Covid 19 V

The prediction of the future developments of a natural phenomenon is one of the main goals of science, but it remains always a great challenge especially when the phenomenon that one is observing involves people that can have a feedback reaction on the observed quantities. This is particularly true in the case of epidemics, especially with the COVID-19 outbreak that the world is suffering in this period. We propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country.

Context: The lockdown orders established in multiple countries in response to the Covid-19 pandemics are perhaps the widest and deep- est shock experienced by human behaviors in recent years. Studying the impact of the lockdown, trough the lens of social media, offers an unprece- dented opportunity for analyzing the susceptibility and the resilience of circadian rhythms to large-scale exogenous shocks. In this context, we address two interconnected research questions: Can variations of online activity cycles provide information on the impact of lockdown on human activities?

Our society is built on a complex web of interdependen- cies whose effects become manifest during extraordinary events, with shocks in one system propagating to the others to an exceptional extent. The recent explosion of publicly shared, decentralized information production that character- izes digital societies and in particular social media activity provides an exceptional laboratory for the observation and the study of these complex social dynamics, and potentially functions as a laboratory to understand, test and validate possible solutions to large-scale crises.

In the last decades, new network theories and epidemiological evidences have substantially advanced our knowledge about how acute respiratory infections spread in the human population. Temporal and multi-layer networks provide a paradigmatic example. These frameworks allow for clearly describing the heterogeneous connectivity features driving transmission, e.g. different connectivity by settings (household, workplace, school, etc.), and occasional vs.

Following the Covid-19 pandemic, governments all over the world have been forced to impose emergency measures such as lockdowns leading to a severe loss in economic output. Whereas standard models assume a progressive return to equilibrium output after a shock, we discuss the impact of a Covid-like shock on a simple toy economy, described by the Mark-0 Agent-Based Model.