Combinatorial approach to spreading processes on networks

Dario Mazzilli & Filippo Radicchi
The European Physical Journal B volume 94, Article number: 15 (2021)

Stochastic spreading models defined on complex network topologies are used to mimic the diffusion of diseases, information, and opinions in real-world systems. Existing theoretical approaches to the characterization of the models in terms of microscopic configurations rely on some approximation of independence among dynamical variables, thus introducing a systematic bias in the prediction of the ground-truth dynamics. Here, we develop a combinatorial framework based on the approximation that spreading may occur only along the shortest paths connecting pairs of nodes. The approximation overestimates dynamical correlations among node states and leads to biased predictions. Systematic bias is, however, pointing in the opposite direction of existing approximations. We show that the combination of the two biased approaches generates predictions of the ground-truth dynamics that are more accurate than the ones given by the two approximations if used in isolation. We further take advantage of the combinatorial approximation to characterize theoretical properties of some inference problems, and show that the reconstruction of microscopic configurations is very sensitive to both the place where and the time when partial knowledge of the system is acquired.

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Survival of the Systems

Timothy M.Lenton, Timothy A.Kohler, Pablo A.Marquet, Richard A.Boyle, Michel Crucifix, David M.Wilkinson, Marten Scheffer

Trends Ecol. Evol.

Recent theoretical progress highlights that natural selection can occur based solely on differential persistence of biological entities, without the need for conventional replication.

This calls for a reconsideration of how ecosystems and social (-ecological) systems can evolve, based on identifying system-level properties that affect their persistence.

Feedback cycles have irreducible properties arising from the interactions of unrelated components, and are critical to determining ecosystem and social system persistence.

Self-perpetuating feedbacks involving the acquisition and recycling of resources, alteration of local environmental conditions, and amplification of disturbance factors, enhance ecosystem and social system spread and persistence.

Cycles built from the by-products of traits, naturally selected at lower levels, avoid conflict between levels and types of selection.

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Dynamics of cascades on burstiness-controlled temporal networks

Samuel Unicomb, Gerardo Iñiguez, James P. Gleeson & Márton Karsai
Nature Communications volume 12, Article number: 133 (2021)

Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. Here we develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics.

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Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas

Alberto Aleta, David Martín-Corral, Michiel A. Bakker, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova, Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran, Ira M. Longini Jr., Alex Pentland, Alessandro Vespignani, Yamir Moreno, Esteban Moro

Detailed characterizations of SARS-CoV-2 transmission risk across different social settings can inform the design of targeted and less disruptive non-pharmaceutical interventions (NPI), yet these data have been lacking. Here we integrate real-time, anonymous and privacy-enhanced geolocalized mobility data with census and demographic data in the New York City and Seattle metropolitan areas to build a detailed agent-based model of SARS-CoV-2 transmission. The aim is to estimate where, when, and how many transmission events happened in those urban areas during the first wave of the pandemic. We estimate that most infections (80%) are produced by a small number of people (27%), and that about 10% of events can be considered super-spreading events (SSEs), i.e. generating more than eight secondary infections. Although mass gatherings present an important risk for future SSEs, we find that the bulk of transmission in the first wave occurred in smaller events at settings like workplaces, grocery stores, or food venues. We also observe that places where the majority of transmission and SSEs happened changed during the pandemic and are different across cities, a signal of the large underlying behavioral component underneath them. Our results demonstrate that constant real-time tracking of transmission events is needed to create, evaluate, and refine more effective and localized measures to contain the pandemic.

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Dynamics of informal risk sharing in collective index insurance

Fernando P. Santos, Jorge M. Pacheco, Francisco C. Santos & Simon A. Levin
Nature Sustainability (2021)

Extreme weather events often prevent low-income farmers from accessing high-return technologies that would enhance their productivity. As a result, they often fall into poverty traps, a problem likely to worsen as the frequency of weather disasters increases due to climate change. Insurance offers, in principle, a solution for this conundrum and a means to guarantee households’ wellbeing. Group collective index insurance constitutes an alternative to indemnity or individual index insurance, and has the potential to alleviate basis risk through within-group informal transfers. Here we show that collective index insurance introduces a coordination dilemma of insurance adoption: socially optimal outcomes are obtained when everyone adopts insurance; however, a minimum fraction of contributors is necessary before the effects of basis risk can be averaged out and individuals start taking up insurance. We further show that additional mechanisms—such as local peer monitoring and defector exclusion—are necessary to stabilize informal transfers and collective index insurance adoption. Together, collective index insurance and informal transfers may thus constitute a practical instrument to improve sustainability in developing countries.

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