The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach.

ABSTRACT: Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location,

SUBMITTER: Delussu F 

PROVIDER: S-EPMC10558401 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature