清抗体检测是重要辅助手段。本文对重庆市江津区中心医院3例COVID-19出院后核酸复检阳性患者,回顾性分析在疾病初期确诊、治疗出院、转诊隔离全程诊疗过程中患者临床病例及其核酸和抗体检测结果,结合患者体内新冠病毒核酸确诊到完全转阴时间变化,初步探讨COVID-19临床治愈出院患者核酸和抗体检测与疾病传染性因素的相关性,为新冠肺炎患者治疗康复过程的病毒感染监测提供参考。     

群体免疫理论与COVID-19的管控

2019年12月底,不明原因的肺炎病例在全球爆发,经检测是新型冠状病毒感染所致.感染该病毒引起的肺炎被命名为"COVID-19".从早期的"不明肺炎",到如今疫情在全球持续传播,急剧增长的肺炎病例确诊数字背后,各个国家(如中国,英国)对于新型冠状病毒的认知和防范都有着哪些不同的应对策略?热搜中的英国的"群体免疫"策略是...     

某起新型冠状病毒肺炎聚集性疫情的流行病学分析

目的:通过分析一起新型冠状病毒肺炎(coronavirus disease 2019,COVID-19)聚集性疫情病例的流行病学特征,为疫情防控提供科学参考依据.方法:以新型冠状病毒肺炎防控技术方案的相关内容为依据,对一起聚集性疫情病例发病、就诊、接触方式及代际传播等进行描述性流行病学分析.结果:该起聚集性疫情涉及6例...     

SARS-CoV-2入侵细胞相关分子结构与机制的研究进展

新型冠状病毒肺炎(corona virus disease 2019,COVID-19)疫情的迅速发展与其病原体新型冠状病毒(severe acute respiratory syndrome coronavirus 2,SARS-CoV-2)具有的高传播、高感染能力关系密切.这是一种新型β属冠状病毒,为了探究其高传染...     

用于临床新型冠状病毒核酸检测的分子诊断新技术

目前新型冠状病毒肺炎(corona virus disease 2019, COVID-19)在全球仍呈现大流行趋势,该病潜伏期长、传染性强,给全世界人民的健康安全带来严重威胁。新型冠状病毒(severe acute respiratory syndrome coronavirus2, SARS-CoV-2)核酸检测作为COVID-19确诊的重要依据之一,在疫情防控工作中发挥了巨大的作用。截至2020年8月17日,国家药品监督管理局已应急批准15个新型冠状病毒核酸检测试剂盒,其中10个试剂盒是以逆转录-实时荧光定量PCR (RT-qPCR)为主要技术原理,其余试剂盒分别采用了不同于RT-qPCR的5种分子诊断技术。本文对上述检测试剂盒所使用的技术原理、反应时间、优缺点等方面进行了介绍,以期为COVID-19及类似传染病的快速筛查、确诊、防控提供更多的方案思路。     

以新型冠状病毒肺炎为例浅析血浆疗法的现况及发展

自2019年12月湖北省武汉地区出现新型冠状病毒肺炎(COVID-19)以来,疫情发展迅速,对我国乃至全球公共卫生构成巨大威胁,造成巨大经济损失和社会恐慌。重症是导致COVID-19死亡的最主要原因,2020年2月19日国家卫生健康委员会出台《新冠肺炎康复者恢复期血浆临床治疗方案(试行第一版)》,主要用于医治重型、危重型病例。血浆疗法这一治疗策略可追溯到20世纪,在目前没有有效疫苗和药物的情况下,仍可使用患者恢复期血浆预防或治疗传染病,阻止疾病恶化,降低病死率。本文对新型冠状病毒进行简要介绍,回顾以往血浆疗法在冠状病毒治疗方面的应用及成效,并分析了血浆疗法的局限性。进一步结合目前抗病毒治疗性抗体的研究现状及发展优势,对COVID-19的抗体治疗提出思考。     

中国和伊朗新型冠状病毒肺炎流行现状及比较分析

新型冠状病毒肺炎(Coronavirus disease 2019,COVID-19)已造成全球大流行,比较不同国家流行特征是了解该病流行规律的重要内容。为了推进我国提出的"一带一路"倡议,防止传染病跨境传播,并探讨COVID-19流行规律和不同国家传染病研究方法,本研究对中国和伊朗COVID-19流行特征和影响因素进行比较分析。根据世界卫生组织、国家卫生健康委员会及新闻媒体等提供的数据和信息,采用描述流行病学方法和Pearson相关进行分析,结果显示,截至2020年7月31日24时,中国累计报告确诊病例88122例,占全球报告0.50%,位居28位;同期伊朗确诊304204例,占全球1.73%,位居全球第9位。2020年4月22日伊朗报告累计确诊病例数首次超过中国,中国宁夏回族自治区、北京市、上海市、甘肃省、广东省分别报告有伊朗输入病例。COVID-19流行水平和严重程度伊朗高于中国:伊朗的发病率为371/10万、中国6.29/10万;伊朗的死亡率为20/10万、中国0.33/10万;伊朗的治愈率为86.63%、中国为92.18%;病死率两国水平接近;伊朗平均每日新增确诊病例数1395例,中国404例。中国疫情出现拐点的时间较伊朗早,每日新增确诊病例首次出现下降用时25d(伊朗42d)、每日新增治愈超过新增确诊用时31d(伊朗49d)。伊朗疫情在5月后出现持续反弹,2020年6月4日确诊病例数达反弹峰值(3574例),超过前期的3186例,且在多数时间新增治愈人数低于新增确诊人数。中国和伊朗每日新增确诊、新增治愈病例数趋势形态不同,中国新增治愈病例数趋势图整体延后新增确诊病例,而伊朗新增治愈与新增确诊病例数几乎同步。相关分析显示武汉每日新增确诊病例数在划分的两个阶段与预防控制措施得分均呈正相关(相关系数分别为0.44、0.53),而与当时武汉的气象因素平均气压(百帕)、平均2min风速(米/秒)、平均气温(℃)、平均相对湿度(百分率)、日照时数(时)无相关性;伊朗在疫情的第一阶段呈正相关,第二阶段为负相关。COVID-19在中国和伊朗的流行存在差异,对不同国家流行情况进行比较研究,有助于加深对COVID-19全球大流行的认识。     

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.     

An analytical study of the factors that influence COVID-19 spread

The outbreak of the coronavirus disease 2019 (COVID-19) continues to constitute an international public health emergency. Seasonality is a long-recognized attribute of many viral infections of humans. Nevertheless, the relationship between environmental factors and the spread of infection, particularly for person-to-person communicable diseases, remains poorly understood. This study explores the relationship between environmental factors and the incidence of COVID-19 in 188 countries with reported COVID-19 cases as of April 13, 2020. Here we show that COVID-19 growth rates peaked in temperate zones in the Northern Hemisphere during the outbreak period, while they were lower in tropical zones. The relationships between COVID-19 and environmental factors were resistant to the potentially confounding effects of air pollution, sea level, and population. To prove the effect of those factors, study, and analysis of the prevalence of COVID-19 in Italy, Spain, and China was undertaken. A fuzzy logic system was designed to predict the effects of that variables on the rate of viral spread of COVID-19.     

Dating first cases of COVID-19

Questions persist as to the origin of the COVID-19 pandemic. Evidence is building that its origin as a zoonotic spillover occurred prior to the officially accepted timing of early December, 2019. Here we provide novel methods to date the origin of COVID-19 cases. We show that six countries had exceptionally early cases, unlikely to represent part of their main case series. The model suggests a likely timing of the first case of COVID-19 in China as November 17 (95% CI October 4). Origination dates are discussed for the first five countries outside China and each continent. Results infer that SARS-CoV-2 emerged in China in early October to mid-November, and by January, had spread globally. This suggests an earlier and more rapid timeline of spread. Our study provides new approaches for estimating dates of the arrival of infectious diseases based on small samples that can be applied to many epidemiological situations.     

Epidemiological Characteristics and Spatial-Temporal Clusters of Hand,Foot, and Mouth Disease in Zhejiang Province,China, 2008-2012

Hand, foot and mouth disease (HFMD) is one of the major public health concerns in China. Being the province with high incidence rates of HFMD, the epidemiological features and the spatial-temporal patterns of Zhejiang Province were still unknown. The objective of this study was to investigate the epidemiological characteristics and the high-incidence clusters, as well as explore some potential risk factors. The surveillance data of HFMD during 2008–2012 were collected from the communicable disease surveillance network system of Zhejiang Provincial Center for Disease Control and Prevention. The distributions of age, gender, occupation, season, region, pathogen’s serotype and disease severity were analyzed to describe the epidemiological features of HFMD in Zhejiang Province. Seroprevalence survey for human enterovirus 71 (EV71) in 549 healthy children of Zhejiang Province was also performed, as well as 27 seroprevalence publications between 1997 and 2015 were summarized. The spatial-temporal methods were performed to explore the clusters at county level. Furthermore, pathogens’ serotypes such as EV71 and coxsackievirus A16 (Cox A16) and meteorological factors were analyzed to explore the potential factors associated with the clusters. A total of 454,339 HFMD cases were reported in Zhejiang Province during 2008–2012, including 1688 (0.37%) severe cases. The annual average incidence rate was 172.98 per 100,000 (ranged from 72.61 to 270.04). The male-to-female ratio for mild cases was around 1.64:1, and up to 1.87:1 for severe cases. Of the total cases, children aged under three years old and under five years old accounted for almost 60% and 90%, respectively. Among all enteroviruses, the predominant serotype was EV71 (49.70%), followed by Cox A16 (26.05%) and other enteroviruses (24.24%) for mild cases. In severe cases, EV71 (82.85%) was the major causative agent. EV71 seroprevalence survey in healthy children confirmed that occult infection was common in children. Furthermore, literature summary for 26 seroprevalence studies during 1997–2015 confirmed that 0–5 years group showed lowest level of EV71 seroprevalence (29.1% on average) compared to the elder children (6–10 years group: 54.6%; 11–20 years group: 61.8%). Global positive spatial autocorrelation patterns (Moran’s Is>0.25, P<0.05) were discovered not only for mild cases but also for severe cases, and local positive spatial autocorrelation patterns were revealed for counties from the eastern coastal and southern regions. The retrospective space-time cluster analysis also confirmed these patterns. Risk factors analyses implied that more EV71 and less sunshine were associated with the clusters of HFMD in Zhejiang Province. Our study confirmed that Zhejiang Province was one of the highly epidemic provinces in China and that the epidemiological characteristics of HFMD were similar to other provinces. Occult infection in elder children and adults was one of the important reasons why most HFMD cases were children aged under-five. Combining the results of spatial autocorrelation analysis and the space-time cluster analysis, the major spatial-temporal clusters were from the eastern coastal and southern regions. The distribution of pathogens’ serotypes and the level of sunshine could be risk factors for, and serve as an early warning of, the outbreak of HFMD in Zhejiang Province.     

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level

The Islamic Republic of Iran reported its first COVID-19 cases by 19^th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19^th to March 18^th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran’s I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.     

Characteristics of SARS-CoV-2 transmission in a medium-sized city with traditional communities during the early COVID-19 epidemic in China

The nationwide COVID-19 epidemic ended in 2020, a few months after its outbreak in Wuhan, China at the end of 2019. Most COVID-19 cases occurred in Hubei Province, with a few local outbreaks in other provinces of China. A few studies have reported the early SARS-CoV-2 epidemics in several large cities or provinces of China. However, information regarding the early epidemics in small and medium-sized cities, where there are still traditionally large families and community culture is more strongly maintained and thus, transmission profiles may differ, is limited. In this study, we characterized 60 newly sequenced SARS-CoV-2 genomes from Anyang as a representative of small and medium-sized Chinese cities, compared them with more than 400 reference genomes from the early outbreak, and studied the SARS-CoV-2 transmission profiles. Genomic epidemiology revealed multiple SARS-CoV-2 introductions in Anyang and a large-scale expansion of the epidemic because of the large family size. Moreover, our study revealed two transmission patterns in a single outbreak, which were attributed to different social activities. We observed the complete dynamic process of single-nucleotide polymorphism development during community transmission and found that intrahost variant analysis was an effective approach to studying cluster infections. In summary, our study provided new SARS-CoV-2 transmission profiles representative of small and medium-sized Chinese cities as well as information on the evolution of SARS-CoV-2 strains during the early COVID-19 epidemic in China.     

Superposition of COVID-19 waves,anticipating a sustained wave,and lessons for the future

The 2019 coronavirus (COVID-19), also known as SARS-CoV-2, is highly pathogenic and virulent, and it spreads very quickly through human-to-human contact. In response to the growing number of cases, governments across the spectrum of affected countries have adopted different strategies in implementing control measures, in a hope to reduce the number of new cases. However, 5 months after the first confirmed case, countries like the United States of America (US) seems to be heading towards a trajectory that indicates a health care crisis. This is in stark contrast to the downward trajectory in Europe, China, and elsewhere in Asia, where the number of new cases has seen a decline ahead of an anticipated second wave. A data-driven approach reveals three key strategies in tackling COVID-19. Our work here has definitively evaluated these strategies and serves as a warning to the US, and more importantly, a guide for tackling future pandemics. Also see the video abstract here https://youtu.be/gPkCi2_7tWo     

基于DPSIR模型的森林生态安全时空间变化研究——以浙江省79个县区为例

森林生态安全对于维持生物多样性、促进经济可持续发展、保障人类生存具有重要影响,本文主要探讨森林生态安全的影响因素以及森林生态安全指数时间与空间变化情况,以期为提高浙江省森林生态安全状况提供理论参考。基于DPSIR(驱动力-压力-状态-影响-响应,Driving force-Pressure-State-Impact-Responses)模型,运用专家法、熵权法等确定浙江省森林生态安全指数,从县区角度对浙江省2000—2015年森林生态安全指数进行计算与分析,并通过绘制森林生态安全指数空间分布图观察森林生态安全空间变化状况。研究结果表明:(1)响应指标权重最大,政府政策作用显著,财政支持效果明显。(2)浙江省森林生态安全等级为较安全,森林生态安全指数分布在0.4—0.5之间,呈现波动式上升趋势。(3)状态指标的影响最明显,其次是压力指标、影响力指标、驱动力指标与响应指标;天然林面积减少是造成状态指标作用下降的主要原因;响应指标受其他指标的相互作用,上升趋势明显;经济发展一定程度上抑制了浙江省森林生态安全环境的改善与指数的提高。(4)浙江省森林生态安全指数地区分布不均匀,西南部地区森林生态...     

Genetic tracing of HCoV-19 for the re-emerging outbreak of COVID-19 in Beijing,China

The ongoing pandemic of coronavirus disease 2019(COVID-19)caused by a novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2,also named as 2019-nCoV or HCoV-19)poses an unprecedented threat to public health(Zhu et al.,2020;Wang et al.,2020;Jiang et al.,2020).The novel HCoV-19 virus has rapidly spread into multiple countries across the world since it was first reported in December 2019.The World Health Organization(WHO)declared COVID-19 as a pandemic on 11th March 2020.As of 4th July,over 10 million confirmed COVID-19 cases have been reported in over 200 countries/regions with more than 0.5 million deaths,including 85,287 documented cases and 4,648 deaths in China(WHO,2020a).