Predictive Models for Ebola using Machine Learning Algorithms

Identifying and tracking individuals affected by this virus in densely
populated areas is a unique and an urgent challenge in the public health sector.
Currently, mapping the spread of the Ebola virus is done manually, however with
the help of social contact networks we can model dynamic graphs and predictive
diffusion models of Ebola virus based on the impact on either a specific person or
a specific community.
With the help of this model, we can make more precise forward
predictions of the disease propagations and to identify possibly infected
individuals which will help perform trace – back analysis to locate the possible
source of infection for a social group. This model will visualize and identify the
families and tightly connected social groups who have had contact with an Ebola
patient and is a proactive approach to reduce the risk of exposure of Ebola
spread within a community or geographic location.