Particle velocity controls phase transitions in contagion dynamics

Influence of human motion patterns on epidemic spreading dynamics

Extensive real-data indicate that human motion exhibits novel patterns and has a significant impact on the epidemic spreading process. The research on the influence of human motion patterns on epidemic spreading dynamics still lacks a systematic study in network science. Based on an agent-based model, this paper simulates the spread of the disease in the gathered population by combining the susceptible-infected-susceptible epidemic process with human motion patterns, described by moving speed and gathering preference. Our simulation results show that the emergence of a hysteresis loop is observed in the system when the moving speed is slow, particularly when humans prefer to gather; that is, the epidemic prevalence of the systems depends on the fraction of initial seeds. Regardless of the gathering preference, the hysteresis loop disappears when the population moves fast. In addition, our study demonstrates that there is an optimal moving speed for the gathered population, at which the epidemic prevalence reaches its maximum value.

Sequential epidemic-like spread between agglomerates of self-propelled agents in one dimension

Motile organisms can form stable agglomerates such as cities or colonies. In the outbreak of a highly contagious disease, the control of large-scale epidemic spread depends on factors like the number and size of agglomerates, travel rate between them, and disease recovery rate. While the emergence of agglomerates permits early interventions, it also explains longer real epidemics. In this work, we study the spread of susceptible-infected-recovered (SIR) epidemics (or any sort of information exchange by contact) in one-dimensional spatially structured systems. By working in one dimension, we establish a necessary foundation for future investigation in higher dimensions and mimic micro-organisms in narrow channels. We employ a model of self-propelled particles which spontaneously form multiple clusters. For a lower rate of stochastic reorientation, particles have a higher tendency to agglomerate and therefore the clusters become larger and less numerous. We examine the time evolution averaged over many epidemics and how it is affected by the existence of clusters through the eventual recovery of infected particles before reaching new clusters. New terms appear in the SIR differential equations in the last epidemic stages. We show how the final number of ever-infected individuals depends nontrivially on single-individual parameters. In particular, the number of ever-infected individuals first increases with the reorientation rate since particles escape sooner from clusters and spread the disease. For higher reorientation rate, travel between clusters becomes too diffusive and the clusters too small, decreasing the number of ever-infected individuals.

Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model

Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. By considering individual random movement and disease spread, our simulations using the CDD-SEIR model reveal that the distribution of agent types in the community exhibits spatial heterogeneity. The estimated basic reproduction number R0 depends on group mobility, increasing logarithmically in strongly heterogeneous cases and saturating in weakly heterogeneous conditions. Notably, R0 is approximately independent of virus virulence when group mobility is low. We also show that transmission through small amounts of long-term contact is possible due to short-term contact patterns. The dependence of R0 on environment and individual movement patterns implies that reduced contact time and vaccination policies can significantly reduce the virus transmission capacity in situations where the virus is highly transmissible (i.e., R0 is relatively large). This work provides new insights into how individual movement patterns affect virus spreading and how to protect people more efficiently.

Non-uniform superlattice magnetic tunnel junctions

We propose a new class of non-uniform superlattice magnetic tunnel junctions (Nu-SLTJs) with the linear, Gaussian, Lorentzian, and Pöschl-Teller width and height based profiles manifesting a sizable enhancement in the TMR (≈10⁴- 10⁶%) with a significant suppression in the switching bias (≈9 folds) owing to the physics of broad-band spin filtering. By exploring the negative differential resistance region in the current-voltage characteristics of the various Nu-SLTJs, we predict the Nu-SLTJs offer fastest spin transfer torque switching in the order of a few hundred picoseconds. We self-consistently employ the atomistic non-equilibrium Green's function formalism coupled with the Landau-Lifshitz-Gilbert-Slonczewski equation to evaluate the device performance of the various Nu-SLTJs. We also present the design of minimal three-barrier Nu-SLTJs having significant TMR (≈10⁴%) and large spin current for the ease of device fabrication. We hope that the class of Nu-SLTJs proposed in this work may lay the bedrock to embark on the exhilarating voyage of exploring various non-uniform superlattices for the next generation of spintronic devices.

Coupling between infectious diseases leads to synchronization of their dynamics

Interactions between different diseases may change their dynamics. Thus, these interactions represent a source of uncertainty in the modeling of empirical data when the symptoms of both infections are hard to distinguish. We recall previously proposed models of interacting infections, generalizing them to non-symmetric scenarios, showing that both cooperative and competitive interactions lead to synchronization of the maximum fraction of infected individuals in their dynamics. We exemplify this framework with a model coupling the dynamics of COVID-19 and seasonal influenza, simulating cooperation, competition, and asymmetric interactions. We find that the coupling synchronizes both infections, with a stronger influence on the dynamics of influenza.

Using active matter to introduce spatial heterogeneity to the susceptible infected recovered model of epidemic spreading

The widely used susceptible-infected-recovered (S-I-R) epidemic model assumes a uniform, well-mixed population, and incorporation of spatial heterogeneities remains a major challenge. Understanding failures of the mixing assumption is important for designing effective disease mitigation approaches. We combine a run-and-tumble self-propelled active matter system with an S-I-R model to capture the effects of spatial disorder. Working in the motility-induced phase separation regime both with and without quenched disorder, we find two epidemic regimes. For low transmissibility, quenched disorder lowers the frequency of epidemics and increases their average duration. For high transmissibility, the epidemic spreads as a front and the epidemic curves are less sensitive to quenched disorder; however, within this regime it is possible for quenched disorder to enhance the contagion by creating regions of higher particle densities. We discuss how this system could be realized using artificial swimmers with mobile optical traps operated on a feedback loop.

Contagion dynamics in self-organized systems of self-propelled agents

We investigate the susceptible-infectious-recovered contagion dynamics in a system of self-propelled particles with polar alignment. Using agent-based simulations, we analyze the outbreak process for different combinations of the spatial parameters (alignment strength and Peclet number) and epidemic parameters (infection-lifetime transmissibility and duration of the individual infectious period). We show that the emerging spatial features strongly affect the contagion process. The ordered homogeneous states greatly disfavor infection spreading, due to their limited mixing, only achieving large outbreaks for high values of the individual infectious duration. The disordered homogeneous states also present low contagion capabilities, requiring relatively high values of both epidemic parameters to reach significant spreading. Instead, the inhomogeneous ordered states display high outbreak levels for a broad range of parameters. The formation of bands and clusters in these states favor infection propagation through a combination of processes that develop inside and outside of these structures. Our results highlight the importance of self-organized spatiotemporal features in a variety of contagion processes that can describe epidemics or other propagation dynamics, thus suggesting new approaches for understanding, predicting, and controlling their spreading in a variety of self-organized biological systems, ranging from bacterial swarms to animal groups and human crowds.

Social distancing in pedestrian dynamics and its effect on disease spreading

Nonpharmaceutical measures such as social distancing can play an important role in controlling the spread of an epidemic. In this paper, we use a mathematical model combining human mobility and disease spreading. For the mobility dynamics, we design an agent-based model consisting of pedestrian dynamics with a novel type of force to resemble social distancing in crowded sites. For the spreading dynamics, we consider the compartmental susceptible-exposed-infective (SEI) dynamics plus an indirect transmission with the footprints of the infectious pedestrians being the contagion factor. We show that the increase in the intensity of social distancing has a significant effect on the exposure risk. By classifying the population into social distancing abiders and nonabiders, we conclude that the practice of social distancing, even by a minority of potentially infectious agents, results in a drastic change in the population exposure risk, but it reduces the effectiveness of the protocols when practiced by the rest of the population. Furthermore, we observe that for contagions for which the indirect transmission is more significant, the effectiveness of social distancing would be reduced. This study can help to provide a quantitative guideline for policy-making on exposure risk reduction.

Understanding contagion dynamics through microscopic processes in active Brownian particles

Together with the universally recognized SIR model, several approaches have been employed to understand the contagion dynamics of interacting particles. Here, Active Brownian particles (ABP) are introduced to model the contagion dynamics of living agents that perform a horizontal transmission of an infectious disease in space and time. By performing an ensemble average description of the ABP simulations, we statistically describe susceptible, infected, and recovered groups in terms of particle densities, activity, contagious rates, and random recovery times. Our results show that ABP reproduces the time dependence observed in traditional compartmental models such as the Susceptible-Infected-Recovery (SIR) models and allows us to explore the critical densities and the contagious radius that facilitates the virus spread. Furthermore, we derive a first-principles analytical expression for the contagion rate in terms of microscopic parameters, without considering free parameters as the classical SIR-based models. This approach offers a novel alternative to incorporate microscopic processes into analyzing SIR-based models with applications in a wide range of biological systems.

Interplay between competitive and cooperative interactions in a three-player pathogen system

In ecological systems, heterogeneous interactions between pathogens take place simultaneously. This occurs, for instance, when two pathogens cooperate, while at the same time, multiple strains of these pathogens co-circulate and compete. Notable examples include the cooperation of human immunodeficiency virus with antibiotic-resistant and susceptible strains of tuberculosis or some respiratory infections with Streptococcus pneumoniae strains. Models focusing on competition or cooperation separately fail to describe how these concurrent interactions shape the epidemiology of such diseases. We studied this problem considering two cooperating pathogens, where one pathogen is further structured in two strains. The spreading follows a susceptible-infected-susceptible process and the strains differ in transmissibility and extent of cooperation with the other pathogen. We combined a mean-field stability analysis with stochastic simulations on networks considering both well-mixed and structured populations. We observed the emergence of a complex phase diagram, where the conditions for the less transmissible, but more cooperative strain to dominate are non-trivial, e.g. non-monotonic boundaries and bistability. Coupled with community structure, the presence of the cooperative pathogen enables the coexistence between strains by breaking the spatial symmetry and dynamically creating different ecological niches. These results shed light on ecological mechanisms that may impact the epidemiology of diseases of public health concern.

Exact solution of generalized cooperative susceptible-infected-removed (SIR) dynamics

In this paper, we introduce a general framework for coinfection as cooperative susceptible-infected-removed (SIR) dynamics. We first solve the SIR model analytically for two symmetric cooperative contagions [L. Chen et al., Europhys. Lett. 104, 50001 (2013)10.1209/0295-5075/104/50001] and then generalize and solve the model exactly in the symmetric scenarios for three and more cooperative contagions. We calculate the transition points and order parameters, i.e., the total number of infected hosts. We show that the behavior of the system does not change qualitatively with the inclusion of more diseases. We also show analytically that there is a saddle-node-like bifurcation for two cooperative SIR dynamics and that the transition is hybrid. Moreover, we investigate where the symmetric solution is stable for initial fluctuations. We finally explore sets of parameters which give rise to asymmetric cases, namely, the asymmetric cases of primary and secondary infection rates of one pathogen with respect to another. This setting can lead to fewer infected hosts, a higher epidemic threshold, and also continuous transitions. These results open the road to a better understanding of disease ecology.