no code implementations • 13 Apr 2023 • Alexandra Blenkinsop, Lysandros Sofocleous, Francesco Di Lauro, Evangelia Georgia Kostaki, Ard van Sighem, Daniela Bezemer, Thijs van de Laar, Peter Reiss, Godelieve de Bree, Nikos Pantazis, Oliver Ratmann
In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection.
1 code implementation • 21 Feb 2022 • Francesco Di Lauro, Wasiur R. KhudaBukhsh, Istvan Z. Kiss, Eben Kenah, Max Jensen, Grzegorz A. Rempala
We present a new method for analyzing stochastic epidemic models under minimal assumptions.
no code implementations • 9 Nov 2021 • Tanja Zerenner, Francesco Di Lauro, Masoumeh Dashti, Luc Berthouze, Istvan Z. Kiss
To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model.
no code implementations • 10 May 2021 • Abhishek Tomy, Matteo Razzanelli, Francesco Di Lauro, Daniela Rus, Cosimo Della Santina
When an epidemic spreads into a population, it is often unpractical or impossible to have a continuous monitoring of all subjects involved.
no code implementations • 27 Sep 2020 • James Bell, Ginestra Bianconi, David Butler, Jon Crowcroft, Paul C. W Davies, Chris Hicks, Hyunju Kim, Istvan Z. Kiss, Francesco Di Lauro, Carsten Maple, Ayan Paul, Mikhail Prokopenko, Philip Tee, Sara I. Walker
On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc.
Physics and Society