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Estimating velocities of infectious disease spread through spatio-temporal log-Gaussian point proces

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:

Fernando Rodriguez Avellaneda  
King Abdullah University of Science and Technology

Presenting Author:

Fernando Rodriguez Avellaneda  
King Abdullah University of Science and Technology

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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