Backbone reconstruction in temporal networks from epidemic data

Abstract

Many complex systems are characterized by time-varying patterns of interactions. These interactions comprise strong ties, driven by dyadic relationships, and weak ties, based on node-specific attributes. The interplay between strong and weak ties plays an important role on dynamical processes that could unfold on complex systems. However, seldom do we have access to precise information about the time-varying topology of interaction patterns. A particularly elusive question is to distinguish strong from weak ties, on the basis of the sole node dynamics. Building upon analytical results, we propose a statistically-principled algorithm to reconstruct the backbone of strong ties from data of a spreading process, consisting of the time series of individuals' states. Our method is numerically validated over a range of synthetic datasets, encapsulating salient features of real-world systems. Motivated by compelling evidence, we propose the integration of our algorithm in a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Through Monte Carlo simulations on synthetic networks and a real-world case study, we demonstrate the viability of our approach.

Stay with your community: Bridges between clusters trigger expansion of COVID-19

“…Our conclusion may differ from this result in that we showed that bridges between clusters contributed to the spreading, which seems to correspond to weak ties rather than to strong ties. However, these weak ties called bridges between clusters that we considered herein are not necessarily as transient as the weak ties defined by Surano et al in [33] and are particularly influential to various clusters, which is consistent with their findings.…”

“…By interpreting that frequent communication across bridges between communities accelerates the spread of infection, we can verify similar tendencies in a different setting. An algorithm for reconstructing the strong ties that are the invariant backbone structure of a network, distinguished from weak ties constructed from transient links in the dynamic network structure, from data on the individuals’ status in the spreading process [ 33 ], has been shown to be useful for a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Our conclusion may differ from this result in that we showed that bridges between clusters contributed to the spreading, which seems to correspond to weak ties rather than to strong ties.…”

An overview of epidemic models with phase transitions to absorbing states running on top of complex networks

“…But we try to mention just a few of many research lines. In the last decades, epidemic models have also been studied in hypergraphs [80], temporal networks [81][82][83], metapopulations [84,85] and also multiplex subtrates [86,87]. They analyzed, among other issues, epidemic spreading with awareness, social contagion, measures of epidemic control and how patterns of mobility affects the transmission of the disease.…”

Modeling and forecasting of epidemic spreading of the SARS-CoV-2 based in the nonlinear stochastic dynamics

“…[1][2][3][4][5][6][7][8][9] On the other hand, the use of models and mathematical methods for theoretical physicists to the study the spread of contagious diseases goes back a least to some works by Daniel Bernoulli in XVIII century on smallpox, [14] where in nowadays, many mathematical models have been proposed and studied for many different diseases. [10][11][12][13] Some diseases as the typhoid fever and also the COVID-19 are spread largely by carriers or individuals who can transmit the disease but who exhibit no overt symptoms. Let x and y denote the proportions of susceptible and carriers, respectively, in the population, suppose that carriers are identified and removed from the population at a rate α, so that dy/dt = −αy.…”