#covid19 #Mobile phone data #epidemics

Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas

Authors: Alberto Aleta, David Martín-Corral, Michiel A.Bakker, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova , Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran,Ira M. Longini,Jr, Alex Pentland, Alessandro Vespignani, Yamir Moreno, and Esteban Moro. Publication: PNAS (2022). Link Abstract: Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2infection to estimate the where, when, and magnitude of transmission events during thepandemic’s first wave. ...

#wordle #Rforeverythingelse

Wordle is getting (slightly) harder

Marty, I’ve been to the future and I have bad news. Wordle is getting harder. Not much, but harder. With millions of people playing the game Wordle online and the recent takeover by the New York times, some speculation about whether the game is getting harder is unavoidable. Since Wordle chooses a different word every day, some players have started to complain recent target words are harder to be guessed: It must be a coincidence, but the NY Times puzzles are hard and suddenly Wordle has questionable words ...

#Mobile Phone Data #Privacy #Deep Learning

Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy

Authors: Alex Berke, Ronan Doorley, Kent Larson, Esteban Moro Publication: arXiv preprint arXiv:2201.01139 (2022). Link Abstract: Location data collected from mobile devices represent mobility behaviors at individual and societal levels. These data have important applications ranging from transportation planning to epidemic modeling. However, issues must be overcome to best serve these use cases: The data often represent a limited sample of the population and use of the data jeopardizes privacy. ...

#wordle #Rforeverythingelse

Playing (and winning) Wordle with R

Introduction Unless you have been away for the last month, you, your family or friends have been talking about or playing Wordle. It is a very straightforward game which reminds us (old enough) of the great MasterMind, but with words. The idea is very simple. In the original version by Josh Wardle, we try to guess a (English) word of five letters. After each guess the game shows you what letters are in the answer in the right position (green), in the answer but in a wrong position (yellow) or not in the answer at all (gray). ...

#resilience #Labour Markets #Data Science

Creating resilient urban labor economies

Like ecosystems, societies with adaptable economies are best prepared for the future. We started a research program to understand and detect economic resilience encoded in the dependency networks of agents, businesses, cities, or jobs. In particular, how much of the adaptability of our economies depends on the fragility of those economic networks? Can we identify weaknesses and design policies to strengthen those interdependent units? Using highly detailed information about jobs, skills, and cities, our foundational study extended traditional economic models to show that the network structure of interactions and flows between jobs determines the resilience of labor markets. ...

#social media #Twitter #Facebook #Sensors

Social networks as economic and social sensors

Temporal, structural and activity patterns in social networks are related to human behavior. Thus, networks do have different shape and dynamics under different exogenous and endogenous shocks or conditions. In the last years we have addressed the question of whether we can use our understanding of social networks to anticipate, predict or measure important phenomena as information propagation, disaster damage, unemployment shocks, weather conditions, gender digital divide, etc. For example in a series of papers, we demonstrated for the first time the ability of the so-called “friendship paradox” in social networks to get better collection of users (sensors) that can anticipate meme, news or event-related information propagation in networks. ...