How Government Can Use AI and ML to Identify Spreading Infectious Diseases
Year: 2018
Type: preprint
Abstract: Infectious diseases like Spanish Flu, TB, Ebola, SARS and influenza have been some of the leading causes of deaths around the world especially in low-income countries despite the progress made in medicine. The previous outbreak of Ebola, SARS and influenza around the world shows that these infectious diseases are have become a constant threat to human existence. Healthcare stakeholders and researchers have found innovative ways to support the health care sector especially efforts to identify the the spread of infectious diseases. The application of machine learning and the use of artificial intelligence is currently changing the way the spread of the diseases is addressed by modeling the course of the virus and also finding new strategies for containment. While governments around the world collaborate with local officials and healthcare professionals to monitor, respond to and slow the spread of infectious diseases, health experts utilize predictive modeling to improve current intervention to avoid further infections. The development of mathematical tools allows scientists to better predict the spread of infectious diseases, consider each pathogen's specificities and define possible priorities for the delivery of vaccines. Big data analytics can be invaluable in responding to deadly outbreaks, as they can analyses massive quantities of information, like health information, trends of human behaviors, and environmental factors. This research aims at exploring potential applications of machine learning and artificial intelligence by governments in identifying the spread of infectious diseases. This section covers a variety of selective applications to explain how artificial intelligence and machine learning advances the control of infectious diseases like TB, influenza and Spanish Flu and how it allows health institutions to better address them.
Source: SSRN Electronic Journal
Author: Lakshmisri Surya
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3132407382