End the Coronavirus
Spread the knowledge, not the virus.
Take part in eradicating this epidemic
Since the first confirmed case of a new, virulent strain of the coronavirus in December in Wuhan, China, the disease has spread to more than 100 countries and territories. As of March 12, 2020, there are 125,048 confirmed cases and 4,613 deaths. These numbers are still increasing.
Everyone can help.
Source: www.endcoronavirus.org
COVID-19 outbreak response: first assessment of mobility changes in Italy following lockdown
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.
Source: covid19mm.github.io
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
Source: arxiv.org
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.
Source: www.mdpi.com
Concepts in Boolean network modeling: What do they all mean?
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.
Source: www.sciencedirect.com
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.
Source: www.medrxiv.org
Landmark Computer Science Proof Cascades Through Physics and Math
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.
Source: www.quantamagazine.org
The effect of human mobility and control measures on the COVID-19 epidemic in China
Moritz U.G. Kraemer, Chia-Hung Yang, Bernardo Gutierrez, Chieh-Hsi Wu, Brennan Klein, David M. Pigott, open COVID-19 data working group, Louis du Plessis, Nuno R Faria, Ruoran Li, William P. Hanage, John S Brownstein, Maylis Layan, Alessandro Vespignani, Huaiyu Tian, Christopher Dye, Simon Cauchemez, Oliver Pybus, Samuel V Scarpino
The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.
Source: www.medrxiv.org
Advanced Control and Optimization for Complex Energy Systems
Chun Wei, Xiaoqing Bai, and Taesic Kim
Editorial | Open Access
Complexity Volume 2020 |Article ID 5908102
The application of renewable energies such as wind and solar has become an inevitable choice for many countries in order to achieve sustainable and healthy economic development [1]. However, due to the intermittent characteristics of renewable energy, the issue with integrating a larger proportion of renewable energy into the grid becomes prominent. Currently, an energy system with weak coordination capability seriously affects the flexibility of power system operation [2]. As a result, this has led to the development of an effective way to integrate high-proportion renewable energy by developing multienergy systems including wind, solar, thermal, and energy storage to allow for the integration and coordination of different energy resources [3]. The major challenge of the multienergy system is its complexity with multispatial and multitemporal scales. Compared with the traditional power system, control and optimization of the complex energy system become more difficult in terms of modeling, operation, and planning [4, 5]. The main purpose of the complex energy system is to coordinate the operation with various distributed energy resources (DERs), energy storage systems, and power grids to ensure its reliability, while reducing the operating costs and achieving the optimal economic benefits.
Source: www.hindawi.com
See Also: Special Issue
On the Synthtesis of Affectivity Embodiment & AI
ALife2020
13-18 July 2020, Montreal, Canada
Affective computing works mostly under a vision of emotions based on a functionalist conception of the mind in which emotions, as any other mental state, are understood as functional relations of information processing. The way in which these functional relations are achieved, whether through neuronal activity and organization or by artificial computer programming, is irrelevant to what emotions essentially are. These ideas are in stark contrast to the positions of embodied cognitive science, especially those emerging from the 4E approach to cognition (Embodied, Ecological, Embedded, Enactive), to which, in general, affectivity is seen as constitutive to cognition and cognition is always embodied.
In this workshop we discuss how relevant is embodiment for the synthesis of affectivity based in AI or other forms of implementation. The workshop is open to the widest possible disciplinary audience to tackle both the theoretical and philosophical aspects of synthetic affectivity, and how this is relevant for real-world implementations. We believe that this discussion is not only relevant in terms of advancing technology –which is exciting all by itself–, but it is a great opportunity to put the embodiment of emotions and affectivity in sharper relief by considering if and how this affective life can be shared with synthetic systems or even artificially implemented. We thus propose a dialogue in which the AI concern with artificial affectivity and the embodied methodologies of ALife can meet.
Source: cogsci4e.wixsite.com
