Search

12 pages, 5 figures, 2 Appendices, How large ecosystems can create and maintain the remarkable biodiversity we see in nature is probably one of the biggest open questions in science, attracting attention from different fields, from theoretical ecology to mathematics and physics. In this context, modeling the stable coexistence of species competing for limited resources is a particularly challenging task. From a mathematical point of view, coexistence in competitive dynamics can be achieved when dominance among species forms intransitive loops. However, these relationships usually lead to species' relative abundances neutrally cycling without converging to a stable equilibrium. Although in recent years several mechanisms have been proposed, models able to explain species coexistence in competitive communities are still limited. Here we identify locality in the interactions as one of the simplest mechanisms leading to stable species coexistence. We consider a simplified ecosystem where individuals of each species lay on a spatial network and interactions are possible only between nodes within a certain distance. Varying such distance allows to interpolate between local and global competition. Our results demonstrate, within the scope of our model, that species coexist reaching a stable equilibrium when two conditions are met: individuals are embedded in space and can only interact with other individuals within a short distance. On the contrary, when one of these ingredients is missing, large oscillations and neutral cycles emerge., V.C-S. and S.M. thank H. Saiz for insightful discussions. J.G.G. acknowledges support by Gobierno de Aragon/Fondo Social Europeo ( E36_20R FENOL group), Grant No. FIS2017-87519-P funded by Spanish MINECO, and Grant No. PID2020-113582GB-I00 funded by MCIN/AEI/10.13039/501100011033. E.H-G., S.M., and V.C-S. acknowledge the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (Grant No. MDM-2017-0711) funded by MCIN/AEI/10.13039/501100011033. S.M. and V.C-S. acknowledge the Spanish State Research Agency through Project No. PID2021-122256NB-C22 funded by MCIN/AEI/10.13039/501100011033/FEDER, UE. V.C-S. acknowledges support from Grant No. FPI/2257/2019 Conv. 201, CAIB Ph.D program. N.K. acknowledges support by Rey Juan Carlos University (Grant No. M2605) and by Community of Madrid and Rey Juan Carlos University through Young Researchers program in R&D (Grant No. CCASSE M2737).

Trabajo presentado en la Conference on Complex Systems (CCS), celebrada en Lyon del 25 al 29 de octubre de 2021., Human perceptual and cognitive abilities are limited resources. Today, in the age of cheap information -cheap to manipulate, to disseminate-, this cognitive bottleneck translates into hypercompetition for rewarding outcomes (acceptance, recognition) among actors. These incentives -summarised as visibility- pushes actors to mutualistically interact with specific memes, seeking the virality of their messages. Contents are driven by the evolving context, i.e. the chances to persist and reach widely are tightly subject to changes in the communication environment. Here, we show that the underlying architecture of the actors-memes interactions in information ecosystems, evolves towards emergent patterns, reminiscent of those found in natural ecosystems. By the analysis of Twitter data streams (Fig. 1(top-left)) we show that communication systems are structurally flexible, i.e. fluctuating from modular to nested, and back to modular, architectures as a response to environmental perturbations. We then propose an ecology-inspired modelling framework, bringing to light the precise mechanisms causing the observed topological reorganization Fig. 1(top-right). The model builds on the idea that the actor-meme network structure is effectively driven by an optimization process [2], aiming at the maximization of visibility, and that each actor and meme specie has assigned a niche profile[1], that represents their topical domain. Results from the numerical simulations predict -and the data confirmthat the users’ struggle for visibility induces a re-equilibration of the network’s mesoscale towards a transition from meso- to macroscale nested arrangements, top and bottom subpanels in Fig. 1(top-left) and (top-right), respectively. Finally, results at the microscale suggests that flexibility at the structural level is not mirrored at the dynamical one, where environmental shocks do leave a trace Fig. 1(bottom).

The vast amount of research devoted to public goods games has shown that contributions may be dramatically affected by varying framing conditions. This is particularly relevant in the context of donations to charities and non-governmental organizations. Here, we design a multiple public goods experiment by introducing five types of funds, each differing in the fraction of the contribution that is donated to a charity. We found that people contribute more to public goods when the associated social donations are presented as indirect rather than as direct donations. At the same time, the fraction of the donations devoted to charity is not affected by the framing. We have also found that, on average, women contribute to public goods and donate to charity significantly more than men. These findings are of potential interest to the design of social investment tools, in particular for charities to ask for better institutional designs from policy makers., This work was partially supported by MINECO (Spain) and FEDER funds through grant no. FIS2017-87519-P (Y.M.) and FJCI-2016-28276 (A.A.); by Ministerio de Ciencia, Innovación y Universidades/FEDER (Spain/UE) through grant no. PGC2018-098186-B-I00 (BASIC) (A.S. and J.C.); by Comunidad de Madrid under grant no. PRACTICO-CM and by Comunidad de Madrid/Universidad Carlos III de Madrid under grant no. CAVTIONS-CM-UC3M (A.S.); by Comunidad de Aragón (Spain) through grant no. E36-20R to FENOL (C.G.L. and Y.M.); by the EU through FET-Proactive Project MULTIPLEX (contract no. 317532, Y.M.) and FET-Proactive Project DOLFINS (contract no. 640772, C.G.L., Y.M. and A.S.), and by the Spanish State Research Agency and FEDER funds, through the María de Maeztu Program for Units of Excellence in R&D (MDM-2017-0711, S.M.) and under the PACSS grant no. (RTI2018-093732-B-C22, S.M.).

Online social networks have significantly changed global communication dynamics. Despite the complexity of such dynamics, emergent patterns from empirical microblogging data on hashtag usage, such as meme popularity power-law distributions, have been observed. However, going beyond empirical or simulation-based studies, highlighting and distinguishing fundamental mechanisms at the origin of such patterns are still open problems. Herein, inspired by the neutral theory of ecology, we provide an analytical formalism capable of capturing the competition for attention in online social systems and explaining several emergent patterns observed in the context of information ecosystems. Specifically, besides meme popularity distributions, we study the persistence distributions of memes within the network and the capacity of the system to sustain several coexisting diverse memes. Such properties have a clear analogy to extinction time and biodiversity in natural ecosystems. In the social network context, diversity of ideas is indeed an identifying characteristic of the “health” of the communication process. We show how our theoretical framework is successful in explaining real data collected from the Twitter online social network. The proposed framework introduced here provides both insight into the roles played by competition and network structure on meme diversity and persistence, and an applicable null model to be tested against real data., This project was funded by the Cariparo Foundation Visiting Program. S.S. and C.A.P. acknowledge UNIPD STARS_Stg grant BioReACT. S.M. and V.C.-S. also acknowledge support from the Spanish Ministry of Science and Innovation, the AEI, and FEDER (EU) through PACSS (Grant No. RTI2018-093732-B-C22) and the Maria de Maeztu program for Units of Excellence in R&D (Grant No. MDM-2017-0711). M.J.P, A.S-R., and J.B-H. acknowledge support from the Spanish MICINN Project No. PGC2018-096999-A-I00. M.J.P. acknowledges as well support from the Universitat Oberta de Catalunya through a doctoral grant. S.S. acknowledges DFA and UNIPD for SUWE_BIRD2020_01 grant. C.A.P. acknowledges the support provided by grant No. ANR-18-CE30-0013 from the Agence Nationale de la Recherche.

The ongoing SARS-CoV-2 pandemic has been holding the world hostage for more than a year now. Mobility is key to viral spreading and its restriction is the main non-pharmaceutical interventions to fight the virus expansion. Previous works have shown a connection between the structural organization of cities and the movement patterns of their residents. This puts urban centers in the focus of epidemic surveillance and interventions. Here we show that the organization of urban flows has a tremendous impact on disease spreading and on the amenability of different mitigation strategies. By studying anonymous and aggregated intra-urban flows in a variety of cities in the United States and other countries, and a combination of empirical analysis and analytical methods, we demonstrate that the response of cities to epidemic spreading can be roughly classified in two major types according to the overall organization of those flows. Hierarchical cities, where flows are concentrated primarily between mobility hotspots, are particularly vulnerable to the rapid spread of epidemics. Nevertheless, mobility restrictions in such types of cities are very effective in mitigating the spread of a virus. Conversely, in sprawled cities which present many centers of activity, the spread of an epidemic is much slower, but the response to mobility restrictions is much weaker and less effective. Investing resources on early monitoring and prompt ad-hoc interventions in more vulnerable cities may prove helpful in containing and reducing the impact of future pandemics., M.M. is funded by the Conselleria d’Innovació, Recerca i Turisme of the Government of the Balearic Islands and the European Social Fund with grant code FPI/2090/2018. J.A., M.M., S.Meloni and J.J.R. also acknowledge funding from the project Distancia-COVID (CSIC-COVID-19) of the CSIC funded by a contribution of AENA, from the project PACSS RTI2018-093732-B-C22 of the MCIN/AEI/10.13039/501100011033/ and by EU through FEDER funds (A way to make Europe), and also from the Maria de Maeztu program MDM-2017-0711 of the MCIN/AEI/10.13039/501100011033/. M.M. acknowledges the financial support of the Sorbonne Université Emergence project RISKFLOW. A.B. and V.N. acknowledge support from the UK EPSRC New Investigator Award Grant No. EP/S027920/1. G.G., S.H. and S. Mimar acknowledge support from from NSF Grant IIS-2029095 and the US Army Research Office under Agreement Number W911NF-18-1-0421. A.K. is supported by the National Defense Science and Engineering Graduate Fellowship through the Department of Defense.

Here we develop an epidemic model that accounts for long-range dispersal of pathogens between plants. This model generalizes the classical compartmental models–Susceptible-Infected-Susceptible (SIS) and Susceptible-Infected-Recovered (SIR)–to take into account those factors that are key to understand epidemics in real plant populations. These ingredients are the spatial characteristics of the plots and fields in which plants are embedded and the effect of long-range dispersal of pathogens. The spatial characteristics are included through the use of random rectangular graphs which allow to consider the effects of the elongation of plots and fields, while the long-range dispersal is implemented by considering transformations, such as the Mellin and Laplace transforms, of a generalization of the adjacency matrix of the geometric graph. Our results point out that long-range dispersal favors the propagation of pathogens while the elongation of plant plots increases the epidemic threshold and decreases dramatically the number of affected plants. Interestingly, our model is able of reproducing the existence of patchy regions of infected plants and the absence of a clear propagation front centered in the initial infected plants, as it is observed in real plant epidemics.

Understanding human interactions in online communications is of paramount importance for our society. Alarming phenomena such as the spreading of fake news or the formation of echo-chambers can emerge in unhealthy communication environments and, ultimately, undermine the democratic discourse. In this context, unveiling the individual drivers that give rise to collective attention can help to conserve the health of our information ecosystems. Here, following a recently proposed analogy between natural and information ecosystems, we explore how competition for attention in online social networks and the strategies adopted by the users to maximize their visibility shape our communication dynamics. Specifically, by analyzing large-scale datasets from the micro-blogging platform Twitter and performing numerical modeling of the system dynamics, we are able to measure the amount of competition for attention experienced by users and how it changes when exogenous events captivate collective attention. The work relies on topic modeling to extract users' interests and memes context from the data and a framework based on ecological niche theory to quantify the strength of negative (competitive) and positive (mutualistic) interactions for both users and memes. Interestingly, our findings show two different behaviors. While memes undergo a sharp increase in competition during exceptional events that can lead to their extinction, users perceive a decrease in effective competition due to a stronger effect of mutualistic interaction, explaining the focus of collective attention around specific topics. Finally, to confirm our results we reproduce the observed shifts with a data-driven model of species dynamics., MJP, AS-R and JB-H acknowledge the support of the Spanish MICINN Project PGC2018-096999-A-I00. EP acknowledges a fellowship funded by the Stazione Zoologica Anton Dohrn (SZN) within the SZN-Open University PhD program. SS thanks the support of UNIPD through ReACT Stars 2018 grant and INFN LINCOLN grant. SM and VC-S acknowledge funding from the project PACSS RTI2018-093732-B-C22 of the MCIN/AEI /10.13039/501100011033/ and by EU through FEDER funds (Away to make Europe), and also from the Maria de Maeztu program MDM-2017-0711 of the MCIN/AEI /10.13039/501100011033. All authors acknowledge the support to the TEAMS project of the Cariparo Visiting Program 2018 (Padova, Italy).

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. High mobility between areas contribute to the importation of cases, affecting the spread of the disease. While many factors influence local incidence and making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can give rise to autonomous outbreaks that impact separate areas of the contact (social) network. Such mechanism has the potential to boost local incidence and size, making control and tracing measures less effective. In Spain, the high heterogeneity in incidence between similar areas despite the uniform mobility control measures taken suggests that multi-seeding could have played an important role in shaping the spreading of the disease. In this work, we focus on the spreading of SARS-CoV-2 among the $52$ Spanish provinces, showing that local incidence strongly correlates with mobility occurred in the early-stage weeks from and to Madrid, the main mobility hub and where the initial local outbreak unfolded. These results clarify the higher order effects that mobility can have on the evolution of an epidemic and highlight the relevance of its control., M.M. is funded by the Conselleria d’Innovaci´o, Recerca i Turisme of the Government of the Balearic Islands and the European Social Fund with grant code FPI/2090/2018. M.M., S.M. and J.J.R. also acknowledge funding from the project Distancia-COVID (CSICCOVID-19) of the CSIC funded by a contribution of AENA, from the Spanish Ministry of Science, Innovation and Universities, the AEI and FEDER (EU) under the grant PACSS (RTI2018-093732-B-C22) and the Maria de Maeztu program for Units of Excellence in R&D (MDM2017-0711).

Vector-borne epidemics are progressively becoming a global burden, especially those related to flaviviruses, and the effects of different factors such as climate change or the increase of human mobility can sensibly increase the population at risk worldwide. Such outbreaks are the result of the combination of different factors including crossed contagions between humans and vectors, their demographic distribution and human mobility among others. The current availability of information about all those ingredients demands their incorporation into current mathematical models for vector-borne disease transmission. Here, relying on a Markovian formulation of the metapopulation dynamics, we propose a framework that explicitly includes human-vector interactions, mobility, and demography. The analysis of the framework allows us not only to derive an expression of the epidemic threshold capturing the conditions for the onset of the epidemics but also to highlight some unseen features of vector-borne epidemics, such as abrupt changes in the unfolding patterns of the disease for small variations of the degree of mobility. Finally, driven by these insights, we obtain a prevalence indicator to rank populations according to their risk of being affected by a vector-borne disease. We illustrate the utility of this indicator by reproducing the spatial distribution Dengue cases reported in the city of Santiago de Cali (Colombia) from 2015 to 2016., This work was partially supported by Universidad del Valle (grant CI-165), Gobierno de Aragon/Fondo Social Europeo (Grant No. E36-17R), Ministerio de Economia, Industria y Competitividad (MINECO) and Fondo Europeo de Desarrollo Regional (FEDER) (Grants No. FIS2015-71582-C2 and No. FIS2017-87519-P) and by the Spanish Ministry of Science, Innovation and Universities, the National Agency for Research Funding AEI and FEDER (EU) under the grant PACSS (RTI2018-093732-B-C22) and the Maria de Maeztu program for Units of Excellence in R&D (MDM-2017-0711).

In the book The Essential Tension (1979) Thomas Kuhn described the conflict between tradition and innovation in scientific research—i.e., the desire to explore new promising areas, counterposed to the need to capitalize on the work done in the past. While it is probable that along their careers many scientists felt this tension, only few works have tried to quantify it. Here, we address this question by analyzing a large-scale dataset, containing all the papers published by the American Physical Society (APS) in 26 years, which allows for a better understanding of scientists’ careers evolution in Physics. We employ the Physics and Astronomy Classification Scheme (PACS) present in each paper to map the scientific interests of 103,246 authors and their evolution along the years. Our results indeed confirm the existence of the “essential tension” with scientists balancing between exploring the boundaries of their area and exploiting previous work. In particular, we found that although the majority of physicists change the topics of their research, they stay within the same broader area thus exploring with caution new scientific endeavors. Furthermore, we quantify the flows of authors moving between different subfields and pinpoint which areas are more likely to attract or donate researchers to the other ones. Overall, our results depict a very distinctive portrait of the evolution of research interests in Physics and can help in designing specific policies for the future., This material is based upon work supported by, or in part by, the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-18-1-0376. A.A. acknowledges support from Santander via the “Universities International Mobility Awards “program and of the FPI doctoral fellowship program from MINECO, Spain (grant FIS2014-55867-P). S.M. acknowledges partial financial support from the Agencia Estatal de Investigacion (AEI, Spain) and Fondo Europeo de Desarrollo Regional under Project PACSS Project No. RTI2018-093732-B-C22 (MCIU, AEI/FEDER,UE) and through the María de Maeztu Program for units of Excellence in R&D (MDM-2017-0711). Y.M. acknowledges partial support from the Government of Aragón, Spain through a grant to the group FENOL (E36-17R), by MINECO and FEDER funds (grant FIS2017-87519-P) and by Intesa Sanpaolo Innovation Center.

The simultaneous emergence of several abrupt disease outbreaks or the extinction of some serotypes of multi-strain 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., 9 pages, 5 figures, 64 refs

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries., The authors declare no competing financial interests. D.Pa. and D.Pe. acknowledge support from H2020 FETPROACT-GSS CIMPLEX Grant No. 641191. KK, CC, D.Pa., D.Pe., Y.M. and M.D. acknowledge support from the Lagrange Project of the Institute for Scientific Interchange Foundation (ISI Foundation) funded by Fondazione Cassa di Risparmio di Torino (Fondazione CRT). Y.M. acknowledges support from the Government of Aragon, Spain through a grant to the group FENOL and by Ministry of Economy and Competitiveness (MINECO) and European Regional Development Fund (FEDER) (Grant No. FIS2017-87519-P). S.M. acknowledges support from the Spanish State Research Agency, through the María de Maeztu Program for Units of Excellence in R&D (MDM-2017-0711 to the IFISC Institute).

The networked structure of contacts shapes the spreading of epidemic processes. Recent advances on network theory have improved our understanding of the epidemic processes at large scale. The relevance of several considerations still needs to be evaluated in the study of epidemic spreading. One of them is that of accounting for the influence of origin and destination patterns in the flow of the carriers of an epidemic. Here we compute origin-destination patterns compatible with empirical data of coarse grained flows in the air transportation network. We study the incidence of epidemic processes in a metapopulation approach considering different alternatives to the flows prior knowledge. The data-driven scenario where the estimation of origin and destination flows is considered turns out to be relevant to assess the impact of the epidemics at a microscopic level (in our scenario, which populations are infected). However, this information is irrelevant to assess its macroscopic incidence (fraction of infected populations). These results are of interest to implement even better computational platforms to forecast epidemic incidence., his work has been partially supported by MINECO through grant FIS2015-71582-C2-1 (A.A. and S.G.), Generalitat de Catalunya project 2017-SGR-896 (A.A. and S.G.), and Universitat Rovira i Virgili projects 2017PFR-URV-B2-41 (A.A. and S.G.) and 2017PFR-URV-B2-29 (A.F.). S.M. acknowledges support from the Spanish State Research Agency, through the María de Maeztu Program for Units of Excellence in R&D (MDM-2017-0711 to the IFISC Institute). A.A. acknowledges financial support from ICREA Academia and the James S. McDonnell Foundation.

Public urban mobility systems are composed by several transportation modes connected together. Most studies in urban mobility and planning often ignore the multi-layer nature of transportation systems considering only aggregated versions of this complex scenario. In this work we present a model for the representation of the transportation system of an entire city as a multiplex network. Using two different perspectives, one in which each line is a layer and one in which lines of the same transportation mode are grouped together, we study the interconnected structure of 9 different cities in Europe raging from small towns to mega-cities like London and Berlin highlighting their vulnerabilities and possible improvements. Finally, for the city of Zaragoza in Spain, we also consider data about service schedule and waiting times, which allow us to create a simple yet realistic model for urban mobility able to reproduce real-world facts and to test for network improvements., 12 pages including figures. Submitted for publication

We investigate the effects of delaying the time to recovery (delayed recovery) and of nonuniform transmission on the propagation of diseases on structured populations. Through a mean-field approximation and large-scale numerical simulations, we find that postponing the transition from the infectious to the recovered states can largely reduce the epidemic threshold, therefore promoting the outbreak of epidemics. On the other hand, if we consider nonuniform transmission among individuals, the epidemic threshold increases, thus inhibiting the spreading process. When both mechanisms are at work, the latter might prevail, hence resulting in an increase of the epidemic threshold with respect to the standard case, in which both ingredients are absent. Our findings are of interest for a better understanding of how diseases propagate on structured populations and to a further design of efficient immunization strategies., Highlights ► We present a novel epidemic model which simultaneously considers the delayed recovery and non-uniform transmission effect. ► Mean-field approximation is used to derive the critical threshold of the epidemic model. ► Numerical results well agree with the mean-field result of critical threshold. ► Compared to the standard model, the delayed recovery can largely promote the outbreak of epidemics. ► Compared to the standard model, the non-uniform transmission can inhibit the outbreak of epidemics.

An active participation of players in evolutionary games depends on several factors, ranging from personal stakes to the properties of the interaction network. Diverse activity patterns thus have to be taken into account when studying the evolution of cooperation in social dilemmas. Here we study the weak prisoner's dilemma game, where the activity of each player is determined in a probabilistic manner either by its degree or by its payoff. While degree-correlated activity introduces cascading failures of cooperation that are particularly severe on scale-free networks with frequently inactive hubs, payoff-correlated activity provides a more nuanced activity profile, which ultimately hinders systemic breakdowns of cooperation. To determine optimal conditions for the evolution of cooperation, we introduce an exponential decay to payoff-correlated activity that determines how fast the activity of a player returns to its default state. We show that there exists an intermediate decay rate, at which the resolution of the social dilemma is optimal. This can be explained by the emerging activity patterns of players, where the inactivity of hubs is compensated effectively by the increased activity of average-degree players, who through their collective influence in the network sustain a higher level of cooperation. The sudden drops in the fraction of cooperators observed with degree-correlated activity therefore vanish, and so does the need for the lengthy spatiotemporal reorganization of compact cooperative clusters. The absence of such asymmetric dynamic instabilities thus leads to an optimal resolution of social dilemmas, especially when the conditions for the evolution of cooperation are strongly adverse., 8 two-column pages, 6 figures; accepted for publication in Physical Review E

Rapidly mutating pathogens may be able to persist in the population and reach an endemic equilibrium by escaping hosts' acquired immunity. For such diseases, multiple biological, environmental and population-level mechanisms determine the dynamics of the outbreak, including pathogen's epidemiological traits (e.g. transmissibility, infectious period and duration of immunity), seasonality, interaction with other circulating strains and hosts' mixing and spatial fragmentation. Here, we study a susceptible-infected-recovered-susceptible model on a metapopulation where individuals are distributed in subpopulations connected via a network of mobility flows. Through extensive numerical simulations, we explore the phase space of pathogen's persistence and map the dynamical regimes of the pathogen following emergence. Our results show that spatial fragmentation and mobility play a key role in the persistence of the disease whose maximum is reached at intermediate mobility values. We describe the occurrence of different phenomena including local extinction and emergence of epidemic waves, and assess the conditions for large scale spreading. Findings are highlighted in reference to previous works and to real scenarios. Our work uncovers the crucial role of hosts' mobility on the ecological dynamics of rapidly mutating pathogens, opening the path for further studies on disease ecology in the presence of a complex and heterogeneous environment., 29 pages, 7 figures. Submitted for publication

The use of network theory to model disease propagation on populations introduces important elements of reality to the classical epidemiological models. The use of random geometric graphs (RGG) is one of such network models that allows for the consideration of spatial properties on disease propagation. In certain real-world scenarios -like in the analysis of a disease propagating through plants- the shape of the plots and fields where the host of the disease is located may play a fundamental role on the propagation dynamics. Here we consider a generalization of the RGG to account for the variation of the shape of the plots/fields where the hosts of a disease are allocated. We consider a disease propagation taking place on the nodes of a random rectangular graph (RRG) and we consider a lower bound for the epidemic threshold of a Susceptible-Infected-Susceptible (SIS) or Susceptible-Infected-Recovered (SIR) model on these networks. Using extensive numerical simulations and based on our analytical results we conclude that (ceteris paribus) the elongation of the plot/field in which the nodes are distributed makes the network more resilient to the propagation of a disease due to the fact that the epidemic threshold increases with the elongation of the rectangle. These results agree with accumulated empirical evidence and simulation results about the propagation of diseases on plants in plots/fields of the same area and different shapes., Version 4, 13 pages, 6 figures, 44 refs