SYDNEY, Aug. 12 (Xinhua) -- A team of Australian and international experts have created a mathematical model to predict genetic resistance to antimalarial drugs in Africa, an initiative they hope will boost the effectiveness of preventive interventions in the continent.

Published in PLOS Computational Biology on Thursday, the study used data from the WorldWide Antimalarial Resistance Network (WWARN), a global scientifically independent collaboration, to map the prevalence of genetic markers that indicate resistance to Plasmodium falciparum -- the parasite that causes malaria.

"The antimalarial drug sulfadoxine-pyrimethamine (SP) is commonly used in various preventative malaria treatment programs in Africa, particularly for infants, young children, and during pregnancy. But we know its efficacy as a treatment is threatened in areas where resistance to SP is high," said lead author of the study, Associate Professor Jennifer Flegg from the University of Melbourne.

Researchers said the study combines all the available SP resistance data from the last two decades in a single model. It allows national malaria control programs and researchers to get needed data on the degree of resistance in a given area or a year.

Health agencies can use this tool to understand when and where SP is appropriate to use as part of preventive malaria treatments and where other antimalarial methods may need to be explored.

Malaria is a life-threatening disease caused by parasites and spread to humans through infected mosquitoes. It is preventable and curable, yet resistance to current antimalarial drugs is causing avoidable loss of life.

The World Health Organization (WHO) estimated there were 241 million cases of malaria worldwide in 2020, with more than 600,000 deaths.

"This timely evidence of the extent of SP resistance across Africa will help to inform where SP preventive treatment, alone or in combination with other antimalarials, would be most likely to have the greatest impact," said Professor Karen Barnes, head of WWARN Pharmacology and Elimination.

Researchers believe this tool will make continuous predictions in space and time. It should help guide health policies that will bring the WHO's ambitious target of reducing global malaria incidence and mortality rates by at least 90 percent by 2030 a step forward. Enditem

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