Probability Test for Ebola Infection
https://blogs.cornell.edu/info2040/2014/09/17/why-the-ebola-virus-is-out-of-control/comment-page-1/
http://www.globalresearch.ca/genetic-strains-of-ebola-that-have-never-been-seen-before-media-lies-and-us-government-coverup/5406603
Several students have drawn upon the topic of Ebola since its outbreak and transmission is getting more and more uncontrollable. They mostly probe into the social impact of global networks on its rapid spreading speed, using the structural features of the social networks to reveal the virus’s geological coverage. Learning Bayes Theorem this week, I attempt to approach the virus through its statistical aspect.
The article published on the global research website discusses the credibility of negative result for Ebola infection test. For its main part, it questions whether people should feel relieved by the result of a negative rate as low as 1%. In fact it might be contaminated by other factors. It argues that the false negative rate must be combined with the “prior probability”, which is estimated on the basis of all the potential contaminating factors. These are factors such as a symptoms, possible exposures, and geographical location. Therefore, if the prior probability of affliction is high enough in some cases, people should still be alerted even if the risk of false negative tests is extremely low. This scientific analysis on the Ebola infection rates provides some insights into the evaluation of test results, but its ability to impose any pragmatic effects on people’s precautionary measures seem relatively weak.
In general, this article utilizes Bayes Theorem to support his arguments and emphasizes the idea of “prior possibility”. His argument that the credibility of Ebola infection rates is highly conditional on pre-existing factors echoes the topic covered in class. Specially, Bayes Rule was studied in class because of its implication on the Cascades Effect, which says people make choices depending on the preceding events they see and usually follow the crowds. This assumption similarly calls upon the idea of conditionality. It is thus shown here that this concept of “prior possibility”, based on the same probability model, can be applied in different aspects of social lives.