Big Data mining against infectious diseases

Big Data mining against infectious diseases HU
Physicists at the ELTE Faculty of Science have also joined the fight against emerging infectious diseases by developing an innovative cloud-based collaborative platform. The research group led by István Csabai, full professor at the Department of Physics of Complex Systems at ELTE Faculty of Sciences has been granted Horizon 2020 research funding.

The application for funding was jointly submitted by twenty internationally renowned research groups under the title VEO: Versatile emerging infectious disease Observatory. VEO combines the work of data scientists, technology experts, healthcare and academic epidemiologists, social scientists, and citizen scientists.

The project applies big data mining techniques for early detection of infectious disease threats driven by climate change and other factors. The objective of the professionals is to create a versatile forecasting and tracking system serving as an interactive observatory for evidence-based early warning and risk assessment of emerging infectious diseases and antimicrobial resistance – to help public health actors and researchers.

The time course of the spread of COVID-19 shown on a graph designed by Krisztián Papp (ELTE, data updated on 13 March)

The VEO data platform supports the mining, sharing, presentation, and analysis of traditional and novel biological data. It will allow data-intensive interdisciplinary collaboration of geographically distributed international research teams, including citizens interested in science. New solutions will also be developed to integrate high density data, such as genomics, into the VEO system promoting thus risk assessments.

The VEO system presents five complementary use case scenarios that reflect the major pathways of disease emergence,

demonstrating the effectiveness of this method, and taking into account the ethical, legal, and social implications. This is a promising approach for the continuous monitoring of environmental samples so that the dangerous pathogens could be detected before epidemics become widespread.

The work package supervised by István Csabai within the VEO project involves the development of an innovative cloud-based collaborative platform, and the integration of database techniques, machine learning solutions, and diverse tools for data analysis and modelling.

Cover image: Pixabay