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美国NovaBios埃博拉病毒检测试剂盒（胶体金法）

广州健仑生物科技有限公司

本司长期供应埃博拉病毒检测试剂盒，其主要品牌包括美国NovaBios、广州创仑等CDC使用的进口产品，试剂盒的实验方法包括胶体金方法、ELISA方法、PCR方法等。

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埃博拉病毒IgM、IgG、ELISA检测试剂、埃博拉快速检测试剂盒、

埃博拉病毒核酸检测试剂盒（荧光探针PCR）

西非工作、旅游埃博拉检测试剂盒

美国CDC使用的埃博拉诊断试剂——美国的NovaBios

美国NovaBios 埃博拉病毒检测试剂盒（胶体金法）

【埃博拉简介】

埃博拉（Ebola virus）又译作伊波拉病毒。是一种十分罕见的病毒，1976年在苏丹南部和刚果（金）（旧称扎伊尔）的埃博拉河地区发现它的存在后，引起医学界的广泛关注和重视，“埃博拉”由此而得名。是一个用来称呼一群属于纤维病毒科埃博拉病毒属下数种病毒的通用术语。是一种能引起人类和灵长类动物产生埃博拉出血热的烈性传染病病毒，有很高的死亡率，在50%至90%之间，致死原因主要为中风、心肌梗塞、低血容量休克或多发性器官衰竭。

埃博拉出血热（EBHF）是由一种丝状病毒感染导致的急性出血性、动物源性传染病。1976年，埃博拉出血热在非洲的苏丹和扎伊尔暴发，病死率高达50% ～ 90% 。因该病始发于扎伊尔北部的埃博拉河流，并在该区域严重流行，故命名为埃博拉病毒，其形态学、致病性等与马尔堡病毒相似，但免疫原性有所区别。

【产品介绍】

该产品是世界卫生组织（WHO）*个批准用于埃博拉病毒检测的诊断试剂卡。不需要借助其他实验仪器设备，只需要采取几滴血清、血浆、血液样品，既可以检测，并在15分钟内就可以得知结果是否感染埃博拉病毒。该产品具有灵敏度高、操作方便、实验时间短等特点。

美国NovaBios

对于引入风险的估计，zui核心的问题便是如何通过动态模型或是统计模型将风险量化。这里介绍一种比较直观简介的估计方式，首先把引入风险分为相对引入风险(relative import risk)和引入风险(absolute import risk)。不妨假设X为疫情爆发区域的某个机场，而Y则是世界上任意一块区域，那么我们可以通过条件概率的形式来定义相对引入风险，即P(Y|X)。而对于引入风险我们则可以通过联合概率来定义，即P(X,Y)=P(Y|X)P(X)，这里需要注意的是P(X)往往远小于P(Y|X)，因此P(X,Y)也会远远小于P(Y|X)。在实际应用中，相对风险较风险而言更有价值，其主要原因在于P(X)的估计在大部分时间都难以实现，事实上P(X)的估计需要依赖于大量的参数来描述X所在地区的各类因素，而风险的估计却恰恰依赖于P(X)。与此相反，相对风险的计算则仅仅需要各机场人口流动的数据即可，即无需考虑地区本身的相关因素。
2. 由移动通信数据预测疫情
　　埃博拉病毒在西非的爆发引起了*的关注，人们纷纷把注意力头像了机场等引起人口流动的公共场所，正如上一节所说，机场数据的确对于流行病学家等研究者们而言具有*的分析价值，但除此之外，基于手机移动端产生的数据同样应用潜力。
　　用户每次利用手机通话的过程中都会同时产生相应的通话记录数据，其中自然也包含了、通话时间以及大致的通信地点等重要信息。对于运营商而言，这些数据可以为各地基站的部署提供参考从而提升通信网络。另一方面对于城市规划者而言则可以基于该数据判断相关地点是否需要拓展相应的公共交通设施。
　　然而除了上述这些相对常用的应用外，在流行病学的应用却更令人期待，更激动人心。事实上到目前为止一般情况下对于疾病扩散建模的常用方法依然是基于人口普查的数据以及相关调查。然而对于通信记录数据，人们却可以得到实时更新的数据，也就是说在实际应用中无需估计某地区的人口是否会迁移。同时幸运的是，在近几年中确实并不缺乏类似的成功案例。2009年在墨西哥爆发的猪流感，研究人员就曾利用通信数据监测公众对于政府发布的健康预警信息的反应。此后2010年随海地地震爆发的霍乱疫情，研究人员则同样基于手机通信数据建模并给出了zui需要援助地点的*估计。
　　在对于埃博拉病毒研究的实际操作中却更为复杂，一个zui主要的原因在于西非大部分民众并没有手机或者其他通信设备。不过尽管如此，某种程度上它却依然优于基于陈旧数据的统计分析。事实上研究人员如果可以从一个传染病爆发的地域追踪到人口的流动，那么对于下一个zui有可能爆发传染病的地点就会有一个比较有效的估计和预测，从而可以提前展开合理有效的资源配置。遗憾的是，尽管很多相关的机构都做了很大程度的努力，但出于隐私等问题的考虑，电信运营商依然不允许研究人员使用这部分数据。

美国NovaBios

我司还提供其它进口或国产试剂盒：登革热、疟疾、乙脑、寨卡、黄热病、基孔肯雅热、克锥虫病、违禁品滥用、肺炎球菌、军团菌等试剂盒以及日本生研细菌分型诊断血清、德国SiFin诊断血清、丹麦SSI诊断血清等产品。

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【公司名称】 广州健仑生物科技有限公司
【市场部】    杨永汉

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【腾讯  】 2042552662
【公司地址】 广州清华科技园创新基地番禺石楼镇创启路63号二期2幢101-103室

For the introduction of risk estimates, the core issue is how to quantify the risk through dynamic models or statistical models. Here is a more intuitive introduction to the introduction of the estimation method, first of all into the risk of relative risk and relative risk (absolute import risk). It is assumed that X is an airport in the outbreak area, and Y is any piece of the world, then we can define the relative introduction risk through the conditional probability form, ie P (Y | X). (X, Y) = P (Y | X) P (X), it is important to note that P (X) is often much smaller than P (Y | X) for the absolute introduction of the risk that we can be defined by the joint probability ), So P (X, Y) will be much smaller than P (Y | X). In practice, the relative risk is more valuable than the absolute risk, the main reason is that P (X) estimates are difficult to achieve for most of the time, in fact P (X) estimates need to rely on a large number of parameters to describe X where the various factors, while the absolute risk estimates are dependent on P (X). In contrast, the relative risk calculation requires only the data of the airport's population flows, that is, without regard to the relevant factors of the region itself.
2. Forecast the epidemic by communication data
The outbreak of the Ebola virus in West Africa has aroused worldwide attention, and there has been a public eye that has attracted attention to the airport and so on. As mentioned in the previous section, the airport data is indeed for epidemiologists and other researchers In terms of a very high analytical value, but in addition, based on the phone data generated by the same great potential.
The user each time using the phone call process will also produce the corresponding call record data, which naturally contains the phone number, talk time and approximate communication location and other important information. For operators, these data can provide a reference for the deployment of base stations to enhance the communication network. On the other hand, for urban planners, it is possible to determine whether the relevant sites need to expand the corresponding public transport facilities based on the data.
However, in addition to these relatively common applications, the epidemiology of the application is more exciting and more exciting. In fact, the usual method of modeling the spread of disease in general is still based on census data and related surveys. However, for communication record data, people can get real-time updated data, that is to say in practical applications do not need to estimate whether a region's population will migrate. Fortunay, in recent years, there is no lack of similar success stories. In 2009, swine flu broke out in Mexico, where researchers used communication data to monitor public response to government health warnings. Since 2010, with the Haiti earthquake outbreak of cholera epidemic, the researchers are also based on communication data modeling and gives the most needed assistance to the optimal estimate of the location.
In the actual operation of the Ebola virus research is more complex, one of the most important reason is that most people in West Africa and no phone or other communications equipment. But nevertheless, to some extent it is still better than statistic analysis based on obsolete data. In fact, if researchers can trace from the outbreak of an infectious disease to the flow of population, then the next most likely outbreak of infectious diseases, there will be a more effective location of the estimates and forecasts, which can be ahead of a reasonable and effective resources Configuration. Unfortunay, although many of the relevant agencies have done a lot of effort, but for privacy and other issues to consider, ecom operators still do not allow researchers to use this part of the data.