Data modelling tools for precision public health - Modelling leptospirosis risk in Fiji
The public health burden of zoonotic infectious diseases is immense. Reducing this burden requires a One Health approach that incorporates person, the animal host, and their shared environment. Increasingly, this is also being combined with strategies tailored towards groups or individuals, to gain maximum public health benefits via more effective use of the limited available resources. Using an example of leptospirosis in Fiji, this talk will demonstrate how Bayesian networks and geographically weighted regression are valuable tools for designing precision public health interventions. By allowing decision makers to explore different scenarios and examine how risk factors vary over geographic space, the most appropriate interventions can be applied to the relevant population.
Dr Helen Mayfield: Her research interests span environmental conservation and human health, and the overlap between the two. Helen's goal is to facilitate evidence based decisions in these fields by making data modelling and decisions science more accessible to decision makers. Her current work focuses on eco-epidemiology of infectious diseases, and the use of expert elicitation for guiding threatened species management in data poor situations. She draws on a range of data modelling and machine learning techniques, with a focus on spatial modelling. Helen is currently a post-doctoral research fellow at The Australia National University working on identifying hotspots for lymphatic filariasis in Samoa.
This session will be live-streamed and recorded - accessible to both internal and external audience
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Please note that the ramp at our main entrance of our Keppel Street building will close from Saturday 5 October until Monday 18 November for essential repairs. An alternative accessible route for visitors with a disability and wheelchair users will be provided on Malet Street. There will be signage to guide you to this entrance.
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