Machine Learning Applications in Disease Surveillance

Abstract

The semantic web provides a framework that allows data to be shared and reused across different applications and enterprises. There are a lot of data stores available all over the internet that contain extensive data and there is a wide range of possibilities when it comes to the applications and systems that can be developed with access to these data stores. One of these applications is disease surveillance. This essentially involves the collection, analysis, and interpretation of data to draw inferences regarding the outbreak and spread diseases as well as the efficiency of the preventive or control measures employed in these cases. In this paper, we would be looking at data gotten from Google Trends and showing the correlation between the amount of Google searches of a disease in a region and the cases or incidences in that region.