Malaria Prediction | Stanford News

Similar to COVID, public health authorities around the world have struggled to predict which communities will be hit hardest by malaria, a life-threatening disease that will affect an estimated 247 million people in 2021. I’m here. Scientists and medical experts in Madagascar pave the way for accurately predicting malaria outbreaks in communities using readily available data. Analysis published on February 22 PLOS Global Public Healthis the first such study to detail these relationships and may aid efforts to combat malaria more efficiently and affordably.

A small community in the study area of ​​Ifanadiana, Madagascar. (Image credit: Getty Images)

“Even if these villages are only a few miles apart, we can predict which villages will have the highest number of malaria cases,” said the study’s lead author. Julie Portoisdoctoral student in biology Stanford Graduate School of Humanities“These projections could help allocate limited health care resources to where they are most needed, which is particularly valuable in countries with limited access to health care.”

expected to be a heavy burden

Almost half of the world’s population is at risk of malaria, an acute febrile disease transmitted by mosquito bites, which will kill around 619,000 people in 2021. This is the latest year the World Health Organization has provided such statistics. The burden falls most acutely on people living in poor communities in Africa, whose children under the age of five accounted for about 80% of malaria deaths in 2021.

Although health care organizations have a good understanding of the factors that cause malaria on a national scale, such as warm climate and rainfall patterns that favor mosquito breeding and activity, factors such as microclimate and land use may affect regional scales. Projections become much more complex and uncertain. Health system data can also inaccurately show the burden on communities because those with poor access to health care are not represented.

Working with Madagascar’s National Malaria Control Program and Pivot, a local healthcare organization, the researchers focused on an area in southeastern Madagascar. They are, Previous Stanford-led research We examined malaria incidence data collected by district medical centers and adjusted them to report biases due to financial and geographic barriers to healthcare. To this, the researchers combined satellite information on climate, land-use maps, and socio-economic data from household surveys conducted by the Madagascar National Institute of Statistics.

Using this blend of data, the researchers asked which of these variables best explained malaria patterns and trained a model to predict monthly malaria cases in 195 villages.

Researchers found that the malaria burden was lower in residential areas and higher in areas with paddy fields. This suggests that malaria is a rural disease in the study area. They also found a strong relationship between poverty and reported malaria cases. This shows that many people living in poverty do not receive treatment in health centres, underscoring the need to improve access to health care.

This analysis allowed us to predict relatively well which villages would be hit hardest by malaria. In fact, this approach correctly identified more than half of the communities in the top 20% of malaria prevalence, explaining more than three-quarters of his variability in malaria incidence.

“A new generation of satellite and land-use data integrated with ground-based socio-economic and public health data has been shown to explain heterogeneity in malaria incidence at very fine spatial scales,” said the study. co-author said. Julio de Leo,Professor Ocean and earth system science in the Stanford Dore School of Sustainability“That wasn’t possible until recently.”

“This is important for bringing advances in disease ecology and disease prediction modeling to communities that need it most, such as areas with high malaria burden, high poverty and low access to healthcare. It’s the first step,” says senior author Andrés. Garchitorena, Researcher at the French Institute for Sustainable Development and Associate Scientific Director at Pivot, said: