Bivariate geostatistical latent variable models for the analysis of antibody density data

2026-08-27

AI Summary

研究针对多抗原血清学数据的复杂分布特性，通过扩展潜在变量框架和引入双变量Matérn随机场方法，有效捕捉抗体反应间的相关性，改善了数据分析。

Abstract

The increasing availability of serosurveys that measure antibody responses to multiple antigens requires the development of methods that can exploit the full information content of such data, both biological and spatial. However, the non-Gaussian and potentially multimodal distributional behaviour of antibody responses makes the development of such methods inherently complex, especially in a multivariate setting. Here, we extend the latent variable framework of Giorgi and Wallin (2026), in which continuous antibody concentrations are modelled through an individual-level latent seroreactivity process that represents the level of immune activation to a given antigen. We focus primarily on the bivariate setting and set out a series of guiding principles that justify the resulting joint modelling structure. The proposed model captures distinct sources of correlation between antibody responses, arising both from shared exposure to the same environment and from biological processes occurring within the same host. Spatial dependence is introduced through a novel bivariate Matérn random field, which we use to construct a parsimonious class of cross-covariance functions between antigen-specific spatial processes. We illustrate the application of the framework to analyse data on bivariate antibody measurements from a malaria serosurvey in the Kenyan highlands. Results from the application and a simulation study show that ignoring this correlation substantially degrades inference on joint properties of the antibody distributions and on individual-level seroreactivity, but matters less when interest lies exclusively in each antibody's marginal distribution. Finally, we discuss how the framework could be extended to settings with more than two antigens, and highlight the modelling challenges that arise as the number of antigens grows.

Problem

• bivariate geostatistical
• antibody density data
• latent variable models
• non-Gaussian distribution
• spatial dependence

Innovation

• bivariate geostatistical latent variable models
• antibody density data
• latent seroreactivity process
• bivariate Matérn random field
• cross-covariance functions