Serina Chang, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky & Jure Leskovec
Nature (2020)

The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread1. We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of “superspreader” POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.

Read the full article at: www.nature.com

Even genes essential for life can be caught in an evolutionary arms race that forces them to change or be replaced.

Read the full article at: www.quantamagazine.org

Paolo Bosetti, Piero Poletti, Massimo Stella, Bruno Lepri, Stefano Merler, and Manlio De Domenico
PNAS

The recent increase in large-scale migration trends generates several concerns about public health in destination countries, especially in the presence of massive incoming human flows from countries with a disrupted healthcare system. Here, we analyze the flow of 3.5 M Syrian refugees toward Turkey to quantify the risk of measles outbreaks. Our results suggest that heterogeneity in immunity, population distribution, and human-mobility flows is mostly responsible for such a risk: In fact, adequate policies of social integration and vaccine campaigns provide the most effective strategies to reduce measles disease risks for both migrant and hosting populations.

Read the full article at: www.pnas.org

Blai Vidiella, Antoni Guillamon, Josep Sardanyés, Victor Maull, Nuria Conde-Pueyo, Ricard Solé

Complex dynamical fluctuations, from molecular noise within cells, collective intelligence, brain dynamics or computer traffic have been shown to display noisy behaviour consistent with a critical state between order and disorder. Living close to the critical point can have a number of adaptive advantages and it has been conjectured that evolution could select (and even tend to) these critical states. One way of approaching such state is by means of so called self-organized criticality (SOC) where the system poises itself close to the critical point. Is this the case of living cells? It is difficult to test this idea given the enormous dimensionality associated with gene and metabolic webs. In this paper we present an alternative approach: to engineer synthetic gene networks displaying SOC behaviour. This is achieved by exploiting the presence of a saturation (congestion) phenomenon of the ClpXP protein degradation machinery in E. coli cells. Using a feedback design that detects and then reduces ClpXP congestion, a critical motif is built from a two-gene network system, where SOC can be successfully implemented. Both deterministic and stochastic models are used, consistently supporting the presence of criticality in intracellular traffic. The potential implications for both cellular dynamics and designed intracellular noise are discussed.

Read the full article at: www.biorxiv.org

You may have noticed that not everyone agrees with the outcome of the 2020 US Presidential election. But looking beyond the ALL CAPS TWEETS of Donald Trump, one claim circulating on social media is that some of Joe Biden’s votes look suspicious because they don’t adhere to “Benford’s law “.

So do the claims stack up? In short, no – but the reasons are interesting.

Read the full article at: physicsworld.com