Considered an essential activity, the production coming from slaughterhouses did not stop its activities in the midst of the new coronavirus pandemics (SARS COV-19) in Brazil. South and Southeast regions of ParÃ¡ are examples of how such activity is associated to the worldwide production circuit. Nevertheless, the spatial circulation of the supplies coming from the slaughterhouses becomes one of the covid-19 transmission vectors, due to the intense mobility of workers in the sector.
The coronavirus pandemic and the associated containment measures are likely to have serious effects on housing. In the short term, the German Federal Government has reacted with temporary exemptions for subject-oriented instruments as well as for rental and lending regulations. However, it has become apparent that further temporary regulations are needed within the scope of social security of housing. In the course of the recession, pronounced price declines on the housing markets are to be expected due to the great relevance of demandside developments.
目的： 评估全球新型冠状病毒肺炎（COVID-19）疫情对我国的输入风险。 方法： 基于收集的疫情数据（各国家每日累计确诊病例数、境外输入病例累计确诊病例数）、人口学数据（各国人口密度、人口数）、旅客潜在来源群体信息（华侨华人常住人口数、在外中国留学生数、海外务工人员数、来华留学生数、航班旅客数估计）和全球健康安全指数（GHS）等信息，进行近期（2月1日-4月25日）和未来（4月26日-）风险分析及预测，构建输入风险得分。 结果： 各国境外输入病例数、累计确诊数、罹患率、华侨华人数、境外留学生数、来华留学生数、航班乘客数和GHS变量间有较强的正相关性。近期风险分析中，俄罗斯输入病例明显较高，英国、美国、法国、西班牙次之。在未来风险预测中，通过各国罹患率指数和平均每日入境乘客数估计值两项信息，评估美国、新加坡等44个国家为未来潜在高风险国家。 结论： 通过COVID-19疫情各国家输入风险评估，可以识别近期及未来的高风险区域，为加强疫情防控，为最终战胜疫情提高帮助。.Objective: To assess the risk of COVID-19 foreign imports cases to China.
Objective: To investigate the clinical characteristics of coronavirus disease 2019 (COVID-19) imported from abroad in Pudong Hospital Affiliated to Fudan University so as to provide evidence for epidemic prevention and control of COVID-19. Methods: We extracted data regarding 77 patients with laboratory-confirmed COVID-19 in Pudong Hospital from Mar 14 to Jul 3, 2020.We then collected and analyzed their epidemic history, blood routine, C-reactive protein (CRP) and chest CT.
Singapore earned early plaudits for its management of the COVID-19 pandemic. However, the government’s failure to pay attention to the health of the country’s sizable foreign worker population and its refusal to heed the repeated warnings from infectious disease experts and advocacy groups has led to a major outbreak in cramped dormitories and a lockdown of the entire country.
Beginning in January 2020, the world has struggled to contain COVID-19 pandemic. Initially lauded as the “gold standard” for containment of the pandemic, Singapore was suddenly confronted with a massive outbreak of infection in the migrant worker dormitories. To date, migrant workers accounted for 95 percent of the almost 60,000 infected, while outside the dormitories infection was relatively well-contained and overall extremely low fatalities.
The advent of the new corona virus hinders the fragile welfare of migrant workers. Those economic sectors with a large migrant workforce appear to be those hit hardest during the lockdown, resulting in surge in migrant unemployment and a plunge in the volume of remittances. This has become yet another factor putting pressure on the gross domestic product (GDP) growth, balance of payments, and budgets of countries that are net remittance recipients, while also triggering rising poverty levels.
The article deals with the contemporary labor migrations from Bosnia and Herzegovina to Slovenia and the other countries of European Union, specifically during the period of the COVID-19 pandemic. On the basis of fieldwork among the participants in these migrations, it seeks to identify the specifics of circumstances and situations that arose suddenly with the closure of political borders and the demands of social di-stancing. In these circumstances, we supposed that labor migrants found themselves to be a particularly vulnerable group of population.
[No abstract available]
This article reviews how Singapore has responded to the COVID-19 pandemic, from late-January to early May, 2020, through the three-phase approach to “learning”: in-between learning, trial-and-error learning, and contingency learning.