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The Mental Health Commission (MHC) of Western Australia has provided funding to The Kids Research Institute Australia to undertake exploratory research to inform a WA approach to aftercare.
The Malaria Atlas Project (MAP) aims to disseminate free, accurate and up-to-date geographical information on malaria and associated topics. Our mission is to generate new and innovative methods to map malaria, to produce a comprehensive range of maps and estimates that will support effective planning of malaria
Adam Dan Francesca Susan Saddler Weiss Sanna Rumisha PhD PhD Dr PhD (Biostatistics) Senior Research Officer Honorary Research Fellow Research
Nick Golding BSc DPhil Honorary Research Fellow Nick.Golding@thekids.org.au Honorary Research Fellow Professor Nick Golding is the UWA Chair in
We study the tidal interaction of galaxies in the Eridanus supergroup, using H i data from the pre-pilot survey of the Widefield ASKAP L-band Legacy All-sky Blind surveY.
Tuberculosis (TB) continues to be a major public health challenge in China. Understanding TB management delays within the context of China’s unique ethnic diversity may be of value in tackling the disease. This study sought to evaluate the impact of ethnic minority status on TB diagnosis and treatment delays.
By mapping land use under projections of socio-economic change, ecological changes can be predicted to inform conservation decision-making. We present a land use model that enables the fine-scale mapping of land use change under future scenarios. Its predictions can be used as input to virtually all existing spatially-explicit ecological models.
Due to global climate change–induced shifts in species distributions, estimating changes in community composition through the use of Species Distribution Models has become a key management tool. Being able to determine how species associations change along environmental gradients is likely to be pivotal in exploring the magnitude of future changes in species’ distributions.
Globally, China has the third highest number of tuberculosis (TB) cases despite high rates (85.6%) of effective treatment coverage. Identifying risk factors associated with unsuccessful treatment outcomes is an important component of maximising the efficacy of TB control programmes.
Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator.