Automated News Screening for Emerging Infectious Disease Risk Identification in the Lake Victoria Basin

By Zorka Mwendwa Rakonczay

Recent years have shown the importance of being able to predict and track outbreaks of infectious diseases with the aim of preventing major incidents that can cause vast disruptions to society.
Predicting disease emergence can be aided by the study of the drivers and trends of infectious disease emergence. This thesis describes a proof of concept for the use of automated news screening as a tool to use in as part of an early warning system to identify the emergence of infectious disease, using the Lake Victoria Basin as the subject for this exploration.
The method described in thesis uses the open-source text mining and data analysis tools of KNIME and R’s tidyGraph, iGraph and visNetwork packages to breakdown and examine digital news articles to create an easily visualizable summary of news articles relevant to news on a topic of choice, in this thesis, disease outbreaks.


KNIME workflow:

EMM Kenya nov.11.2022.3_2.knwf

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