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.
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.
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.
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.
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.