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Assessing changes in commuting and individual mobility in major metropolitan areas in the United States during the COVID-19 outbreak

Wed, 04/15/2020 - 11:21

Brennan Klein, Timothy LaRock, Stefan McCabe, Leo Torres, Filippo Privitera, Brennan Lake, Moritz U. G. Kraemer, John S. Brownstein, David Lazer, Tina Eliassi-Rad, Samuel V. Scarpino, Matteo Chinazzi, and Alessandro Vespignani


On March 16, 2020, the United States government issued new guidelines promoting public health social social distancing interventions to reduce the spread of the COVID-19 epidemic in the country [1]. In addition, many state and local governments in the United States have enacted stay-at-home policies banning mass gatherings, enforcing school closures, and promoting smart working. So far, however, the extent to which these policies have resulted in reduced people’s mobility has not been quantified. By analyzing data from millions of (anonymized, aggregated, privacy-enhanced) devices, we estimate that by March 23 the policies have generally reduced by half the overall mobility in several major U.S. cities. In order to gauge the observed results we know events, we note that the commuting volume on Monday, March 16, approached those of a typical snow day or analogous day when public schools are partially closed (i.e. January 2). By Friday, March 20, we observe commuting numbers that resemble those measured on federal holidays (i.e. Martin Luther King Jr. Day in January or Presidents’ Day in February). Currently, we are unable to quantify the extent to which this reduced commuting volume is driven by people working from home or simply an increase in unemployment, though it is surely a mixture of both. Whether this reduction in mobility is enough to change the course of this pandemic is not yet known, but it does provide guidance for further measures that can be implemented at a national scale in the United States.



Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics

Wed, 04/15/2020 - 09:37

Alexandru Topirceanu, Mihai Udrescu, Radu Marculescu


The infectious diseases are spreading due to human interactions enabled by various social networks. Therefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading. To this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks. Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading. The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one. Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values. Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy. Finally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement. We find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time. We believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.


Complex Systems Collide, Markets Crash

Tue, 04/14/2020 - 14:31

At some point, systems flip from being complicated, which is a challenge to manage, to being complex. Complexity is more than a challenge because it opens the door to all kinds of unexpected crashes and events.

Their behavior cannot be reduced to their component parts. It’s as if they take on a life of their own.


Special report: The simulations driving the world’s response to COVID-19

Sun, 04/12/2020 - 13:13

How epidemiologists rushed to model the coronavirus pandemic.


How the world’s collective attention is being paid to a pandemic: COVID-19 related 1-gram time series for 24 languages on Twitter

Sun, 04/12/2020 - 09:35

In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most `important’ for March 2020 with respect to March 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for `virus’ in January followed by a decline through February and a recent surge through March. The world’s collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations.


Stochastic Models and Experiments in Ecology and Biology 2020 Conference – Venice 21-24th September

Wed, 04/08/2020 - 21:36

SMEEB 2020 conference will be held in Venice, September 21-24, 2020, at the European Center of Living Technology (ECLT). 

   The aim of the workshop is to bring together scientists with different backgrounds (mathematics, biology, physics and computing) interested in microbial ecology and evolutionary biology (both theory and experiments). We will discuss important and recent research topics in these areas as well as methods and ideas.    Topics will include stochastic population dynamics, quantitative and systemic biology, community ecology of microbes, statistical mechanics models in ecology, evolution in microbial communities, biodiversity coexistence and species interactions. The style of the workshop will purposely be informal to encourage discussions.    Invited Speakers(*tbc): Otto X. Cordero, Eric Dykeman, Daniel Fisher, Nigel Goldenfeld*, Susan Holmes*, Terry Hwa, Eleni Katifori, David Nelson, Derek Tittensor, Amandine Veber.     The call of abstracts for contributed talks will close on May 24, 2020 (EasyChair submission link: ).   

 Please bring this announcement to the attention of anyone who may be interested, especially students and post-docs who are not in this mailing list. There are 2 registration fee waivers for Ph.Ds / young Post-docs. Look in the website for all info. The attendance fee of the workshop will be 200 Euro, which includes coffee breaks and workshop material. However, owing to the current Covid-19 epidemic, the payment is not open at the moment. Once the workshop will eventually be confirmed, we will open the payment link and contact those who have pre-registered or submitted an abstract for the final registration.

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

Wed, 04/08/2020 - 12:30

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.


The Emergence of Informative Higher Scales in Complex Networks

Wed, 04/08/2020 - 09:29

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.


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.


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.


(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.


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.


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%.


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.


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.


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.


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.


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.


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.


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.