Alessandro Vespignani

TL;DR: This study looks at the use of search data to track prevalence of Influenza-Like Illness, and builds a behavioral model of flu search based on survey data linked to users’ online browsing data and utilizes human computation for classifying search strings.

TL;DR: In this article, the authors quantify the impact of mask adherence and mask mandates on COVID-19 outcomes and find that mask mandates are associated with a statistically significant decrease in daily new cases (−3.24 per 100K), deaths (−0.19 per 100k), and the proportion of hospital admissions (−2.47%) due to COVID19 between February 1 and September 27, 2020.

TL;DR: This paper explores the issue of incomplete sampling in the case of the Internet, which is generally mapped from a limited set of sources by using traceroute-like probes and the origin of the biases introduced by such a sampling process is investigated and related with the global topological properties of the underlying network.

TL;DR: In this paper, the authors derive a mean-field analytical approximation for the probability of edge and vertex detection that allows them to relate the global topological properties of the underlying network with the statistical accuracy of the sampled graph.

TL;DR: In this paper , the authors characterize collective physical distancing in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices.