arXiv subjects

Dirk Douwes-Schultz

Publications and source records attributed to Dirk Douwes-Schultz.

5 recordsLinked to original sources

arXiv2026

Hidden Markov Individual-level Models of Infectious Disease Transmission

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 first showed symptoms of the disease 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, given only the symptom-onset times. More traditional data augmentation methods used in epidemic modelling have either assumed that the infection or removal times are known or that all infected individuals showed symptoms. In contrast, we build an observation model where there is a chance that an infectious individual never shows symptoms during the study, allowing for undetected (e.g., asymptomatic) infections. Symptom onset is not assumed to correspond to infection or removal. Bayesian coupled hidden Markov models have been used previously for individual-level epidemic data consisting of laboratory diagnostic test results. However, these models are not appropriate for symptom-onset data, as they have a chance of generating multiple symptom-onset times for an individual. In contrast, we incorporate autoregression into the observation process, ensuring that at most one symptom-onset time is observed per individual. 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.

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arXiv2025

Markov switching zero-inflated space-time multinomial models for comparing multiple infectious diseases

Univariate zero-inflated models are increasingly being used to account for excess zeros in spatio-temporal infectious disease counts. However, the multivariate case is challenging due to the need to account for correlations across space, time and disease in both the count and zero-inflated components of the model. We are interested in comparing the transmission dynamics of several co-circulating infectious diseases across space and time, where some of the diseases can be absent for long periods. We first assume there is a baseline disease that is well-established and always present in the region. The other diseases switch between periods of presence and absence in each area through a series of coupled Markov chains, which account for long periods of disease absence, disease interactions and disease spread from neighboring areas. Since we are mainly interested in comparing the diseases, we assume the cases of the present diseases in an area jointly follow an autoregressive multinomial model. We use the multinomial model to investigate whether there are associations between certain factors, such as temperature, and differences in the transmission intensity of the diseases. Inference is performed using efficient Bayesian Markov chain Monte Carlo methods based on jointly sampling all unknown presence indicators. We apply the model to spatio-temporal counts of dengue, Zika, and chikungunya cases in Rio de Janeiro, during the first triple epidemic there.

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arXiv2025

A Comparison between Markov Switching Zero-inflated and Hurdle Models for Spatio-temporal Infectious Disease Counts

In epidemiological studies, zero-inflated and hurdle models are commonly used to handle excess zeros in reported infectious disease cases. However, they can not model the persistence (changing from presence to presence) and reemergence (changing from absence to presence) of a disease separately. Covariates can sometimes have different effects on the reemergence and persistence of a disease. Recently, a zero-inflated Markov switching negative binomial model was proposed to accommodate this issue. We introduce a Markov switching negative binomial hurdle model as a competitor of that approach, as hurdle models are often also used as alternatives to zero-inflated models for accommodating excess zeroes. We begin the comparison by inspecting the underlying assumptions made by both models. Hurdle models assume perfect detection of the disease cases while zero-inflated models implicitly assume the case counts can be under-reported, thus we investigate when a negative binomial distribution can approximate the true distribution of reported counts. A comparison of the fit of the two types of Markov switching models is undertaken on chikungunya cases across the neighborhoods of Rio de Janeiro. We find that, among the fitted models, the Markov switching negative binomial zero-inflated model produces the best predictions and both Markov switching models produce remarkably better predictions than more traditional negative binomial hurdle and zero-inflated models.

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arXiv2024

A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions

Recurrent COVID-19 outbreaks have placed immense strain on the hospital system in Quebec. We develop a Bayesian three-state coupled Markov switching model to analyze COVID-19 outbreaks across Quebec based on admissions in the 30 largest hospitals. Within each catchment area, we assume the existence of three states for the disease: absence, a new state meant to account for many zeroes in some of the smaller areas, endemic and outbreak. Then we assume the disease switches between the three states in each area through a series of coupled nonhomogeneous hidden Markov chains. Unlike previous approaches, the transition probabilities may depend on covariates and the occurrence of outbreaks in neighboring areas, to account for geographical outbreak spread. Additionally, to prevent rapid switching between endemic and outbreak periods we introduce clone states into the model which enforce minimum endemic and outbreak durations. We make some interesting findings, such as that mobility in retail and recreation venues had a positive association with the development and persistence of new COVID-19 outbreaks in Quebec. Based on model comparison our contributions show promise in improving state estimation retrospectively and in real-time, especially when there are smaller areas and highly spatially synchronized outbreaks. Furthermore, our approach offers new and interesting epidemiological interpretations, such as being able to estimate the effect of covariates on disease extinction.

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arXiv2021

Zero-state Coupled Markov Switching Count Models for Spatio-temporal Infectious Disease Spread

Spatio-temporal counts of infectious disease cases often contain an excess of zeros. With existing zero inflated count models applied to such data it is difficult to quantify space-time heterogeneity in the effects of disease spread between areas. Also, existing methods do not allow for separate dynamics to affect the reemergence and persistence of the disease. As an alternative, we develop a new zero-state coupled Markov switching negative binomial model, under which the disease switches between periods of presence and absence in each area through a series of partially hidden nonhomogeneous Markov chains coupled between neighboring locations. When the disease is present, an autoregressive negative binomial model generates the cases with a possible 0 representing the disease being undetected. Bayesian inference and prediction is illustrated using spatio-temporal counts of dengue fever cases in Rio de Janeiro, Brazil.

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