Transforming Native Epidemic Models by Using the Machine Learning Approach

Abstract

Since the dawn of civilization, disease outbreaks have always been a threat to humanity. Despite ongoing efforts to improve health care around the world, epidemic outbreaks remain a major health concern. The issue with an epidemic is that pathogen dissemination relies on numerous socio-environmental variables that are changing all the time, rendering epidemic models obsolete. A successful response to such outbreaks requires prompt action based on a variety of factors such as the influence of environmental conditions, incubation period, infection rate, etc. Conventional epidemic models often produce results that are essentially hand-coded to respond to inputs that way. In the event of an unforeseen circumstance, these models do not adapt well because they lack independent discernment. Furthermore, due to the variety of data-forms, issues, and strategies used to address these models- collecting, visualizing, and interpreting epidemic data is becoming more challenging. We hope to survey existing methodologies used to fight epidemics and intend to propose a data-driven approach by breaking down existing epidemic models, which will learn to react to a specific outcome and adapt to new data and varying variables. We conclude by emphasizing how this strategy can be optimized and adapted for potential epidemics, allowing governments and organizations to plan for the next outbreak and educate citizens against high-risk activities.