A Framework for the Joint Modeling of Longitudinal Diagnostic Outcome Data and Latent Infection Status: Application to Investigating the Temporal Relationship between Infection and Disease

Abstract

For many diseases the infection status of individuals cannot be observed directly, but can only be inferred from biomarkers that are subject to measurement error. Diagnosis of infection based on observed symptoms can itself be regarded as an imperfect test of infection status. The temporal relationship between infection and marker outcomes may be complex, especially for recurrent diseases where individuals can experience multiple bouts of infection. We propose an approach that first models the unobserved longitudinal infection status of individuals conditional on relevant covariates, and then jointly models the longitudinal sequence of biomarker outcomes conditional on infection status and covariate information through time, thus resulting in a joint model for longitudinal infection and biomarker sequences. This model can be used to investigate the temporal dynamics of infection, and to evaluate the usefulness of biomarkers for monitoring purposes. Our work is motivated and illustrated by a longitudinal study of bovine digital dermatitis (BDD) on commercial dairy farms in North West England and North Wales, in which the infection of interest is Treponeme spp., and the biomarkers of interest are a continuous enzyme-linked immunosorbent assay test outcome and a dichotomous outcome, foot lesion status. BDD is known to be one of the possible causes of foot lesions in cows.

Bayesian modeling of temporal properties of infectious disease in a college student population

“…When in state S, different individuals may have distinct levels of susceptibility to virus-borne illness, and this is accounted for in the model. We have tied state I to symptoms, as is common [1,3,7,16,20,26]. However, there are asymptomatic individuals who shed virus [21] and hence are in the infective state I; these individuals are identified and discussed when presenting results.…”

“…There has been significant interest in the analysis of community-to-individual and individualto-individual transfer of virus [1,3,7,16,20,26]. Many of these studies have been concerned with infection transfer in households [3,7,20], in confined spaces like elementary schools [26], as well as transfer among domestic animal populations [16].…”

“…Many of these studies have been concerned with infection transfer in households [3,7,20], in confined spaces like elementary schools [26], as well as transfer among domestic animal populations [16]. The modeling may take different forms, depending upon the data considered and questions being asked.…”

“…For example, based on the incidences of ILI, one may be interested in the analysis of large-scale dynamics of epidemic propagation [1,10,14], where in this case the data may be counts of spacetime ILI events. There are other studies for which the modeling is performed at or near the level of the symptoms or biomarkers, which are noisy and often imperfect [16]. The studies are complicated by missing and incomplete data, and an unknown number of competing pathogens [26].…”

Model-based clustering for assessing the prognostic value of imaging biomarkers and mixed type tests

“…Fixed effect and random effect models were adopted to relax the conditional independence assumption (Qu et al, 1996; Xu and Craig, 2009; Wang and Zhou, 2012). Different longitudinal models were proposed for repeated test measurements (Cook et al, 2000; Jones et al, 2012; Wu et al, 2016). And Albert and Dodd (2004) investigated the influence on parameter estimates introduced by different modeling assumption for ${\gamma }_{d}false({\overrightarrow{t}}_{i}false)$.…”