Univerza na Primorskem Fakulteta za matematiko, naravoslovje in informacijske tehnologije

petek, 27. marec 2020 Online seminar: András BÓTA: Network-based framework for estimating the risk factors of epidemic spreading

V ponedeljek, 30. marca 2020, bo ob 16.00 uri prek spletnih orodij na daljavo izvedeno predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 30. marec 2020 ob 16.00 na daljavo


András Bóta finished his postgraduate studies in Computer Science at the University of Szeged under the supervision of Dr. Miklós Krész and Dr. András Pluhár in 2015, then worked as a Research Associate at the University of New South Wales with Dr. Lauren Gardner for two years. Following that, he worked as a postdoctoral fellow at Umeå University. His main research interests lie in the fields of complex network analysis and artificial intelligence.

NASLOV: Network-based framework for estimating the risk factors of epidemic spreading


Network-based adaptations of traditional compartmental infection models such as SIR or SEIR can be used to model the spreading of diseases between cities, countries or other geographical regions. One of the common challenges arising in these applications is the lack of available transmission probabilities between these geographical units. The task of inverse infection is the systematic estimation of these values. Several methods have been proposed recently for solving this task. One of them is the Generalized Inverse Infection Model (GIIM) [1]. GIIM offers a large amount of modeling flexibility and allows transmission probabilities to be defined as a function of known attributes, or risk factors in an epidemic context. In this presentation we will see how GIIM works in two specific real-life outbreaks.
Both examples are embedded in a geographical and temporal setting. The first one considers the 2015-2016 Zika virus outbreak in the Americas, where the countries and overseas territories of the continent form the nodes of the network and air travel routes define the links [2]. The second application models the 2009 H1N1 outbreak between the municipalities of Sweden, with links between the municipalities indicating frequent travel routes.
Our first goal in both of these studies is to discover the relationship between the transmission risk between geographical units and a variety of travel, environmental, meteorological and socioeconomic risk factors. Our second goal is to estimate the risk of exportation and importation of the diseases for the territories involved in these studies. We will show that the GIIM model is able to identify the most critical risk factors in both scenarios, and in the influenza study, it is able make predictions about future outbreaks with good accuracy.

1. A. Bóta, L. M. Gardner: A generalized framework for the estimation of edge infection probabilities. arXiv:1706.07532 (2017)
2. L. M. Gardner, A. Bóta, N. D. Grubaugh, K. Gangavarapu, M. U. G. Kramer: Inferring the risk factors behind the geographical spread and transmission of Zika in the Americas.PLoS Neglected Tropical Diseases. http://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0006194


Predavanje bo potekalo v angleškem jeziku prek spletnega orodja Zoom.

Do predavanja dostopate tako, da se povežete prek sledeče povezave: https://zoom.us/j/297328207