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The COVID-19 pandemic has challenged authorities at different levels of government administration aroundthe 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 as well as the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible?Exposed?Infected?Recovered modelthat incorporates the social structure of Mar del Plata, the 4◦ most inhabited city in Argentina and head ofthe Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individualsaccording to the illness severity, as well as data of local health resources, to bring predictions closer to thelocal reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantinestrategy: a part of the population can circulate without restrictions at any time, while the rest is equally dividedinto two groups and goes on successive periods of normal activity and lockdown, each one with a durationof days. We also implement a random testing strategy with a threshold over the population. We found that = 7 is a good choice for the quarantine strategy since it reduces the infected population and, conveniently,it suits a weekly schedule. Focusing on the health system, projecting from the situation as of September 30,we foresee a difficulty to avoid saturation of the available ICU, given the extremely low levels of mobility thatwould be required. In the worst case, our model estimates that four thousand deaths would occur, of which30% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing wouldallow an increase in the percentage of people that can circulate without restrictions, and the medical facilitiesto deal with the additional critical patients would be relatively low. Fil: Vassallo, Lautaro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina Fil: Pérez, Ignacio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina Fil: Alvarez Zuzek, Lucila G.. University Of Georgetown; Estados Unidos Fil: Amaya, Julián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina Fil: Torres, Marcos F.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina Fil: Valdez, Lucas Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina Fil: la Rocca, Cristian Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina Fil: Braunstein, Lidia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina

In this paper we study the Motter-Lai model of cascading failures induced by overloads in both isotropic and anisotropic spatial networks, generated by placing nodes in a square lattice and using various distributions of link lengths and angles. Anisotropy has not been earlier considered in the Motter-Lai model and is a real feature that may affect the cascading failures. This could reflect the existence of a preferred direction in which a given attribute of the system manifests, such as power lines that follow a city built parallel to the coast. We show that the anisotropy causes a greater spread of damage along the preferential direction of links. We also identify the critical linear size, $l_c$, for a square shaped localized attack, which satisfies with high probability that above $l_c$ the cascading disrupts the giant component of functional nodes, while below $l_c$ the damage does not spread. We find that, for networks with any characteristic link length, their robustness decreases with the strength of the anisotropy. We show that the value of $l_c$ is finite and independent of the system size (for large systems), both for isotropic and anisotropic networks. Thus, in contrast to random attacks, where the critical fraction of nodes that survive the initial attack, $p_c$, is usually below 1, here $p_c = 1$. Note that the analogy to $p_c = 1$ is also found for localized attacks in interdependent spatial networks. Finally, we measure the final distribution of functional cluster sizes and find a power-law behavior, with exponents similar to regular percolation. This indicates that, after the cascade which destroys the giant component, the system is at a percolation critical point. Additionally, we observe a crossover in the value of the distribution exponent, from critical percolation in a two-dimensional lattice for strong spatial embedding, to mean-field percolation for weak embedding., Comment: 22 pages, 11 figures

In real social networks, person-to-person interactions are known to be heterogeneous, which can affect the way a disease spreads through a population, reaches a tipping point in the fraction of infected individuals, and becomes an epidemic. This property, called disorder, is usually associated with contact times between individuals and can be modeled by a weighted network, where the weights are related to normalized contact times $\omega$. In this paper, we study the SIR model for disease spreading when both close and distant types of interactions are present. We develop a mitigation strategy that reduces only the time duration of distant contacts, which are easier to alter in practice. Using branching theory, supported by simulations, we found that the effectiveness of the strategy increases when the density $f_1$ of close contacts decreases. Moreover, we found a threshold $\tilde{f}_1 = T_c / \beta$ below which the strategy can bring the system from an epidemic to a non-epidemic phase, even when close contacts have the longest time durations., Comment: 17 pages, 14 figures

The frequent emergence of diseases with the potential to become threats at local and global scales, such as influenza A(H1N1), SARS, MERS, and recently COVID-19 disease, makes it crucial to keep designing models of disease propagation and strategies to prevent or mitigate their effects in populations. Since isolated systems are exceptionally rare to find in any context, especially in human contact networks, here we examine the susceptible-infected-recovered model of disease spreading in a multiplex network formed by two distinct networks or layers, interconnected through a fraction $q$ of shared individuals (overlap). We model the interactions through weighted networks, because person-to-person interactions are diverse (or disordered); weights represent the contact times of the interactions. Using branching theory supported by simulations, we analyze a social distancing strategy that reduces the average contact time in both layers, where the intensity of the distancing is related to the topology of the layers. We find that the critical values of the distancing intensities, above which an epidemic can be prevented, increase with the overlap $q$. Also we study the effect of the social distancing on the mutual giant component of susceptible individuals, which is crucial to keep the functionality of the system. In addition, we find that for relatively small values of the overlap $q$, social distancing policies might not be needed at all to maintain the functionality of the system., Comment: 24 pages, 9 figures

Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems, and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behavior. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed., Comment: This review has been accepted for publication in the Journal of Complex Networks Published by Oxford University Press

Interdependencies are ubiquitous throughout the world. Every real-world system interacts with and is dependent on other systems, and this interdependency affects their performance. In particular, interdependencies among networks make them vulnerable to failure cascades, the effects of which are often catastrophic. Failure propagation fragments network components, disconnects them, and may cause complete systemic failure. We propose a strategy of avoiding or at least mitigating the complete destruction of a system of interdependent networks experiencing a failure cascade. Starting with a fraction $1-p$ of failing nodes in one network, we reconnect with a probability $\gamma$ every isolated component to a functional giant component (GC), the largest connected cluster. We find that as $\gamma$ increases the resilience of the system to cascading failure also increases. We also find that our strategy is more effective when it is applied in a network of low average degree. We solve the problem theoretically using percolation theory, and we find that the solution agrees with simulation results., Comment: 13 pages, 4 figures

We propose and study a model for the interplay between two different dynamical processes -one for opinion formation and the other for decision making- on two interconnected networks A and B. The opinion dynamics on network A corresponds to that of the M-model, where the state of each agent can take one of four possible values (S = -2,-1, 1, 2), describing its level of agreement on a given issue. The likelihood to become an extremist (S = ±2) or a moderate (S = ±1) is controlled by a reinforcement parameter r ≥ 0. The decision making dynamics on network B is akin to that of the Abrams-Strogatz model, where agents can be either in favor (S = +1) or against (S = -1) the issue. The probability that an agent changes its state is proportional to the fraction of neighbors that hold the opposite state raised to a power β. Starting from a polarized case scenario in which all agents of network A hold positive orientations while all agents of network B have a negative orientation, we explore the conditions under which one of the dynamics prevails over the other, imposing its initial orientation. We find that, for a given value of β, the two-network system reaches a consensus in the positive state (initial state of network A) when the reinforcement overcomes a crossover value r∗(β), while a negative consensus happens for r < r∗(β). In the r - β phase space, the system displays a transition at a critical threshold βc, from a coexistence of both orientations for β < βc to a dominance of one orientation for β > βc. We develop an analytical mean-field approach that gives an insight into these regimes and shows that both dynamics are equivalent along the crossover line (r∗, β∗)., Facultad de Ciencias Exactas, Instituto de Física de Líquidos y Sistemas Biológicos

We study the changes of opinions about vaccination together with the evolution of a disease. In our model we consider a multiplex network consisting of two layers. One of the layers corresponds to a social network where people share their opinions and influence others opinions. The social model that rules the dynamic is the M-model, which takes into account two different processes that occurs in a society: persuasion and compromise. This two processes are related through a parameter $r$, $r1$ the society tends to have extremist opinions, while $r=1$ represents a neutral society. This social network may be of real or virtual contacts. On the other hand, the second layer corresponds to a network of physical contacts where the disease spreading is described by the SIR-Model. In this model the individuals may be in one of the following four states: Susceptible ($S$), Infected($I$), Recovered ($R$) or Vaccinated ($V$). A Susceptible individual can: i) get vaccinated, if his opinion in the other layer is totally in favor of the vaccine, ii) get infected, with probability $\beta$ if he is in contact with an infected neighbor. Those $I$ individuals recover after a certain period $t_r=6$. Vaccinated individuals have an extremist positive opinion that does not change. We consider that the vaccine has a certain effectiveness $\omega$ and as a consequence vaccinated nodes can be infected with probability $\beta (1 - \omega)$ if they are in contact with an infected neighbor. In this case, if the infection process is successful, the new infected individual changes his opinion from extremist positive to totally against the vaccine. We find that depending on the trend in the opinion of the society, which depends on $r$, different behaviors in the spread of the epidemic occurs. An epidemic threshold was found., Comment: 17 pages, 5 figures