Complexity Digest

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High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2

5 hours 31 min ago

Steven Sanche, Yen Ting Lin, Chonggang Xu, Ethan Romero-Severson, Nick Hengartner, and Ruian Ke

Emerging Infectious Diseases journal – CDC, 26(7)

 

Severe acute respiratory syndrome coronavirus 2 is the causative agent of the 2019 novel coronavirus disease pandemic. Initial estimates of the early dynamics of the outbreak in Wuhan, China, suggested a doubling time of the number of infected persons of 6–7 days and a basic reproductive number (R0) of 2.2–2.7. We collected extensive individual case reports across China and estimated key epidemiologic parameters, including the incubation period. We then designed 2 mathematical modeling approaches to infer the outbreak dynamics in Wuhan by using high-resolution domestic travel and infection data. Results show that the doubling time early in the epidemic in Wuhan was 2.3–3.3 days. Assuming a serial interval of 6–9 days, we calculated a median R0 value of 5.7 (95% CI 3.8–8.9). We further show that active surveillance, contact tracing, quarantine, and early strong social distancing efforts are needed to stop transmission of the virus.

Source: wwwnc.cdc.gov

The Emergence of Informative Higher Scales in Complex Networks

8 hours 32 min ago

Brennan Klein and Erik Hoel

Complexity Volume 2020 |Article ID 8932526

 

The connectivity of a network contains information about the relationships between nodes, which can denote interactions, associations, or dependencies. We show that this information can be analyzed by measuring the uncertainty (and certainty) contained in paths along nodes and links in a network. Specifically, we derive from first principles a measure known as effective information and describe its behavior in common network models. Networks with higher effective information contain more information in the relationships between nodes. We show how subgraphs of nodes can be grouped into macronodes, reducing the size of a network while increasing its effective information (a phenomenon known as causal emergence). We find that informative higher scales are common in simulated and real networks across biological, social, informational, and technological domains. These results show that the emergence of higher scales in networks can be directly assessed and that these higher scales offer a way to create certainty out of uncertainty.

Source: www.hindawi.com

Addressing climate change post-coronavirus

Tue, 04/07/2020 - 12:26

The ongoing crisis holds profound lessons that can help us address climate change post-coronavirus–if we make greater economic and environmental plans for the recovery ahead.

Source: www.mckinsey.com

Guiding the Self-Organization of Cyber-Physical Systems

Mon, 04/06/2020 - 17:29

Carlos Gershenson

Front. Robot. AI, 03 April 2020 

 

Self-organization offers a promising approach for designing adaptive systems. Given the inherent complexity of most cyber-physical systems, adaptivity is desired, as predictability is limited. Here I summarize different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design. Then I mention real-world examples of systems where self-organization has managed to provide solutions that outperform classical approaches, in particular related to urban mobility. Finally, I identify when a centralized, distributed, or self-organizing control is more appropriate.

Source: www.frontiersin.org

(So) Big Data and the transformation of the city 

Sat, 04/04/2020 - 09:02

Gennady Andrienko, Natalia Andrienko, Chiara Boldrini, Guido Caldarelli, Paolo Cintia, Stefano Cresci, Angelo Facchini, Fosca Giannotti, Aristides Gionis, Riccardo Guidotti, Michael Mathioudakis, Cristina Ioana Muntean, Luca Pappalardo, Dino Pedreschi, Evangelos Pournaras, Francesca Pratesi, Maurizio Tesconi & Roberto Trasarti 

International Journal of Data Science and Analytics (2020)

 

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.

Source: link.springer.com

Crime and its fear in social media

Fri, 04/03/2020 - 12:40

Rafael Prieto Curiel, Stefano Cresci, Cristina Ioana Muntean & Steven Richard Bishop 
Palgrave Communications volume 6, Article number: 57 (2020)

 

Social media posts incorporate real-time information that has, elsewhere, been exploited to predict social trends. This paper considers whether such information can be useful in relation to crime and fear of crime. A large number of tweets were collected from the 18 largest Spanish-speaking countries in Latin America, over a period of 70 days. These tweets are then classified as being crime-related or not and additional information is extracted, including the type of crime and where possible, any geo-location at a city level. From the analysis of collected data, it is established that around 15 out of every 1000 tweets have text related to a crime, or fear of crime. The frequency of tweets related to crime is then compared against the number of murders, the murder rate, or the level of fear of crime as recorded in surveys. Results show that, like mass media, such as newspapers, social media suffer from a strong bias towards violent or sexual crimes. Furthermore, social media messages are not highly correlated with crime. Thus, social media is shown not to be highly useful for detecting trends in crime itself, but what they do demonstrate is rather a reflection of the level of the fear of crime.

Source: www.nature.com

Strategies for controlling the medical and socio-economic costs of the Corona pandemic

Thu, 04/02/2020 - 14:33

Claudius Gros, Roser Valenti, Kilian Valenti, Daniel Gros

 

In response to the rapid spread of the Coronavirus (COVID-19), with ten thousands of deaths and intensive-care hospitalizations, a large number of regions and countries have been put under lockdown by their respective governments. Policy makers are confronted in this situation with the problem of balancing public health considerations, with the economic costs of a persistent lockdown. We introduce a modified epidemic model, the controlled-SIR model, in which the disease reproduction rates evolve dynamically in response to political and societal reactions. Social distancing measures are triggered by the number of infections, providing a dynamic feedback-loop which slows the spread of the virus. We estimate the total cost of several distinct containment policies incurring over the entire path of the endemic. Costs comprise direct medical cost for intensive care, the economic cost of social distancing, as well as the economic value of lives saved. Under plausible parameters, the total costs are highest at a medium level of reactivity when value of life costs are omitted. Very strict measures fare best, with a hands-off policy coming second. Our key findings are independent of the specific parameter estimates, which are to be adjusted with the COVID-19 research status. In addition to numerical simulations, an explicit analytical solution for the controlled continuous-time SIR model is presented. For an uncontrolled outbreak and a reproduction factor of three, an additional 28% of the population is infected beyond the herd immunity point, reached at an infection level of 66%, which adds up to a total of 94%.

Source: arxiv.org

Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome

Thu, 04/02/2020 - 10:45

Alejandro Morales and Tom Froese

Front. Robot. AI, 02 April 2020

 

Modeling of complex adaptive systems has revealed a still poorly understood benefit of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor configurations, they begin to reorganize their connectivity in a direction that minimizes the coordination constraints posed by the initial network architecture. This self-optimization process has been replicated in various neural network formalisms, but it is still unclear whether it can be applied to biologically more realistic network topologies and scaled up to larger networks. Here we continue our efforts to respond to these challenges by demonstrating the process on the connectome of the widely studied nematode worm C. elegans. We extend our previous work by considering the contributions made by hierarchical partitions of the connectome that form functional clusters, and we explore possible beneficial effects of inter-cluster inhibitory connections. We conclude that the self-optimization process can be applied to neural network topologies characterized by greater biological realism, and that long-range inhibitory connections can facilitate the generalization capacity of the process.

Source: www.frontiersin.org

Swarm Robotic Behaviors and Current Applications

Thu, 04/02/2020 - 08:25

Melanie Schranz, Martina Umlauft, Micha Sende and Wilfried Elmenreich

Front. Robot. AI, 02 April 2020

 

In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.

Source: www.frontiersin.org

Relevance of temporal cores for epidemic spread in temporal networks

Tue, 03/31/2020 - 14:58

Martino Ciaperoni, Edoardo Galimberti, Francesco Bonchi, Ciro Cattuto, Francesco Gullo, Alain Barrat

 

Temporal networks are widely used to represent a vast diversity of systems, including in particular social interactions, and the spreading processes unfolding on top of them. The identification of structures playing important roles in such processes remain an open question, despite recent progresses in the case of static networks. Here, we consider as candidate structures the recently introduced concept of span-cores: the span-cores decompose a temporal network into subgraphs of controlled duration and increasing connectivity, generalizing the core-decomposition of static graphs. We explore the effectiveness of strategies aimed either at containing or maximizing the impact of a spread, based respectively on removing span-cores of high cohesiveness or duration to decrease the epidemic risk, or on seeding the process from such structures. The effectiveness of such strategies is assessed in a variety of empirical data sets and against a number of baselines that use only static information on the centrality of nodes and static concepts of coreness. Our results show that the removal of the most stable and cohesive temporal cores has a strong impact on epidemic processes on temporal networks, and that their nodes are likely to represent influential spreaders.

Source: arxiv.org

Real-time Epidemic Datathon

Tue, 03/31/2020 - 08:00

Real-time Epidemic Datathon is a collective open-source real-time forecasting challenge aimed at joining forces to push modeling limits further for real-time epidemic forecasting at large scale. Organized by ETH Zürich, UCLA, EU SoBigData++ project, NYU COURANT, and other partner organizations. The goal of this project is to bring together researchers and students from different disciplines (e.g., computer science, epidemiology, physics, statistics, applied math, …) and advance real-time epidemic modeling frameworks. We provide a platform for scientific exchange and discussion. Participating teams can submit predictions of COVID-19 case evolutions in different countries and evaluate/compare their modeling approaches.

Who can join? Everyone can join and contribute in various ways: (i) register as a developer (individual or with a team) of a real-time epidemic forecasting model, (ii) register and monitor scientific developments (see our disclaimer section), or (iii) share the news about this event and help us to reach more contributors.

Source: www.epidemicdatathon.com

Functional and Social Team Dynamics in Industrial Settings

Thu, 03/26/2020 - 10:14

Dominic E. Saadi, Mark Sutcliffe, Yaneer Bar-Yam, and Alfredo J. Morales

Complexity Volume 2020 |Article ID 8301575

 

Like other social systems, corporations comprise networks of individuals that share information and create interdependencies among their actions. The properties of these networks are crucial to a corporation’s success. Understanding how individuals self-organize into teams and how this relates to performance is a challenge for managers and management software developers looking for ways to enhance corporate tasks. In this paper, we analyze functional and social communication networks from industrial production plants and relate their properties to performance. We use internal management software data that reveal aspects of functional and social communications among workers. We found that distinct features of functional and social communication networks emerge. The former are asymmetrical, and the latter are segregated by job title, i.e., executives, managers, supervisors, and operators. We show that performance is negatively correlated with the volume of functional communications but positively correlated with the density of the emerging communication networks. Exposing social dynamics in the workplace matters given the increasing digitization and automation of corporate tasks and managerial processes.

Source: www.hindawi.com

Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China

Wed, 03/25/2020 - 13:40

Juanjuan Zhang, Maria Litvinova, Yuxia Liang, Yan Wang, Wei Wang, Shanlu Zhao, Qianhui Wu, Stefano Merler, Cecile Viboud, Alessandro Vespignani, Marco Ajelli, Hongjie Yu

 

Strict interventions were successful to control the novel coronavirus (COVID-19) outbreak in China. As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection and disease, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact surveys data for Wuhan and Shanghai before and during the outbreak and contact tracing information from Hunan Province. Daily contacts were reduced 7-9 fold during the COVID-19 social distancing period, with most interactions restricted to the household. Children 0-14 years were 59% (95% CI 7-82%) less susceptible than individuals 65 years and over. A transmission model calibrated against these data indicates that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. While proactive school closures cannot interrupt transmission on their own, they reduce peak incidence by half and delay the epidemic. These findings can help guide global intervention policies.

Source: www.medrxiv.org

To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation

Wed, 03/25/2020 - 09:30

Sven Tomforde and Martin Goller

Computers 2020, 9(1), 21

 

Self-adaptation and self-organization (SASO) have been introduced to the management of technical systems as an attempt to improve robustness and administrability. In particular, both mechanisms adapt the system’s structure and behavior in response to dynamics of the environment and internal or external disturbances. By now, adaptivity has been considered to be fully desirable. This position paper argues that too much adaptation conflicts with goals such as stability and user acceptance. Consequently, a kind of situation-dependent degree of adaptation is desired, which defines the amount and severity of tolerated adaptations in certain situations. As a first step into this direction, this position paper presents a quantification approach for measuring the current adaptation behavior based on generative, probabilistic models. The behavior of this method is analyzed in terms of three application scenarios: urban traffic control, the swidden farming model, and data communication protocols. Furthermore, we define a research roadmap in terms of six challenges for an overall measurement framework for SASO systems.

Source: www.mdpi.com

Effectiveness of social distancing strategies for protecting a community from a pandemic with a data- driven contact network based on census and real-world mobility data

Wed, 03/25/2020 - 09:18

David Martín-Calvo, Alberto Aleta, Alex Pentland, Yamir Moreno, Esteban Moro

 

The current situation of emergency is global. As of today, March 22nd 2020, there are more than 23 countries with more than 1.000 infected cases by COVID-19, in the exponential growth phase of the disease. Furthermore, there are different mitigation and suppression strategies in place worldwide, but many of them are based on enforcing, to a more or less extent, the so-called social distancing. The impact and outcomes of the adopted measures are yet to be contrasted and quantified. Therefore, realistic modeling approaches could provide important clues about what to expect and what could be the best course of actions. Such modeling efforts could potentially save thousands, if not millions of lives. Our report contains preliminary results that aim at answering the following questions in relation to the spread and control of the COVID-19 pandemic:
– What is the expected impact of current social distancing strategies?
– How long should such measures need to be in place?
– How many people will be infected and at which social level?
– How do R(t) and the epidemic dynamic change based on the adopted strategies?
– What is the probability of having a second outbreak, i.e., a reemergence?
– If there is a reemergence, how much time do we have to get ready?
– What is the best strategy to minimize the current epidemic and get ready for a second wave?
In this report, we provide details of the data analyzed, the methodology (and its limitations) employed as well as a quantitative and qualitative assessment of strategies based on social distancing and corresponding what-if-scenarios for control and mitigation. We use real world mobility and census data of the Boston area to build a co-location network at three different layers (community, households and schools), and a data-driven SEIR model that allows testing six different social distancing strategies, namely, (i) school closures, (ii) self-distancing and teleworking, (iii) self-distancing and teleworking plus School closure (iv) Restaurants, nightlife and cultural closures, (v) non-essential workplace closures and (vi) total confinement. We test the impact of establishing these strategies at different stages of the epidemic evolution and for different periods of time.

Source: covid-19-sds.github.io

ccnr covid-19 research

Wed, 03/25/2020 - 09:15

Network Medicine offers a series of powerful tools to identify new drugs and diagnostics. In this exceptional moment of need, we decided to turn the BarabasiLab’s intellectual resources and network medicine toolset to aid the hunt for a treatment for the COVID-19.

Source: covid.barabasilab.com

Distinguishing cell phenotype using cell epigenotype

Tue, 03/24/2020 - 13:42

Thomas P. Wytock and Adilson E. Motter
Science Advances 6 (12), eaax7798 (2020)

Abstract. The relationship between microscopic observations and macroscopic behavior is a fundamental open question in biophysical systems. Here, we develop a unified approach that—in contrast with existing methods—predicts cell type from macromolecular data even when accounting for the scale of human tissue diversity and limitations in the available data. We achieve these benefits by applying a k-nearest-neighbors algorithm after projecting our data onto the eigenvectors of the correlation matrix inferred from many observations of gene expression or chromatin conformation. Our approach identifies variations in epigenotype that affect cell type, thereby supporting the cell-type attractor hypothesis and representing the first step toward model-independent control strategies in biological systems.

Source: advances.sciencemag.org

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics

Tue, 03/24/2020 - 12:19

Edmund R. Hunt

 

The real world is highly variable and unpredictable, and so fine-tuned robot controllers that successfully result in group-level “emergence” of swarm capabilities indoors may quickly become inadequate outside. One response to unpredictability could be greater robot complexity and cost, but this seems counter to the “swarm philosophy” of deploying (very) large numbers of simple agents. Instead, here I argue that bioinspiration in swarm robotics has considerable untapped potential in relation to the phenomenon of phenotypic plasticity: when a genotype can produce a range of distinctive changes in organismal behavior, physiology and morphology in response to different environments. This commonly arises following a natural history of variable conditions; implying the need for more diverse and hazardous simulated environments in offline, pre-deployment optimization of swarms. This will generate—indicate the need for—plasticity. Biological plasticity is sometimes irreversible; yet this characteristic remains relevant in the context of minimal swarms, where robots may become mass-producible. Plasticity can be introduced through the greater use of adaptive threshold-based behaviors; more fundamentally, it can link to emerging technologies such as smart materials, which can adapt form and function to environmental conditions. Moreover, in social animals, individual heterogeneity is increasingly recognized as functional for the group. Phenotypic plasticity can provide meaningful diversity “for free” based on early, local sensory experience, contributing toward better collective decision-making and resistance against adversarial agents, for example. Nature has already solved the challenge of resilient self-organisation in the physical realm through phenotypic plasticity: swarm engineers can follow this lead.

Source: www.frontiersin.org

Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarization

Tue, 03/24/2020 - 08:57

Elias Fernández Domingos, Jelena Grujić, Juan C. Burguillo, Georg Kirchsteiger, Francisco C. Santos, Tom Lenaerts

 

Human social dilemmas are often shaped by actions involving uncertain goals and returns that may only be achieved in the future. Climate action, voluntary vaccination and other prospective choices stand as paramount examples of this setting. In this context, as well as in many other social dilemmas, uncertainty may produce non-trivial effects. Whereas uncertainty about collective targets and their impact were shown to negatively affect group coordination and success, no information is available about timing uncertainty, i.e. how uncertainty about when the target needs to be reached affects the outcome as well as the decision-making. Here we show experimentally, through a collective dilemma wherein groups of participants need to avoid a tipping point under the risk of collective loss, that timing uncertainty prompts not only early generosity but also polarized contributions, in which participants’ total contributions are distributed more unfairly than when there is no uncertainty. Analyzing participant behavior reveals, under uncertainty, an increase in reciprocal strategies wherein contributions are conditional on the previous donations of the other participants, a group analogue of the well-known Tit-for-Tat strategy. Although large timing uncertainty appears to reduce collective success, groups that successfully collect the required amount show strong reciprocal coordination. This conclusion is supported by a game theoretic model examining the dominance of behaviors in case of timing uncertainty. In general, timing uncertainty casts a shadow on the future that leads participants to respond early, encouraging reciprocal behaviors, and unequal contributions.

Source: arxiv.org

Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarization

Tue, 03/24/2020 - 08:57

Elias Fernández Domingos, Jelena Grujić, Juan C. Burguillo, Georg Kirchsteiger, Francisco C. Santos, Tom Lenaerts

 

Human social dilemmas are often shaped by actions involving uncertain goals and returns that may only be achieved in the future. Climate action, voluntary vaccination and other prospective choices stand as paramount examples of this setting. In this context, as well as in many other social dilemmas, uncertainty may produce non-trivial effects. Whereas uncertainty about collective targets and their impact were shown to negatively affect group coordination and success, no information is available about timing uncertainty, i.e. how uncertainty about when the target needs to be reached affects the outcome as well as the decision-making. Here we show experimentally, through a collective dilemma wherein groups of participants need to avoid a tipping point under the risk of collective loss, that timing uncertainty prompts not only early generosity but also polarized contributions, in which participants’ total contributions are distributed more unfairly than when there is no uncertainty. Analyzing participant behavior reveals, under uncertainty, an increase in reciprocal strategies wherein contributions are conditional on the previous donations of the other participants, a group analogue of the well-known Tit-for-Tat strategy. Although large timing uncertainty appears to reduce collective success, groups that successfully collect the required amount show strong reciprocal coordination. This conclusion is supported by a game theoretic model examining the dominance of behaviors in case of timing uncertainty. In general, timing uncertainty casts a shadow on the future that leads participants to respond early, encouraging reciprocal behaviors, and unequal contributions.

Source: arxiv.org

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