Application of Machine Learning for the predictions of Epidemic Diseases

An Epidemic is the rapid spread of infectious diseases to a large number of people in a given population within a short period of time. For example, AIDS spread widely throughout the world in the 1980’s, and since that time it has taken the lives of more than 25 million people. Ebola is also an example of an epidemic.

Global epidemic data and statistics

Epidemics of infectious diseases generally depends on several factors which also depends upon the population of that area hence the epidemic may restrict to only one location due to these factors. If the epidemic spreads to other countries then it is termed as Pandemic. There are several changes that occurs in the pathogens that may trigger the epidemic like Virulence and changes to the host susceptibility to the infectious agent.

Predicting these epidemics (Infectious Diseases) can save thousands of lives and can be very valuable to the society. With the fast advancements of Big Data and Machine Learning these problems can be solved. With accurate analysis of data can help in the early disease detection and hence better health care can be given. With the huge computational power now a days it would rather take less time than it should’ve taken 10 years ago.

The geographical factors, Climate and Population distribution should be taken into consideration for the Machine Learning prediction Model. Given an area where an epidemic outbreak has occurred, the Machine Learning model would be able to predict the next epidemic outbreak prone area. This ML model would be beneficial to different health organizations so that they could have a track of these epidemic diseases and stop the widespread. The epidemic of the infectious diseases depends on the ecology of the host.

The Data Sources may include weather report of an area, population density of an area, Economic Profiles etc. The Exploratory Data Analysis can be done on the data to get the insight of the data and the important features. The modelling can be done for a particular location and epidemic diseases because the conditions that governs the outbreak of the different epidemics is different hence models can be trained according to the disease.