Rayan Succar

The COVID‐19 pandemic exposed the vulnerabilities of carceral systems as high‐risk environments for disease transmission. In response, jurisdictions across the United States implemented mass decarceration policies aimed at reducing jail populations. This paper investigates such interventions and their optimal planning, leveraging system‐theoretic insights. First, we empirically demonstrate the influence of jail population changes on COVID‐19 infections by applying convergent cross mapping, transfer entropy, and linear regression to real‐world data. We find that the jail population dynamics were associated with the evolution of the pandemic. Building on this empirical finding, we present a compartmental epidemiological model that captures the coupled dynamics of disease spread in jails and their surrounding communities, with fluxes modeled as time‐dependent control inputs. Using optimal control theory, we derive an optimal design for these fluxes to balance public health and public safety. Our findings provide both empirical and model‐informed evidence that mass decarceration was not only a reactive policy choice but also a justifiable intervention for pandemic mitigation. We emphasize that our optimization is carried out within the space of admission and release policies and does not rank decarceration against other public‐health measures.

Network dynamical systems are ubiquitous in science and engineering. The most basic property of a network dynamical system is its size, which, for scalar dynamics, corresponds to the number of nodes. For linear network systems, recent studies have developed reliable tools for inferring the size of the system from perceptible dynamics (measurements of one or some of the network nodes) across multiple experiments. Here, we extend these tools to nonlinear network systems by putting forward a model-agnostic approach that combines clustering techniques, the use of detection matrices, and spectral analysis. The theoretical premise of the algorithm is that, under mild assumptions, the variation between the dynamics of some nodes across multiple measurements can be used to bound the variation between the dynamics of all nodes across the same measurements. By applying clustering techniques on perceptible dynamics, we identify nearby measurements, about which the variational dynamics are approximately linear and the use of the detection matrix is valid. From the spectrum of the detection matrix, we infer its rank, which corresponds to the size of the nonlinear network system. We demonstrate our approach via numerical experiments on different nonlinear network systems, including different types of hypergraphs. Whether nonlinearity comes from individual dynamics of the nodes or the interactions among them, it is rarely a feature that one can dismiss. Our work paves the way to infer the size of a nonlinear network system when governing equations are unknown and only limited data are accessible.

Firearm injuries are a leading cause of death in the United States, surpassing fatalities from motor vehicle crashes. Despite this significant public health risk, Americans continue to purchase firearms in large quantities. Commonly cited drivers of firearm acquisition include fear of violent crime, fear of mass shootings, and panic-buying. Additionally, advocacy groups’ activity on social media may capitalize on emotions like fear and influence firearm acquisition. The simultaneous effects of these variables have not been explored in a causal framework. In this study, we aim to elucidate the causal roles of media coverage of firearm laws and regulations, media coverage of mass shootings, media coverage of violent crimes, and the Twitter activity of anti- and proregulation advocacy groups in short-term firearm acquisition in the United States. We collect daily time series for these variables from 2012 to 2020 and employ the PCMCI+ framework to investigate the causal structures among them simultaneously. Our results indicate that the Twitter activity of antiregulation advocacy groups directly drives firearm acquisitions. We also find that media coverage of firearm laws and regulations and media coverage of violent crimes influence firearm acquisition. Although media coverage of mass shootings and online activity of proregulation organizations are potential drivers of firearm acquisition, in the short term, only the lobbying efforts of antiregulation organizations on social media and specific media coverage appear to influence individuals’ decisions to purchase firearms.

Impulsivity has been proposed as a key driver of obesity. However, evidence linking impulsivity and obesity has relied on the study of individual factors, with limited account for the urban attributes of obesogenic environments. Here, we investigate the relationship between obesity and impulsivity through urban scaling and causal discovery. For 915 cities in the United States of America, we study the prevalence of obesity in adults, attention deficit hyperactivity disorder (ADHD) in children, and relevant urban features. We observe sublinear scaling of obesity and ADHD with population size, these disorders being less prevalent in larger cities. By applying a causal discovery tool to the deviations of cities from the urban scaling laws, we identify an influence of ADHD on obesity, moderated by lifestyle. The strength of these associations is confirmed by individual-level data on a cohort of 19,333 children, wherein we observe that ADHD modulates obesity both directly and indirectly.

Detecting coupling in network dynamical systems from time series is an open problem in the physics of complex systems. In this Letter, we tackle this issue from a control-theoretic perspective. Drawing inspiration from Kalman's notion of observability, we argue the presence of directional coupling between two units, X→Y, when X is detected as an internal state from the measurement of Y. We illustrate this approach on a series of analytically tractable systems, showcasing how it overcomes limitations of state-of-the-art methods for network inference.

In the realm of urban science, scaling laws are essential for understanding the relationship between city population and urban features, such as socioeconomic outputs. Ideally, these laws would be based on complete datasets; however, researchers often face challenges related to data availability and reporting practices, resulting in datasets that include only the highest observations of the urban features (top-k). A key question that emerges is: Under what conditions can an analysis based solely on top-k observations accurately determine whether a scaling relationship is truly superlinear or sublinear? To address this question, we conduct a numerical study that explores how relying exclusively on reported values can lead to erroneous conclusions, revealing a selection bias that favors sublinear over superlinear scaling. In response, we develop a method that provides robust estimates of the minimum and maximum potential scaling exponents when only top-k observations are available. We apply this method to two case studies involving firearm violence, a domain notorious for its suppressed datasets, and we demonstrate how this approach offers a reliable framework for analyzing scaling relationships with censored data.

Inferring the number of states of a stochastic system from partial measurements is a fundamental problem in physics, for which methodological tools remain scarce. It is sometimes difficult to distinguish the stochastic dynamical states from measurements, deceiving us into incorrect models and flawed understanding of natural phenomena. Here, we propose a model-free statistical framework, grounded in network and control theory, to estimate the number of states of a stochastic system from perceptible dynamics. The framework extends previous techniques for deterministic systems, based on the rank of ancillary matrices. We show applications of our approach to a variety of physics domains, such as statistical mechanics, biophysics, physical chemistry, and epidemiology. Published by the American Physical Society 2024

Public sentiment towards the police is a matter of great interest in the United States, as reports on police misconduct are increasingly being published in mass and social media. Here, we test how the public’s perception of the police can be majorly shaped by media reports of police brutality and local crime. We collect data on media coverage of police brutality and local crime, together with Twitter posts from 2010-2020 about the police in 18 metropolitan areas in the country. Using a range of model-free approaches building on transfer entropy analysis, we discover an association between public sentiment towards the police and media coverage of police brutality. We cautiously interpret this relationship as causal. Through this lens, the public’s sentiment towards the police appears to be driven by media-projected images of police misconduct, with no statistically significant evidence for a comparable effect driven by media reports on crimes.

BACKGROUND Firearm injuries are a leading cause of death in the United States, where a greater number of people die by firearms than in motor vehicle crashes. Although firearms present a significant public health risk, Americans continue to purchase them in large amounts. Three drivers of firearm acquisition are commonly cited in the literature, namely, fear of violent crime, fear of mass shootings, and panic-buying. These drivers were widely researched as conceptual factors that contribute to individuals’ decision to purchase firearms, yet their role in prompting purchases has never been studied. In addition, the activity of relevant interest groups on social media, which capitalize on emotions like fear, may impact impulsive firearm acquisition. The combined roles of all factors in driving firearm purchases have never been tested in a causal framework. OBJECTIVE We aim to elucidate the role of media coverage of firearm laws and regulations, mass shootings, violent crimes, and the activity of anti- and pro-regulation organizations on social media in short-term firearm acquisition in the United States. METHODS We generate daily time series of media coverage of firearm laws and regulations, media coverage of mass shootings, media coverage of violent crimes, Twitter posts of anti-regulation organizations, Twitter posts of pro-regulation organizations, and background checks between 2012 and 2020. We employ the framework of PCMCI+ to investigate the causal structures between the six variables simultaneously. RESULTS We find that the activity of anti-regulation organizations on social media directly drives firearm acquisitions. We also show that media coverage of firearm laws and regulations and media coverage of violent crimes influence firearm acquisition. CONCLUSIONS Although media coverage of mass shootings and online activity of pro-regulation organizations are possible drivers of firearm acquisition, in the short term, only lobbying efforts of anti-regulation organizations, media coverage of violent crime, and media coverage of firearm laws and regulations appear to influence people’s decision to purchase a firearm. CLINICALTRIAL