Unravelling animal exposure profiles of human Q fever cases in Queensland, Australia, using natural language processing.
Coxiella burnetii
Australia
Markov Random Fields
Q fever
text mining
topic models
zoonosis
Journal
Transboundary and emerging diseases
ISSN: 1865-1682
Titre abrégé: Transbound Emerg Dis
Pays: Germany
ID NLM: 101319538
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
revised:
11
02
2020
received:
29
10
2019
accepted:
28
03
2020
medline:
8
4
2020
pubmed:
8
4
2020
entrez:
8
4
2020
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2133-2145Subventions
Organisme : Australian Government Department of Agriculture and Water Resources
ID : RnD4Profit-15-02-008
Organisme : Australian Research Council
ID : DE160100477
Informations de copyright
© 2020 Blackwell Verlag GmbH.
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