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Research to eliminate one of the world’s deadliest diseases – malaria – will be accelerated thanks to a USD $4.7 million grant from the Gates Foundation for scientists at The Kids Research Institute Australia and The University of Western Australia (UWA).
The Malaria Atlas Project (MAP) – which houses the world’s largest malaria database and is at the forefront of efforts to track and tackle the disease – has been awarded more than $16 million by the Bill & Melinda Gates Foundation.
Disruptions of malaria case management caused by the COVID-19 pandemic likely contributed to an extra 76,000 malaria deaths in sub-Saharan Africa, according to analysis by The Kids Research Institute Australia and Curtin University.
A world-leading research team built to tackle malaria has relocated from Oxford University to Western Australia to take advantage of the state’s growing big data talent pool.
Providing protection from malaria vector bites, both indoors and outdoors, is crucial to curbing malaria parasite transmission. Screening of house entry points, especially with incorporated insecticides, confers significant protection but remains a costly and labour-intensive application. Use of spatial repellents has shown promise in creating areas of protection in peri-domestic areas.
Vector mosquito biting intensity is an important measure to understand malaria transmission. Human landing catch (HLC) is an effective but labour-intensive, expensive, and potentially hazardous entomological surveillance tool. The Centres for Disease Control light trap (CDC-LT) and the human decoy trap (HDT) are exposure-free alternatives.
Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species-questions rarely answerable from individual entomological studies (that typically focus on a single location or species).
In recent years there have been reports of viral haemorrhagic fever (VHF) epidemics in sub-Saharan Africa where malaria is endemic. VHF and malaria have overlapping clinical presentations making differential diagnosis a challenge.
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.
When models are used to inform decision-making, both their strengths and limitations must be considered. Using malaria as an example, we explain how and why models are limited and offer guidance for ensuring a model is well-suited for its intended purpose.