Semantic Framework for Practicing Data Science in Public Health Organizations during the Covid-19 Pandemics

Abstract

This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the COVID-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, non-government organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal uses the sentiment analysis of Twitter data relevant to COVID-19 and classification of tweets with machine learning