Abstract Number:
2718
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Fernando Rodriguez Avellaneda (1), Jorge Mateu (2), Paula Moraga (1)
Institutions:
(1) King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, (2) Department of Mathematics, University Jaume I, 12071 Castellón, Spain, Castellón, Spain
Co-Author(s):
Jorge Mateu
Department of Mathematics, University Jaume I, 12071 Castellón, Spain
Paula Moraga
King Abdullah University of Science and Technology
First Author:
Presenting Author:
Abstract Text:
Understanding of the spread of infectious diseases such as COVID-19 is crucial for informed decision-making and resource allocation. A critical component of disease behavior is the velocity with which disease spreads, defined as the rate of change for each location and time. In this paper, we propose a spatio-temporal modeling approach to determine the velocities of infectious disease spread. Our approach assumes that the locations and times of people infected can be considered as a spatio-temporal point pattern that arises as a realization of a spatio-temporal log-Gaussian Cox process. The intensity of this process is estimated using fast Bayesian inference by employing the integrated nested Laplace approximation (INLA) and the Stochastic Partial Differential Equations (SPDE) approaches. Velocities are then computed by using finite differences that approximate the derivatives of the intensity function. Finally, the directions and magnitudes of the velocities can be mapped at specific times to better examine disease spread across the region. We demonstrate our method by analyzing COVID-19 spread in Cali, Colombia, during the 2020-2021 pandemic.
Keywords:
Bayesian inference|Log-Gaussian Cox processes|Spatio-temporal point patterns|Velocities| |
Sponsors:
Section on Bayesian Statistical Science
Tracks:
Space, time and process modeling
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