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。     

Had COVID-19 spread in the community before the first confirmed case in Nagasaki,Japan?

This retrospective study evaluated stored nasopharyngeal swab samples from Japanese patients with influenza-like illness during the 2019/2020 season. We aimed to determine whether COVID-19 had spread in the community before the first confirmed case. The period of influenza season during 2019/2020 in Nagasaki was shorter than in previous influenza seasons. When the first COVID-19 case was reported in Nagasaki prefecture, the number of influenza cases were very low. No positive results for SARS-CoV-2 were detected in 182 samples that were obtained from adult outpatients. Our results revealed no large-scale spread of COVID-19 in the community before the first confirmed case.     

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).     

Mass gathering events and the spread of infectious diseases: Evidence from the early growth phase of COVID-19

This paper studies the impact on reported coronavirus 2019 (COVID-19) cases and deaths in Spain resulting from large mass gatherings that occurred from March 6 to March 8, 2020. To study these outcomes, the geographic differences in the planned pre-pandemic major events that took place on these dates were exploited, which is a quasi-random source of variation for identification purposes. We collected daily and detailed information about the number of attendees at football (soccer) and basketball matches in addition to individuals participating in the Women’s Day marches across Spain, which we merged with daily data on reported COVID-19 cases and deaths at the provincial level. Our results reveal evidence of non-negligible COVID-19 cases related to the differences in the percentage of attendees at these major events from March 6 to March 8. In a typical province, approximately 31% of the average daily reported COVID-19 cases per 100,000 inhabitants between mid-March and early April 2020 can be explained by the participation rate in those major events. A back-of-the-envelope calculation suggests that this implies almost five million euros (169,000 euros/day) of additional economic cost in the health system of a typical province with one million inhabitants in the period under consideration. Several mechanisms behind the spread of COVID-19 are also examined.     

Measures,Gaps, and Mitigation Strategies in Bangladesh’s COVID-19 Response

EcoHealth - The Coronavirus Disease 2019 (COVID-19) spread rapidly from China to most other countries around the world in early 2020 killing millions of people. To prevent virus spread, world...     

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.     

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.     

Contribution of traditional Chinese medicine to the treatment of COVID-19

COVID-19 as an epidemic disease has spread across the planet since December 2019. The somber situation reminds each country to take actions in preventing the spreading of the virus. China as one of the early affected countries has been fighting against the novel coronavirus with the achievements of nearly 80,000 cured confirmed patients. Traditional Chinese medicine (TCM) has made contributions to the treatment of COVID-19 because of its efficacy and comprehensive therapeutic theory. In this commentary, the advantage, etiology and mechanism of TCM therapy were discussed in the aspect of its functions in reducing the harms brought by COVID-19 to human beings.     

S2 subunit of SARS-nCoV-2 interacts with tumor suppressor protein p53 and BRCA: an in silico study

Novel coronavirus disease 2019 (COVID-19) is the biggest threat to human being globally. The first case was identified in a patient with flu symptoms along with severe acute respiratory syndrome in Wuhan, China in December 2019 and now it has spread in more than 200 countries. COVID-19 is more lethal in the elderly and people with an underlying condition such as asthma, cancer, diabetes. Here we performed bioinformatic analysis to investigate the interaction of S2 subunit protein of SARS-nCoV-2 of novel coronavirus with tumor suppressor proteins p53 and BRCA-1/2. In this short communication we report the interaction between S2 subunit proteins with tumor suppressor proteins for the first time. This preliminary result will open up a new direction to investigate the effect of a novel coronavirus in cancer patients.     

Transmission dynamics and control of COVID-19 in Chile,March-October, 2020

Since the detection of the first case of COVID-19 in Chile on March 3^rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2^nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile’s incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2^nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7^th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.     

Assessing the effectiveness of mandatory outdoor mask policy: The natural experiment of Campania

Face masks are possibly the main symbol of the COVID-19 pandemic. Once rarely used in Western countries, in the last two years they have become an object it is impossible to leave one’s home without. Italy made their use a legal requirement, even outdoors, from late 2020 to early 2022. The effectiveness of this policy in reducing COVID-19 cases has been widely debated. The recent cancellation of their mandatory use in Italy offers an interesting setting in which to test its impact, since one Italian region (Campania) extended the restriction for a further three weeks. We aim to shed some light on the real-world impact of mandatory use of face masks outdoors, identifying the effect of this policy on the spread of COVID-19. By means of a quantitative analysis, employing a synthetic control method approach, we find that Campania had statistically the same number of cases as its synthetic counterfactual, built from a donor pool formed from the other Italian provinces. Hence, results suggest that while it imposes a burden on the public, the use of face masks outdoors is not correlated with a decrease in the number of COVID-19 cases.     

An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020–2021

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with R_t<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.     

The Impact of COVID-19 On Comorbidities: A Review Of Recent Updates For Combating It

Coronavirus disease is caused by the SARS-CoV-2 virus. The virus first appeared in Wuhan (China) in December 2019 and has spread globally. Till now, it affected 269 million people with 5.3 million deaths in 224 countries and territories. With the emergence of variants like Omicron, the COVID-19 cases grew exponentially, with thousands of deaths. The general symptoms of COVID-19 include fever, sore throat, cough, lung infections, and, in severe cases, acute respiratory distress syndrome, sepsis, and death. SARS-CoV-2 predominantly affects the lung, but it can also affect other organs such as the brain, heart, and gastrointestinal system. It is observed that 75 % of hospitalized COVID-19 patients have at least one COVID-19 associated comorbidity. The most common reported comorbidities are hypertension, NDs, diabetes, cancer, endothelial dysfunction, and CVDs. Moreover, older and pre-existing polypharmacy patients have worsened COVID-19 associated complications. SARS-CoV-2 also results in the hypercoagulability issues like gangrene, stroke, pulmonary embolism, and other associated complications. This review aims to provide the latest information on the impact of the COVID-19 on pre-existing comorbidities such as CVDs, NDs, COPD, and other complications. This review will help us to understand the current scenario of COVID-19 and comorbidities; thus, it will play an important role in the management and decision-making efforts to tackle such complications.     

The effect of weekend curfews on epidemics: a Monte Carlo simulation

The ongoing COVID-19 pandemic is being responded with various methods, applying vaccines, experimental treatment options, total lockdowns or partial curfews. Weekend curfews are among the methods for reducing the number of infected persons, and this method is practically applied in some countries such as Turkey. In this study, the effect of weekend curfews on reducing the spread of a contagious disease, such as COVID-19, is modeled using a Monte Carlo algorithm with a hybrid lattice model. In the simulation setup, a fictional country with three towns and 26,610 citizens were used as a model. Results indicate that applying a weekend curfew reduces the ratio of ill cases from 0.23 to 0.15. The results also show that applying personal precautions such as social distancing is important for reducing the number of cases and deaths. If the probability of disease spread can be reduced to 0.1, in that case, the death ratio can be minimized down to 0.     

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     

Spatially resolved simulations of the spread of COVID-19 in three European countries

We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governmental interventions and changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country. Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle income countries which remain understudied.     

The effect of income support programs on job search,workplace mobility and COVID-19: International evidence

This study examines the effect of Income Support Programs (ISPs) on job search effort, work- place mobility, COVID-19 cases, and mortality growth rates. To identify ISPs’ causal effect, I use the variation in their introductions’ timing across countries and implement a difference-in-difference and multi-event analysis method. I find that ISPs led to a 4.4–8.29 percentage points reduction in workplace mobility and a 6.6–11.6 percentage points reduction in job search effort levels. They also caused a 21.8–47.7 and 17.1–29.7 percentage points reduction in the COVID-19 case growth rate and COVID-19 mortality growth rates, respectively. Using the event analysis estimates, I simulated the counterfactual job search effort, workplace mobility, and the number of COVID-19 cases and mortality without income support programs. The average global job search effort and workplace mobility without ISPs would have been 11.12 and 9.26 percent higher than the observed mean job search effort and workplace mobility. However, these would have come at the cost of 3.69 million and 166, 690 additional COVID-19 cases and mortality than the cases and deaths registered by May 15th.     

浙江省新型冠状病毒疫情空间扩散特征及影响因子探究

2020年新型冠状病毒肺炎(COVID-19)在全球200多个国家蔓延,对世界公共卫生环境、民众生命健康安全与社会经济带来了巨大影响。为了解病毒传播的时空特征及驱动因子对新型冠状病毒(SARS-CoV-2)防控的重要意义,本研究基于2020年1月21日~2020年3月10日浙江省各县、市(区)的COVID-19疫情数据,按照病例数的变化情况将疫情时期分为迅速增长期、高位回落期、控制减少期和稳定期,发现省外输入病例主要确诊于迅速增长期和高位回落期,省内扩散病例主要确诊于控制减少期和稳定期。利用ArcGIS软件进行数据可视化,总结了两种病例类型在浙江省各县、市(区)分布的时空变化特征,发现浙江省新冠肺炎疫情在温州最为严重,其次是杭州、宁波和台州;并利用地理探测器方法探究了各因子的作用,发现社会经济因子和城市建设因子影响较大,自然环境因子影响较小。     

新型冠状病毒疫苗研究策略分析

新型冠状病毒(SARS-CoV-2)是一种可引起人新型冠状病毒肺炎(COVID-19)的新发呼吸道病原体,与重症急性呼吸道综合症冠状病毒(SARS-CoV)和中东呼吸综合征冠状病毒(MERS-CoV)同属于β-冠状病毒,具有较高的传染性和一定的致死率。2019年12月在我国武汉被发现,随后蔓延到我国大部分省份,给我国人民健康和经济发展造成巨大损失。疫苗接种是预防和控制传染病的常规和有效手段,国内外多个机构已启动COVID-19疫苗研究工作。文中基于SARS和MERS疫苗研究的经验和教训,对COVID-19疫苗的研究策略和需要注意的关键问题进行了阐述,为相关研究人员提供参考。