Emergence, survival, and segregation of competing gangs.

In this paper, we approach the phenomenon of criminal activity from an infectious perspective by using tailored compartmental agent-based models that include the social flavor of the mechanisms governing the evolution of crime in society. Specifically, we focus on addressing how the existence of competing gangs shapes the penetration of crime. The mean-field analysis of the model proves that the introduction of dynamical rules favoring the simultaneous survival of both gangs reduces the overall number of criminals across the population as a result of the competition between them. The implementation of the model in networked populations with homogeneous contact patterns reveals that the evolution of crime substantially differs from that predicted by the mean-field equations. We prove that the system evolves toward a segregated configuration where, depending on the features of the underlying network, both gangs can form spatially separated clusters. In this scenario, we show that the beneficial effect of the coexistence of two gangs is hindered, resulting in a higher penetration of crime in the population.

A metapopulation approach to identify targets for Wolbachia-based dengue control.

Over the last decade, the release of Wolbachia-infected Aedes aegypti into the natural habitat of this mosquito species has become the most sustainable and long-lasting technique to prevent and control vector-borne diseases, such as dengue, zika, or chikungunya. However, the limited resources to generate such mosquitoes and their effective distribution in large areas dominated by the Aedes aegypti vector represent a challenge for policymakers. Here, we introduce a mathematical framework for the spread of dengue in which competition between wild and Wolbachia-infected mosquitoes, the cross-contagion patterns between humans and vectors, the heterogeneous distribution of the human population in different areas, and the mobility flows between them are combined. Our framework allows us to identify the most effective areas for the release of Wolbachia-infected mosquitoes to achieve a large decrease in the global dengue prevalence.

Contagion-diffusion processes with recurrent mobility patterns of distinguishable agents.

The analysis of contagion-diffusion processes in metapopulations is a powerful theoretical tool to study how mobility influences the spread of communicable diseases. Nevertheless, many metapopulation approaches use indistinguishable agents to alleviate analytical difficulties. Here, we address the impact that recurrent mobility patterns, and the spatial distribution of distinguishable agents, have on the unfolding of epidemics in large urban areas. We incorporate the distinguishable nature of agents regarding both their residence and their usual destination. The proposed model allows both a fast computation of the spatiotemporal pattern of the epidemic trajectory and the analytical calculation of the epidemic threshold. This threshold is found as the spectral radius of a mixing matrix encapsulating the residential distribution and the specific commuting patterns of agents. We prove that the simplification of indistinguishable individuals overestimates the value of the epidemic threshold.

Fear induced explosive transitions in the dynamics of corruption.

In this article, we analyze a compartmental model aimed at mimicking the role of imitation and delation of corruption in social systems. In particular, the model relies on a compartmental dynamics in which individuals can transit between three states: honesty, corruption, and ostracism. We model the transitions from honesty to corruption and from corruption to ostracism as pairwise interactions. In particular, honest agents imitate corrupt peers while corrupt individuals pass to ostracism due to the delation of honest acquaintances. Under this framework, we explore the effects of introducing social intimidation in the delation of corrupt people. To this aim, we model the probability that an honest delates a corrupt agent as a decreasing function of the number of corrupt agents, thus mimicking the fear of honest individuals to reprisals by those corrupt ones. When this mechanism is absent or weak, the phase diagram of the model shows three equilibria [(i) full honesty, (ii) full corruption, and (iii) a mixed state] that are connected via smooth transitions. However, when social intimidation is strong, the transitions connecting these states turn explosive leading to a bistable phase in which a stable full corruption phase coexists with either mixed or full honesty stable equilibria. To shed light on the generality of these transitions, we analyze the model in different network substrates by means of Monte Carlo simulations and deterministic microscopic Markov chain equations. This latter formulation allows us to derive analytically the different bifurcation points that separate the different phases of the system.

Norm violation versus punishment risk in a social model of corruption.

We analyze the onset of social-norm-violating behaviors when social punishment is present. To this aim, a compartmental model is introduced to illustrate the flows among the three possible states: honest, corrupt, and ostracism. With this simple model we attempt to capture some essential ingredients such as the contagion of corrupt behaviors to honest agents, the delation of corrupt individuals by honest ones, and the warning to wrongdoers (fear like that triggers the conversion of corrupt people into honesty). In nonequilibrium statistical physics terms, the former dynamics can be viewed as a non-Hamiltonian kinetic spin-1 Ising model. After developing in full detail its mean-field theory and comparing its predictions with simulations made on regular networks, we derive the conditions for the emergence of corrupt behaviors and, more importantly, illustrate the key role of the warning-to-wrongdoers mechanism in the latter.

Explosive transitions induced by interdependent contagion-consensus dynamics in multiplex networks.

We introduce a model to study the interplay between information spreading and opinion formation in social systems. Our framework consists in a two-layer multiplex network where opinion dynamics takes place in one layer, while information spreads on the other one. The two dynamical processes are mutually coupled in such a way that the control parameters governing the dynamics of the node states at one layer depend on the dynamical states at the other layer. In particular, we consider the case in which consensus is favored by the common adoption of information, while information spreading is boosted between agents sharing similar opinions. Numerical simulations of the model point out that, when the coupling between the dynamics of the two layers is strong enough, a double explosive transition, i.e., a discontinuous transition both in consensus dynamics and in information spreading appears. Such explosive transitions lead to bi-stability regions in which the consensus-informed states and the disagreement-uninformed states are both stable solutions of the intertwined dynamics.

Markovian approach to tackle the interaction of simultaneous diseases.

The simultaneous emergence of several abrupt disease outbreaks or the extinction of some serotypes of multistrain diseases are fingerprints of the interaction between pathogens spreading within the same population. Here, we propose a general and versatile benchmark to address the unfolding of both cooperative and competitive interacting diseases. We characterize the explosive transitions between the disease-free and the epidemic regimes arising from the cooperation between pathogens and show the critical degree of cooperation needed for the onset of such abrupt transitions. For the competing diseases, we characterize the mutually exclusive case and derive analytically the transition point between the full-dominance phase, in which only one pathogen propagates, and the coexistence regime. Finally, we use this framework to analyze the behavior of the former transition point as the competition between pathogens is relaxed.