Hidden Markov Individual-level Models of Infectious Disease Transmission

2026-02-16

AI Summary

This study addresses the challenge of modeling individual-level infectious disease transmission when only a single, non-continuously observed test time is available, while infection and removal times remain unobserved. To overcome limitations of conventional approaches—which often assume testing coincides with infection or removal, that all individuals are tested, or that tests are independent—the authors propose a Bayesian autoregressive coupled hidden Markov model. This framework jointly infers individual infection and removal times alongside transmission parameters, allowing the probability of testing to depend on prior observations and thus accommodating historical dependence in the detection process. The model’s flexibility and practical utility are demonstrated through applications to two real-world datasets: tomato spotted wilt virus in pepper plants and norovirus transmission among hospital nurses.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Bayesian Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Individual-level epidemic models are increasingly being used to help understand the transmission dynamics of various infectious diseases. However, fitting such models to individual-level epidemic data is challenging, as we often only know when an individual's disease status was detected (e.g., when they showed symptoms) and not when they were infected or removed. We propose an autoregressive coupled hidden Markov model to infer unknown infection and removal times, as well as other model parameters, from a single observed detection time for each detected individual. Unlike more traditional data augmentation methods used in epidemic modelling, we do not assume that this detection time corresponds to infection or removal or that infected individuals must at some point be detected. Bayesian coupled hidden Markov models have been used previously for individual-level epidemic data. However, these approaches assumed each individual was continuously tested and that the tests were independent. In practice, individuals are often only tested until their first positive test, and even if they are continuously tested, only the initial detection times may be reported. In addition, multiple tests on the same individual may not be independent. We accommodate these scenarios by assuming that the probability of detecting the disease can depend on past observations, which allows us to fit a much wider range of practical applications. We illustrate the flexibility of our approach by fitting two examples: an experiment on the spread of tomato spot wilt virus in pepper plants and an outbreak of norovirus among nurses in a hospital.

Problem

• infectious disease transmission
• individual-level models
• hidden infection times
• detection time
• epidemic data

Innovation

• autoregressive coupled hidden Markov model
• individual-level epidemic modeling
• infection time inference
• detection dependence
• Bayesian inference