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  KEYWORD

Modelling and data-driven analysis of collective behavioral responses to the COVID-19 pandemic

keywords BEHAVIORAL MODELING, COVID-19, DATA ANALYSIS, EPIDEMIC SPREADING

Reference persons ALESSANDRO RIZZO

External reference persons Lorenzo Zino, University of Groningen, The Netherlands
Mengbin Ye, Curtin University, Perth, Australie

Thesis type DATA ANALYSIS - SIMULATION

Description Slowing down and reducing the impact of the COVID-19 pandemic critically relies on individuals in a community collectively adopting correct self-protective behaviors, such as obeying stay-at-home mandates or utilizing face masks. Google is providing a regularly updated large dataset of mobility patterns across different countries and types of locations, capturing changes to community social activity since the start of the pandemic. This project will analyze this mobility data to understand the behavioral response of communities, and use this data to validate recently proposed models capturing collective decision-making and behavioral response during an epidemic.

Reference:
M.Ye, L.Zino, A.Rizzo, M.Cao, Modelling collective decision-making during epidemics, under review and onto ArXiV (https://arxiv.org/abs/2008.01971)

See also  https://arxiv.org/abs/2008.01971

Required skills basic math skills (calculus, statistics), computational skills (MATLAB or Python, data analysis)


Deadline 30/11/2021      PROPONI LA TUA CANDIDATURA




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