Papers
525.6K views | +10 today
Follow
 
Scooped by Complexity Digest
onto Papers
Scoop.it!

Explosive synchronization in adaptive and multilayer networks

Explosive synchronization (ES) is nowadays a hot topic of interest in nonlinear science and complex networks. So far, it is conjectured that ES is rooted in the setting of specific microscopic correlation features between the natural frequencies of the networked oscillators and their effective coupling strengths. We show that ES, in fact, is far more general, and can occur in adaptive and multilayer networks also in the absence of such correlation properties. Precisely, we first report evidence of ES in the absence of correlation for networks where a fraction f of the nodes have links adaptively controlled by a local order parameter, and then we extend the study to a variety of two-layer networks with a fraction f of their nodes coupled each other by means of dependency links. In this latter case, we even show that ES sets in, regardless of the differences in the frequency distribution and/or in the topology of connections between the two layers. Finally, we provide a rigorous, analytical, treatment to properly ground all the observed scenario, and to facilitate the understanding of the actual mechanisms at the basis of ES in real-world systems.


Explosive synchronization in adaptive and multilayer networks
Xiyun Zhang, Stefano Boccaletti, Shuguang Guan, Zonghua Liu

http://arxiv.org/abs/1410.2986

No comment yet.
Papers
Recent publications related to complex systems
Your new post is loading...
Your new post is loading...
Scooped by Complexity Digest
Scoop.it!

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

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%.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Swarm Robotic Behaviors and Current Applications

Swarm Robotic Behaviors and Current Applications | Papers | Scoop.it

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Functional and Social Team Dynamics in Industrial Settings

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

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

To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation | Papers | Scoop.it

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Distinguishing cell phenotype using cell epigenotype

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarization

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Flattening the COVID-19 Curves

Flattening the COVID-19 Curves | Papers | Scoop.it

What is the best public policy to counter the health risk from the Coronavirus, COVID-19? This is the question on everyone’s mind.
It is wise to try and learn from the current situation in China, where the rate of COVID-19 infections was extinguished as a result of a lockdown, and Italy, where hospitals are full and doctors have to make life-death decisions about patients because there are not enough beds to treat everyone in need. The mortality fraction of infected people appears to be higher by an order of magnitude when hospitals are overcrowded, so suppressing the rate of new infections serves the important purpose of allowing those in need to be treated.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Complex Control and the Governmentality of Digital Platforms

Petter Törnberg and Justus Uitermark

Front. Sustain. Cities

 

Digital platforms are reshaping cities in the twenty-first century, providing not only new ways of seeing and navigating the world, but also new ways of organizing the economy, our cities and social lives. They bring great promises, claiming to facilitate a new “sharing” economy, outside of the exploitation of the market and the inefficiencies of the state. This paper reflects on this promise, and its associated notion of “self-organization,” by situating digital platforms in a longer history of control, discipline and surveillance. Using Foucault, Deleuze, and Bauman, we scrutinize the theoretical and political notion of “self-organization” and unpack its idealistic connotations: To what extent does self-organization actually imply empowerment or freedom? Who is the “self” in “self-organization,” and who is the user on urban digital platforms? Is self-organization necessarily an expression of the interests of the constituent participants? In this way, the paper broadens the analysis of neoliberal governmentalities to reveal the forms of power concealed under the narratives of “sharing” and “self-organization” of the platform era. We find that control is increasingly moving to lower-level strata, operating by setting the context and conditions for self-organization. Thus, the order of things emerge seemingly naturally from the rules of the game. This points to an emerging form of complex control, which has gone beyond the fast and flexible forms of digital control theorized by Deleuze.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Autocatalytic chemical networks at the origin of metabolism

Joana C. Xavier, Wim Hordijk, Stuart Kauffman, Mike Steel and William F. Martin

Proceedings of the Royal Society B: Biological Sciences

 

Modern cells embody metabolic networks containing thousands of elements and form autocatalytic sets of molecules that produce copies of themselves. How the first self-sustaining metabolic networks arose at life's origin is a major open question. Autocatalytic sets smaller than metabolic networks were proposed as transitory intermediates at the origin of life, but evidence for their role in prebiotic evolution is lacking. Here, we identify reflexively autocatalytic food-generated networks (RAFs)—self-sustaining networks that collectively catalyse all their reactions—embedded within microbial metabolism. RAFs in the metabolism of ancient anaerobic autotrophs that live from H2 and CO2 provided with small-molecule catalysts generate acetyl-CoA as well as amino acids and bases, the monomeric components of protein and RNA, but amino acids and bases without organic catalysts do not generate metabolic RAFs. This suggests that RAFs identify attributes of biochemical origins conserved in metabolic networks. RAFs are consistent with an autotrophic origin of metabolism and furthermore indicate that autocatalytic chemical networks preceded proteins and RNA in evolution. RAFs uncover intermediate stages in the emergence of metabolic networks, narrowing the gaps between early Earth chemistry and life.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

What China’s coronavirus response can teach the rest of the world

What China’s coronavirus response can teach the rest of the world | Papers | Scoop.it

As the new coronavirus marches around the globe, countries with escalating outbreaks are eager to learn whether China’s extreme lockdowns were responsible for bringing the crisis there under control. Other nations are now following China’s lead and limiting movement within their borders, while dozens of countries have restricted international visitors.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

School closures, event cancellations, and the mesoscopic localization of epidemics in networks with higher-order structure

The COVID-19 epidemic is challenging in many ways, perhaps most obvious are failures of the surveillance system. Consequently, the official intervention has focused on conventional wisdom --- social distancing, hand washing, etc. --- while critical decisions such as the cancellation of large events like festivals, workshops and academic conferences are done on a case-by-case basis with limited information about local risks. Adding to this uncertainty is the fact that our mathematical models tend to assume some level of random mixing patterns instead of the higher-order structures necessary to describe these large events. Here, we discuss a higher-order description of epidemic dynamics on networks that provides a natural way of extending common models to interaction beyond simple pairwise contacts. We show that unlike the classic diffusion of standard epidemic models, higher-order interactions can give rise to mesoscopic localization, i.e., a phenomenon in which there is a concentration of the epidemic around certain substructures in the network. We discuss the implications of these results and show the potential impact of a blanket cancellation of events larger than a certain critical size. Unlike standard models of delocalized dynamics, epidemics in a localized phase can suddenly collapse when facing an intervention operating over structures rather than individuals.

 

Guillaume St-Onge, Vincent Thibeault, Antoine Allard, Louis J. Dubé, Laurent Hébert-Dufresne

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Concepts in Boolean network modeling: What do they all mean?

Concepts in Boolean network modeling: What do they all mean? | Papers | Scoop.it

Julian D. Schwab, Silke D. Kühlwein, Nensi Ikonomi, Michael Kühl, Hans A. Kestler

Computational and Structural Biotechnology Journal

 

Boolean network models are one of the simplest models to study complex dynamic behavior in biological systems. They can be applied to unravel the mechanisms regulating the properties of the system or to identify promising intervention targets. Since its introduction by Stuart Kauffman in 1969 for describing gene regulatory networks, various biologically based networks and tools for their analysis were developed. Here, we summarize and explain the concepts for Boolean network modeling. We also present application examples and guidelines to work with and analyze Boolean network models.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Landmark Computer Science Proof Cascades Through Physics and Math

Landmark Computer Science Proof Cascades Through Physics and Math | Papers | Scoop.it

In 1935, Albert Einstein, working with Boris Podolsky and Nathan Rosen, grappled with a possibility revealed by the new laws of quantum physics: that two particles could be entangled, or correlated, even across vast distances.

The very next year, Alan Turing formulated the first general theory of computing and proved that there exists a problem that computers will never be able to solve.

These two ideas revolutionized their respective disciplines. They also seemed to have nothing to do with each other. But now a landmark proof has combined them while solving a raft of open problems in computer science, physics and mathematics.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

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

Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome | Papers | Scoop.it

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Relevance of temporal cores for epidemic spread in temporal networks

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

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

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

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

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.

No comment yet.
Suggested by Edmund
Scoop.it!

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics | Papers | Scoop.it

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.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Beauty in artistic expressions through the eyes of networks and physics

Matjaž Perc

Journal of The Royal Society Interface Volume 17 Issue 164

 

Beauty is subjective, and as such it, of course, cannot be defined in absolute terms. But we all know or feel when something is beautiful to us personally. And in such instances, methods of statistical physics and network science can be used to quantify and to better understand what it is that evokes that pleasant feeling, be it when reading a book or looking at a painting. Indeed, recent large-scale explorations of digital data have lifted the veil on many aspects of our artistic expressions that would remain forever hidden in smaller samples. From the determination of complexity and entropy of art paintings to the creation of the flavour network and the principles of food pairing, fascinating research at the interface of art, physics and network science abounds. We here review the existing literature, focusing in particular on culinary, visual, musical and literary arts. We also touch upon cultural history and culturomics, as well as on the connections between physics and the social sciences in general. The review shows that the synergies between these fields yield highly entertaining results that can often be enjoyed by layman and experts alike. In addition to its wider appeal, the reviewed research also has many applications, ranging from improved recommendation to the detection of plagiarism.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

We are creating conditions for diseases like COVID-19 to emerge

We are creating conditions for diseases like COVID-19 to emerge | Papers | Scoop.it

Increasingly, says Jones, these zoonotic diseases are linked to environmental change and human behavior. The disruption of pristine forests driven by logging, mining, road building through remote places, rapid urbanization and population growth is bringing people into closer contact with animal species they may never have been near before, she says.
The resulting transmission of disease from wildlife to humans, she says, is now “a hidden cost of human economic development. There are just so many more of us, in every environment. We are going into largely undisturbed places and being exposed more and more. We are creating habitats where viruses are transmitted more easily, and then we are surprised that we have new ones.”

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Forecasting of Population Narcotization under the Implementation of a Drug Use Reduction Policy

Sergey Mityagin, Carlos Gershenson, and Alexander Boukhanovsky

Complexity Volume 2020 |Article ID 9135024

 

In this paper, we present an approach to drug addiction simulation and forecasting in the medium and long terms in cities having a high population density and a high rate of social communication. Drug addiction forecasting is one of the basic components of the antidrug policy, giving informational and analytic support both at the regional and at the governmental level. However, views on the drug consumption problem vary in different regions, and as a consequence, several approaches to antidrug policy implementation exist. Thereby, notwithstanding the fact that the phenomenology of the population narcotization process is similar in the different regions, approaches to the modeling of drug addiction may also substantially differ for different kinds of antidrug policies. This paper presents a survey of the available antidrug policies and the corresponding approaches to the simulation of population narcotization. This article considers the approach to the construction of the regression model of anesthesia on the main components formed on the basis of indicators of social and economic development. The substantiation of the chosen method is given, which is associated with a significant correlation of indicators, which characterizes the presence of a small number of superfactors. This allows us to form a conclusion about the general level of development of the region as the main factor determining the drug addiction. A new model is proposed for one of the most widespread antidrug policies, namely, the drug use reduction policy. The model helps determine the significant factors of population narcotization and allows to estimate its damage. The model is tested successfully using St. Petersburg data.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)

Ruiyun Li, Sen Pei, Bin Chen, Yimeng Song, Tao Zhang, Wan Yang, Jeffrey Shaman

Science 16 Mar 2020:
eabb3221
DOI: 10.1126/science.abb3221

 

Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%–90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%–62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

COVID-19 outbreak response: first assessment of mobility changes in Italy following lockdown

COVID-19 outbreak response: first assessment of mobility changes in Italy following lockdown | Papers | Scoop.it

Emanuele Pepe, Paolo Bajardi, Laetitia Gauvin, Filippo Privitera, Ciro Cattuto, Michele Tizzoni

 

The mitigation measures enacted as part of the response to the unfolding SARS-CoV-2 pandemic are unprecedented in their breadth and societal burden. A major challenge in this situation is to quantitatively assess the impact of non-pharmaceutical interventions like mobility restrictions and social distancing, to better understand the ensuing reduction of mobility flows, individual mobility changes, and impact on contact patterns. Here we report preliminary results on tackling the above challenges by using de-identified, large-scale data from a location intelligence company, Cuebiq, that has instrumented smartphone apps with high-accuracy location-data collection software. We focus this initial analysis on Italy, where the COVID-19 epidemic has already triggered an unprecedented and escalating series of restrictions on travel and individual mobility of citizens.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Evolving Always-Critical Networks

Marco Villani , Salvatore Magrì, Andrea Roli and Roberto Serra

 

Living beings share several common features at the molecular level, but there are very few large-scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One interesting candidate is the “criticality” principle, which claims that biological evolution favors those dynamical regimes that are intermediaries between ordered and disordered states (i.e., “at the edge of chaos”). The reasons why this should be the case and experimental evidence are briefly discussed, observing that gene regulatory networks are indeed often found on, or close to, the critical boundaries. Therefore, assuming that criticality provides an edge, it is important to ascertain whether systems that are critical can further evolve while remaining critical. In order to explore the possibility of achieving such “always-critical” evolution, we resort to simulated evolution, by suitably modifying a genetic algorithm in such a way that the newly-generated individuals are constrained to be critical. It is then shown that these modified genetic algorithms can actually develop critical gene regulatory networks with two interesting (and quite different) features of biological significance, involving, in one case, the average gene activation values and, in the other case, the response to perturbations. These two cases suggest that it is often possible to evolve networks with interesting properties without losing the advantages of criticality. The evolved networks also show some interesting features which are discussed.

No comment yet.
Scooped by Complexity Digest
Scoop.it!

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak: an observational and modelling study

Shengjie Lai, Nick W Ruktanonchai, Liangcai Zhou, Olivia Prosper, Wei Luo, Jessica R Floyd, Amy Wesolowski, Chi Zhang, Xiangjun Du, Hongjie Yu, Andrew J Tatem

 

Background: The COVID-19 outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective. Quantitative research is still needed however to assess the efficacy of different candidate NPIs and their timings to guide ongoing and future responses to epidemics of this emerging disease across the World. Methods: We built a travel network-based susceptible-exposed-infectious-removed (SEIR) model to simulate the outbreak across cities in mainland China. We used epidemiological parameters estimated for the early stage of outbreak in Wuhan to parameterise the transmission before NPIs were implemented. To quantify the relative effect of various NPIs, daily changes of delay from illness onset to the first reported case in each county were used as a proxy for the improvement of case identification and isolation across the outbreak. Historical and near-real time human movement data, obtained from Baidu location-based service, were used to derive the intensity of travel restrictions and contact reductions across China. The model and outputs were validated using daily reported case numbers, with a series of sensitivity analyses conducted. Findings: We estimated that there were a total of 114,325 COVID-19 cases (interquartile range [IQR] 76,776 - 164,576) in mainland China as of February 29, 2020, and these were highly correlated (p<0.001, R2=0.86) with reported incidence. Without NPIs, the number of COVID-19 cases would likely have shown a 67-fold increase (IQR: 44 - 94), with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to prevent more infections than travel restrictions and contact reductions, but integrated NPIs would achieve the strongest and most rapid effect. If NPIs could have been conducted one week, two weeks, or three weeks earlier in China, cases could have been reduced by 66%, 86%, and 95%, respectively, together with significantly reducing the number of affected areas. However, if NPIs were conducted one week, two weeks, or three weeks later, the number of cases could have shown a 3-fold, 7-fold, and 18-fold increase across China, respectively. Results also suggest that the social distancing intervention should be continued for the next few months in China to prevent case numbers increasing again after travel restrictions were lifted on February 17, 2020. Conclusion: The NPIs deployed in China appear to be effectively containing the COVID-19 outbreak, but the efficacy of the different interventions varied, with the early case detection and contact reduction being the most effective. Moreover, deploying the NPIs early is also important to prevent further spread. Early and integrated NPI strategies should be prepared, adopted and adjusted to minimize health, social and economic impacts in affected regions around the World.

No comment yet.