HEALTH BROKEN WOVEN POISSON SPHERES TO MANAGE DEADLY EBOLA INCIDENCES
DOI : 10.3844/ajidsp.2014.142.153
American Journal of Infectious Diseases
Volume 10, Issue 3
The deadly infectious Ebola incidences scare not only to the residents of western Africa but also to the travelers and medical professionals who treat the patients, as they became victims. Since 27 July until 13 August 2014 alone, about 2,127 Ebola cases occurred in just four Western African countries: Guinea, Liberia, Nigeria and Sierra Leone and more than 50% of them died. The mortality is extremely higher. No known medication exists. Though the virus is not airborne spreading, a contact with the patientâs fluids, tissues, or bodies is known to transmit Ebola virus. There had been three categories: Suspected, probable, or confirmed in the collection of Ebola incidences and deaths. Their data are quite informative if they are properly processed and it is exactly the aim of this article. For this purpose, the stochastic nature of the data is probed rationally. The Ebola incidences and deaths in each category exhibit a separate Poisson chance environment and yet, they are connected. Therefore, suitable Poisson models are developed for each category and are then woven together to analyse the entire pertinent data on Ebola incidences and deaths in those four countries. Pictures are worth the thousand words to comprehend non-trivial findings. Hence, innovatively the data analytic concepts for three-dimensional sphere for each country is developed and applied. By superimposing the four spheres (one for each country), this article points out the relative performance of the four countries with respect to the Ebola incidences and deaths together in each category. One country does better than others in one category but poorly in other two categories. A better performance by a country is a reflection of effective prevention and successful medical treatment of Ebola cases.
© 2014 Ramalingam Shanmugam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.