arXiv subjects

Lucila G. Alvarez-Zuzek

Publications and source records attributed to Lucila G. Alvarez-Zuzek.

6 recordsLinked to original sources

arXiv2026

Statistical Laws of Human Mobility under Large-Scale Behavioural Shock

Human mobility follows robust statistical laws, but whether these laws persist under large, sudden behavioural shocks remains unclear. Here we analyse privacy-enhanced GPS trajectories of over one million users in California and 180,000 in Massachusetts from January to September 2020, spanning the onset and easing of COVID-19 restrictions. During restrictions, individual ranges contracted below a characteristic scale of about 5 km, while short- and medium-range trip lengths and rare long-distance trips persisted. Exploration slowed from super-diffusive to near-diffusive, and visits concentrated on a few habitual locations. Trajectory shape, once rescaled, was preserved, and the entropy reduction is largely explained by fewer visited locations rather than more regular routines. Recovery after reopening was heterogeneous rather than a uniform return to baseline, and the decline began before mandatory orders. These results suggest that restrictions reorganised, rather than simply rescaled, individual mobility, with implications for mobility-based epidemic models.

physics.soc-ph↗

arXiv2026

Opinion dynamics under large-scale exogenous events

Large-scale events such as political scandals or misinformation campaigns can abruptly shift the collective opinion of a population and reshape the dynamics in ways that standard local-interaction models of sociophysics do not capture. Here, we investigate the dynamics of opinion formation when a group of interacting agents is subjected to sudden, macroscopic exogenous shocks. Our framework is general and applies to arbitrary opinion dynamics models; to illustrate its scope, we consider the voter model and variations thereof. We verify that the mathematical description is excellent in describing the first-passage properties to consensus of the voter model on the complete graph and on complex networks. Remarkably, we identify the conditions under which shocks can be advantageous or detrimental to reach consensus. For the case of the all-to-all noisy voter model under exogenous events, we unveil a rich phase diagram characterized by novel trimodal and asymmetric bimodal states. Ultimately, this work provides a rigorous yet flexible stochastic-process framework for understanding the out-of-equilibrium behavior of opinion dynamics under recurrent exogenous stressors.

physics.soc-ph↗

arXiv2025

Mapping the interaction between science and misinformation in COVID-19 tweets

During the COVID-19 pandemic, scientific knowledge evolved rapidly, accompanied by a surge of misinformation, labelled an infodemic by the WHO. In this context, we study the interaction between science and misinformation on Twitter (now X) using a database of ~407M COVID-19-related tweets. We classify URL reliability with Media Bias/Fact Check and used Altmetric data to identify scientific publications. We find that among ~1.2M users who shared science, 45% also shared unreliable content. Scientific papers circulated by these users were more often preprints, slightly more likely to be retracted, less cited, and published in lower-impact journals. Our findings indicate misinformation is not driven by a lack of exposure to science but instead raise critical questions about open science practices, particularly the role of preprints in amplifying misleading narratives. Our results underscore the importance of proactive scientific engagement on social media in countering misinformation and reinforcing trust in science during global crises.

physics.soc-ph↗

arXiv2024

Quantifying the impact of persuasiveness, cautiousness and prior beliefs in (mis)information sharing on online social networks using Drift Diffusion Models

Misleading newsletters can shape individuals' perceptions, and pose a threat to societies; as we witnessed by lowering the severity of follow-up stay-at-home orders and burdening a significant challenge to the fight against COVID-19. In this research, we study (mis)information spreading, reanalyzing behavioral data on online sharing, and analyzing decision-making mechanisms using the Drift Diffusion Model (DDM). We find that subjects display an increased instinctive inclination towards sharing misleading news, but rational thinking significantly curbs this reaction, especially for more cautious and older individuals. On top of network structures with similar characteristics as X, Mastodon, and Facebook, we use an agent-based model to expand this individual knowledge to a large scale where individuals are exposed to (mis)information through friends and share (or not) content with probabilities driven by DDM. We found that the natural shape of these social online networks provides a fertile ground for any news to rapidly become viral. Yet we have found that, for the case of X, limiting the number of followers of the most connected users proves to be an appropriate and feasible containment strategy.

physics.soc-ph↗

arXiv2024

Predicting human cooperation: sensitizing drift-diffusion model to interaction and external stimuli

As humans perceive and actively engage with the world, we adjust our decisions in response to shifting group dynamics and are influenced by social interactions. This study aims to identify which aspects of interaction affect cooperation-defection choices. Specifically, we investigate human cooperation within the Prisoner's Dilemma game, using the Drift-Diffusion Model to describe the decision-making process. We introduce a novel Bayesian model for the evolution of the model's parameters based on the nature of interactions experienced with other players. This approach enables us to predict the evolution of the population's expected cooperation rate. We successfully validate our model using an unseen test dataset and apply it to explore three strategic scenarios: co-player manipulation, use of rewards and punishments, and time pressure. These results support the potential of our model as a foundational tool for developing and testing strategies aimed at enhancing cooperation, ultimately contributing to societal welfare.

physics.soc-ph↗

arXiv2021

An epidemic model for COVID-19 transmission in Argentina: Exploration of the alternating quarantine and massive testing strategies

The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system and the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the $4^\circ$ most inhabited city in Argentina and head of the Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring these predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy, in which a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of $τ$ days. Besides, we implement a random testing strategy over the population. We found that $τ= 7$ is a good choice for the quarantine strategy since it matches with the weekly cycle as it reduces the infected population. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30\% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, being the equipment required to deal with the additional critical patients relatively low.

q-bio.PE↗