The study integrates climate, land use, and socioeconomic data to explain and predict malaria dynamics at the village level.
The study integrates climate, land use, and socioeconomic data to explain and predict malaria dynamics at the village level. The approach could inform health care practitioners and make control strategies more efficient and cost-effective.
As with COVID, public health agencies around the world have struggled to predict which communities will be hit the hardest with malaria, a life-threatening disease that infected an estimated 247 million people in 2021. A new Stanford-led study done in collaboration with local scientists and health care experts in Madagascar paves the way to using easily obtainable data to accurately predict malaria outbreaks in communities. The analysis, published Feb. 22 in PLOS Global Public Health, is the first such study to show these relationships in fine detail and could inform efforts to combat malaria more efficiently and affordably.
“We can predict which villages will have the most malaria cases, even when these villages are only a few miles apart,” said study lead author Julie Pourtois, a PhD student in biology at the Stanford School of Humanities and Sciences. “These predictions could help distribute limited health care resources where they are most needed, which is particularly valuable in countries with limited access to health care.”
Read more at Stanford University
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