Antoine Allard, Cristopher Moore, Samuel V. Scarpino, Benjamin M. Althouse, and Laurent Hébert-Dufresne

SIAM Review Vol. 65, Iss. 2 (2023) 10.1137/20M1383811

Most models of epidemic spread, including many designed specifically for COVID-19, implicitly assume mass-action contact patterns and undirected contact networks, meaning that the individuals most likely to spread the disease are also the most at risk of contracting it from others. Here, we review results from the theory of random directed graphs which show that many important quantities, including the reproduction number and the epidemic size, depend sensitively on the joint distribution of in- and out-degrees (“risk” and “spread”), including their heterogeneity and the correlation between them. By considering joint distributions of various kinds, we elucidate why some types of heterogeneity cause a deviation from the standard Kermack–McKendrick analysis of SIR models, i.e., so-called mass-action models where contacts are homogeneous and random, and why some do not. We also show that some structured SIR models informed by realistic complex contact patterns among types of individuals (age or activity) are simply mixtures of Poisson processes and tend not to deviate significantly from the simplest mass-action model. Finally, we point out some possible policy implications of this directed structure, both for contact tracing strategy and for interventions designed to prevent superspreading events. In particular, directed graphs have a forward and a backward version of the classic “friendship paradox”—forward edges tend to lead to individuals with high risk, while backward edges lead to individuals with high spread—such that a combination of both forward and backward contact tracing is necessary to find superspreading events and prevent future cascades of infection.

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KAROL A. BACIK, BOGDAN S. BACIK, AND TIM ROGERS

SCIENCE Vol 379, Issue 6635 pp. 923-928

Laning is a paradigmatic example of spontaneous organization in active two-component flows that has been observed in diverse contexts, including pedestrian traffic, driven colloids, complex plasmas, and molecular transport. We introduce a kinetic theory that elucidates the physical origins of laning and quantifies the propensity for lane nucleation in a given physical system. Our theory is valid in the low-density regime, and it makes different predictions about situations in which lanes may form that are not parallel with the direction of flow. We report on experiments with human crowds that verify two notable consequences of this phenomenon: tilting lanes under broken chiral symmetry and lane nucleation along elliptic, parabolic, and hyperbolic curves in the presence of sources or sinks.

Read the full article at: www.science.org

Nicholas W. Landry, Ilya Amburg, Mirah Shi, Sinan G. Aksoy
Many complex systems often contain interactions between more than two nodes, known as higher-order interactions, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset’s structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.

Read the full article at: arxiv.org

Emanuela Del Dottore, Barbara Mazzolai

Artificial Life

Plants thrive in virtually all natural and human-adapted environments and are becoming popular models for developing robotics systems because of their strategies of morphological and behavioral adaptation. Such adaptation and high plasticity offer new approaches for designing, modeling, and controlling artificial systems acting in unstructured scenarios. At the same time, the development of artifacts based on their working principles reveals how plants promote innovative approaches for preservation and management plans and opens new applications for engineering-driven plant science. Environmentally mediated growth patterns (e.g., tropisms) are clear examples of adaptive behaviors displayed through morphological phenotyping. Plants also create networks with other plants through subterranean roots–fungi symbiosis and use these networks to exchange resources or warning signals. This article discusses the functional behaviors of plants and shows the close similarities with a perceptron-like model that could act as a behavior-based control model in plants. We begin by analyzing communication rules and growth behaviors of plants; we then show how we translated plant behaviors into algorithmic solutions for bioinspired robot controllers; and finally, we discuss how those solutions can be extended to embrace original approaches to networking and robotics control architectures.

Read the full article at: direct.mit.edu