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

TL;DR

This paper introduces a novel spatio-temporal modeling method using log-Gaussian Cox processes and Bayesian inference to estimate the velocity and direction of infectious disease spread, demonstrated on COVID-19 data in Cali, Colombia.

Contribution

It presents a new approach combining spatio-temporal point processes with Bayesian inference to quantify disease spread velocities and directions.

Findings

Successfully estimated COVID-19 spread velocities in Cali.

Mapped disease spread directions and magnitudes over time.

Provided a framework for real-time infectious disease monitoring.

Abstract

Understanding 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 between time and space. 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. The velocity is then calculated 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 examine better the spread of the disease throughout the region. We demonstrate our method by analyzing COVID-19 spread in Cali, Colombia, during the 2020-2021 pandemic.