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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.

Q fever is a notifiable zoonotic disease in Australia, caused by infection with Coxiella burnetii. This study has reviewed 2,838 Q fever notifications reported in Queensland between 2003 and 2017 presenting descriptive analyses, with counts, rates, and proportions. For this study period, Queensland accounted for 43% of the Australian national Q fever notifications. Enhanced surveillance follow-up of Q fever cases through Queensland Public Health Units was implemented in 2012, which improved the data collected for occupational risk exposures and animal contacts. For 2013–2017, forty-nine percent (377/774) of cases with an identifiable occupational group would be considered high risk for Q fever. The most common identifiable occupational group was agricultural/farming (31%). For the same period, at-risk environmental exposures were identified in 82% (961/1,170) of notifications; at-risk animal-related exposures were identified in 52% (612/1,170) of notifications; abattoir exposure was identified in 7% of notifications. This study has shown that the improved follow-up of Q fever cases since 2012 has been effective in the identification of possible exposure pathways for Q fever transmission. This improved surveillance has highlighted the need for further education and heightened awareness of Q fever risk for all people living in Queensland, not just those in previously-considered high risk occupations.

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