Socioeconomic correlates of urban mobility trends in two Australian cities during transitional periods of the COVID-19 pandemic.

During the COVID-19 pandemic, both government-mandated lockdowns and discretionary changes in behaviour combined to produce dramatic and abrupt changes to human mobility patterns. To understand the socioeconomic determinants of intervention compliance and discretionary behavioural responses to epidemic threats, we investigate whether changes in human mobility showed a systematic variation by socioeconomic status during two distinct periods of the COVID-19 pandemic in Australia. We analyse mobility data from two major urban centres and compare the trends during mandated stay-at-home policies and after the full relaxation of nonpharmaceutical interventions, which coincided with a large surge of COVID-19 cases.

Estimating household contact matrices structure from easily collectable metadata.

Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them.

Glial Cx43 hemichannels and neuronal Panx1 hemichannels and P2X7 receptors orchestrate presynaptic homeostatic plasticity.

The emerging role of glial cells in modulating neuronal excitability and synaptic strength is a growing field in neuroscience. In recent years, a pivotal role of gliotransmission in homeostatic presynaptic plasticity has been highlighted and glial-derived ATP arises as a key contributor. However, very little is known about the glial non-vesicular ATP-release pathway and how ATP participates in the modulation of synaptic strength.

The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach.

Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions.