Covid 19 IV

The COVID-19 pandemic has left no country untouched affecting people’s routines over the globe. The mobility of the population is identified as a fundamental driving force for the dissemination of the COVID-19 at both regional and local levels. This work dissects the integrated impact of urban form and human mobility in the spreading of COVID-19 within cities. Namely, systematically exploring the effects of human mobility imposed by the diverse urban patterns in regards to the distribution of population and job locations.

Owing to the new coronavirus and to the COVID-19, new words and concepts are being incorporated into our daily lives, and into linguistics as well. In an environment flooded with terms such as new normal, flatten the curve, asymptomatic, comorbidity, community spread, herd immunity, lockdown, quarantine, social distancing, live (as in live video or live streaming), and work from home, a new glossary has been made ordinary and assimilated by society.

The spread of COVID-19 during the initial phase of the first half of 2020 was curtailed to a larger or lesser extent through measures of social distancing imposed by most countries. In this work, we link directly, through machine learning techniques, infection data at a country level to a single number that signifies social distancing effectiveness. We assume that the standard SIR model gives a reasonable description of the dynamics of spreading, and thus the social distancing aspect can be modeled through time-dependent infection rates that are imposed externally.

Protein-protein interaction (PPI) networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID-19 and providing ground for drug repurposing strategies. However, our knowledge of (dis)similarities between this one and other viral agents is still very limited. Here we compare the novel coronavirus PPI network against 45 known viruses, from the perspective of statistical physics.