Month: April 2021

Computational Epidemiology at the time of COVID-19 by Alessandro Vespignani


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Colloquium Virtual Complexity at C3-UNAM
Universities for Science Consortium

“Computational Epidemiology at the time of COVID-19”
Alessandro Vespignani
Network Science Institute at Northeastern University

Abstract:
The data science revolution is finally enabling the development of large-scale data-driven models that provide real- or near-real-time forecasts and risk analysis for infectious disease threats. These models also provide rationales and quantitative analysis to support policy-making decisions and intervention plans. At the same time, the non-incremental advance of the field presents a broad range of challenges: algorithmic (multiscale constitutive equations, scalability, parallelization), real-time integration of novel digital data streams (social networks, participatory platform, human mobility etc.). I will review and discuss recent results and challenges in the area, and focus on ongoing work aimed at responding to the COVID-19 pandemic.

Short Bio:
Alessandro Vespignani is the Director of the Network Science Institute and Sternberg Family Distinguished University Professor at Northeastern University. He is a professor with interdisciplinary appointments in the College of Computer and Information Science, College of Science, and the Bouvé College of Health Sciences. Dr. Vespignani’s work focuses on statistical and numerical simulation methods to model spreading phenomena, including the realistic and data-driven computational modeling of biological, social, and technological systems. For several years his work has focused on the spreading of infectious diseases, working closely with the CDC and the WHO.

Watch at: www.youtube.com

Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes

Brandon Alexander , Alexandra Pushkar and Michelle Girvan

Journal of the Royal Society Interface Volume 18 Issue 177

We study a simplified model of gene regulatory network evolution in which links (regulatory interactions) are added via various selection rules that are based on the structural and dynamical features of the network nodes (genes). Similar to well-studied models of ‘explosive’ percolation, in our approach, links are selectively added so as to delay the transition to large-scale damage propagation, i.e. to make the network robust to small perturbations of gene states. We find that when selection depends only on structure, evolved networks are resistant to widespread damage propagation, even without knowledge of individual gene propensities for becoming ‘damaged’. We also observe that networks evolved to avoid damage propagation tend towards disassortativity (i.e. directed links preferentially connect high degree ‘source’ genes to low degree ‘target’ genes and vice versa). We compare our simulations to reconstructed gene regulatory networks for several different species, with genes and links added over evolutionary time, and we find a similar bias towards disassortativity in the reconstructed networks.

Read the full article at: royalsocietypublishing.org

“Too Lazy”: Episode 2 with Roberta Sinatra –

Today is Roberta Sinatra day on #TooLazyPod!! Roberta is a physicist, an expert on science of success, and all-round fantastic person. In the podcast, we talks about her recent paper “Success and luck in creative careers”.

Full episode at: sunelehmann.com