Malaria Outbreak Forecast with New Analysis

Similar to COVID, public health organizations around the world have had difficulty predicting which populations will be most affected by malaria, a potentially fatal illness that is expected to afflict 247 million people worldwide in 2021. A recent study from Stanford that was conducted in Madagascar in conjunction with regional scientists and healthcare professionals lays the way for the use of readily available data to properly anticipate a malaria outbreak in any community.

The report, published on Feb. 22 in PLOS Global Public Health, is the first such study to illustrate these links in exquisite detail and could inform efforts to treat malaria more efficiently and inexpensively.

“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 Ph.D. 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.”

Predicting A Malaria Outbreak

The most recent year for which figures are available from the World Health Organization is 2021 when about 619,000 individuals worldwide died from malaria, an acute fever sickness spread by mosquito bites. In Africa, where children under 5 accounted for almost 80% of all malaria deaths in 2021, its burden is felt most acutely by those who live in disadvantaged communities.

While healthcare organizations have a good understanding of what causes malaria at the national level, including warm weather and rain patterns that facilitate mosquito breeding and activity, local-scale predictions are much more difficult to make and uncertain due to factors like microclimates and land use. Data from the health system might also give a false impression of the burden on the community because it does not include those who are less able to obtain healthcare.

The researchers concentrated on a region in southeast Madagascar in cooperation with Pivot, a neighborhood health care group, and Madagascar’s national malaria control program. They expanded on a prior study done by Stanford that examined data on malaria incidence gathered by hospitals in the district and made adjustments to account for reporting biases caused by distance and cost constraints on access to care. The researchers added land use maps, satellite data on climate, and socioeconomic information from household surveys carried out by the Madagascar National Institute of Statistics.

The researchers used this mixture of data to determine which of these factors best explained malaria trends and built a model to forecast the monthly prevalence of malaria across 195 villages.

Malaria appears to be more of a rural disease in the study area than it is elsewhere, as evidenced by the researchers’ finding that the malaria burden is low in residential areas and high in regions with flooded rice fields. Also, they discovered a significant correlation between poverty and reported malaria cases, showing that many individuals living in poverty were not receiving care at health centers and emphasizing the urgent need to expand access to healthcare.

The analysis was able to predict relatively well which villages were going to be hit the hardest with malaria outbreak. In fact, the approach correctly identified more than half of communities in the top 20% for malaria transmission and explained over three-quarters of the variation in malaria incidence rank.

“We have shown that the new generation of satellite and land use data, integrated with socio-economic and public health data gathered on the ground allows to describe heterogeneity in malaria incidence at a very fine spatial scale,” said study co-author Giulio De Leo, a professor of oceans and Earth system science in the Stanford Doerr School of Sustainability. “That was impossible until recently.”

“This is an important first step towards bringing advances in disease ecology and modeling for disease prediction to local communities in settings that need them the most: those with high burdens of malaria, widespread poverty, and low access to health care,” said senior author Andres Garchitorena, a researcher at the French Research Institute for Sustainable Development and associate scientific director at Pivot.

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