Q fever, caused by the zoonotic bacterium Coxiella burnetii, is a globally distributed emerging infectious disease. Livestock are the most important zoonotic transmission sources, yet infection in people without livestock exposure is common. Identifying potential exposure pathways is necessary to design effective interventions and aid outbreak prevention. We used natural language processing and graphical network methods to provide insights into how Q fever notifications are associated with variation in patient occupations or lifestyles. Using an 18-year time-series of Q fever notifications in Queensland, Australia, we used topic models to test whether compositions of patient answers to follow-up exposure questionnaires varied between demographic groups or across geographical areas. To determine heterogeneity in possible zoonotic exposures, we explored patterns of livestock and game animal co-exposures using Markov Random Fields models. Finally, to identify possible correlates of Q fever case severity, we modelled patient probabilities of being hospitalized as a function of particular exposures. Different demographic groups consistently reported distinct sets of exposure terms and were concentrated in different areas of the state, suggesting the presence of multiple transmission pathways. Macropod exposure was commonly reported among Q fever cases, even when exposure to cattle, sheep or goats was absent. Males, older patients and those that reported macropod exposure were more likely to be hospitalized due to Q fever infection. Our study indicates that follow-up surveillance combined with text modelling is useful for unravelling exposure pathways in the battle to reduce Q fever incidence and associated morbidity.
Keywords: Coxiella burnetii; Australia; Markov Random Fields; Q fever; text mining; topic models; zoonosis.
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