Hidden Markov Individual-level Models of Infectious Disease Transmission

TL;DR

This paper introduces a novel autoregressive coupled hidden Markov model to infer infection and removal times from limited detection data, improving the analysis of individual-level epidemic transmission dynamics.

Contribution

It develops a flexible Bayesian hidden Markov model that accounts for dependent testing scenarios and partial detection data, advancing epidemic modeling techniques.

Findings

Successfully applied to tomato virus spread data

Effectively modeled norovirus outbreak among hospital nurses

Demonstrated improved inference with limited detection information

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.