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On the use of clinical based infection data for pandemic case studies

Authors :
Maria Mazzitelli
Gabriel Gabriele
Pietro Hiram Guzzi
Patrizia Vizza
Mattia Prosperi
Pierangelo Veltri
Carlo Torti
Giuseppe Tradigo
Source :
BIBM, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

Epidemiological models are relevant to study and analyze clinical as well as environmental and behavioural data, useful to support health studies. The target is to perform epidemiological analysis producing fast and reliable data access useful to guide prevention and curing processes. This is currently true in pandemic emergency as the current Covid-19 context. Epidemiological models should support in the early identification of pandemic phenomena and in making available data set for studying more accurate drug-based strategy for vaccines or virus containment.In this contribution we present an epidemiology database which integrates different types of clinical data to support research, follow-up and patient monitoring. The idea starts from an hospital databases cooperation integration where virus available data have been integrated to support statistical based studies. Starting from an available database containing 5 years data of infection related viruses (such as HPC, hepatitis) and patient anonymous data, the proposed system provide an integrated data access able to (i) extracting data filtered by means of clinical hypothesis based on patient profiles, environment and drugs and (ii) allowing to build large scale geographical data mappings in order to study correlations among chronic infection diseases and their relations with upcoming pandemic phenomena. Even if the application is in its infancy, the application is relevant with high very important applications.

Details

Database :
OpenAIRE
Journal :
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Accession number :
edsair.doi.dedup.....26c9d5f0c87f9a8cf0eee3217d20b08f
Full Text :
https://doi.org/10.1109/bibm49941.2020.9313469