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Improving estimates of antimalarial drug resistance

The effect of malaria on the developing world is devastating. Each year there are more than 200 million cases and over 400,000 deaths, with children under the age of five the most vulnerable.

Investigators

  • Dr August Hao (The Kids)  
  • Professor Nicholas Golding (The University of Western Australia) 
  • Assoc Prof Daniel Weiss (Curtin University)

Partners

  • WorldWide Antimalarial Research Network (WWARN) 
  • University of Melbourne 

Project description

The effect of malaria on the developing world is devastating. Each year there are more than 200 million cases and over 400,000 deaths, with children under the age of five the most vulnerable. Ambitious malaria elimination targets have been set by the World Health Organization for 2030. These involve the elimination of the disease in at least 35 countries, including Australia's neighbouring countries in Asia. However, these malaria elimination targets rest precariously on being able to treat the disease appropriately; a difficult feat with the emergence and spread of antimalarial drug resistance across Southeast Asia and sub-Saharan Africa (SSA). Detections of resistance are sporadic and incomplete, especially in SSA where resistance to artemisinin derivatives, the current first line drugs for malaria, is emerging. There is an urgent need for reliable mapping of antimalarial drugs in space and time, to guide policy makers to make well-informed decisions. Our work draws on rich datasets of molecular and clinical evidence of drug resistance across SE Asia and SSA, and develops bespoke statistical models to predict resistance prevalence as continuous patterns across space and time. Our predictions highlight heterogeneous and spatially clustered patterns of emerging artemisinin resistance in SSA. These findings show that combating emerging resistance in high-burden settings requires strong international coordination and digital infrastructure build-up in surveillance efforts. Antimalarial resistance is a growing problem, and strong surveillance and prediction capabilities are crucial to our ability to solve it.

External collaborators

  • Associate Professor Jennifer Flegg (University of Melbourne)
  • Prof Philippe Guerin (WWARN) 

Funders

  • NHMRC