Estimating Velocities of Infectious Disease Spread Through Spatio‐Temporal Log‐Gaussian Cox Point Processes
提出一种时空建模方法，利用对数高斯Cox点过程和扩散随机偏微分方程估计传染病传播速度，并通过有限差分计算速度的方向和大小，以COVID-19在哥伦比亚卡利的传播为例进行验证。
Summary Understanding the spread of infectious diseases such as COVID‐19 is crucial for informed decision‐making and resource allocation. A critical component of disease behaviour is the velocity with which disease spreads, defined as the rate of change between time and space. This paper proposes 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 a spatio‐temporal point pattern that arises as a realisation of a spatio‐temporal log‐Gaussian Cox point process. The intensity function of this process is estimated using a fully nonseparable spatio‐temporal model derived from diffusion stochastic partial differential equations (SPDE), and fast Bayesian inference is performed using integrated nested Laplace approximation (INLA). 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 better examine the spread of the disease throughout the region. This method is demonstrated by analysing COVID‐19 spread in Cali, Colombia, during the 2020–2021 pandemic.