Network self-exciting point processes to measure health impacts of COVID-19

Abstract

Abstract The assessment of the health impacts of the COVID-19 pandemic requires the consideration of mobility networks. To this aim, we propose to augment spatio-temporal point process models with mobility network covariates. We show how the resulting model can be employed to predict contagion patterns and to help in important decisions such as the distribution of vaccines. The application of the proposed methodology to 27 European countries shows that human mobility, along with vaccine doses and government policies, are significant predictors of the number of new COVID-19 reported infections and are therefore key variables for decision-making.