Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions

Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions

Blog Article

Abstract A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control.Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19.The model allows xrbrands.shop spatial and temporal heterogeneity of transmission parameters and involves transportation between regions.Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters.

Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves Toner higher accuracy in predicting the newly confirmed cases than baseline models.