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Warming Climate Boosts Malaria in Kenya

Summary

New research suggests rising temperatures in the highlands of East Africa are at least partly to blame for the increase in malaria transmission in recent decades.

In the highlands of East Africa, malaria transmission has skyrocketed over recent decades. New research suggests rising temperatures are at least partly to blame.

A mathematical model of malaria transmission developed by Howard Hughes Medical Institute (HHMI) scientists showed that warming could explain a significant part of the increase in malaria cases. While previous studies have considered instead scenarios for the future of the disease, the new study, published online November 10, 2010, in the British scientific journal, Proceedings of the Royal Society B, is one of the first to examine the effect of past warming on malaria transmission.

We know that climate may be playing a limiting role [in the spread of malaria].

Mercedes Pascual

Mosquitoes and the malaria parasites they carry have a limited range, largely because they need a certain amount of rainfall and particular temperatures to thrive. As temperatures increase, malaria-carrying mosquitoes may be able to infiltrate new areas and spread the disease to new populations. This is especially problematic because people who have had little exposure to the disease have no natural immunity.

HHMI investigator Mercedes Pascual and her colleagues decided to look at the relationship between warming and malaria in the highlands of Kenya. "When you go up in altitude, temperature decreases," she says. "We know that climate may be playing a limiting role [in the spread of malaria]."

Mosquito host—A mosquito becomes infected with malaria when it sucks the blood from an infected human. Once inside the mosquito, the parasites reproduce in the gut and accumulate in the salivary glands, ready to infect another human host with the next bite.
Video: HHMI Biointeractive

The researchers focused on a tea plantation in Kenya's Kericho district, which lies west of the Great Rift Valley. The plantation has a hospital for its workers and their families—some 50,000 people—with records going back 30 years. Finding such a long, consistent dataset is rare in Africa, Pascual says, which makes the plantation a good study site.

Data from two local meteorological stations suggest that the region warmed about one degree between 1970 and 2003. "We wanted to ask whether there was already evidence for an effect of the rising temperature [on malaria] in this region," Pascual says.

Pascual's approach to problems such as these is to build a mathematical model. Her models are already changing the way scientists think about how climate variability influences the dynamics of infectious diseases. One of the long-term goals of her research is to build computational tools that will help scientists identify when epidemics will occur, thereby enabling them to alert public health agencies. Those agencies, in turn, would be in a better position to implement prevention measures and meet the increased demand for lifesaving medicine and supplies when an epidemic occurs.

For the current study, Pascual and her colleagues developed a mathematical model that takes into account both the human population and the mosquitoes. Several of the model's parameters can be influenced by changes in temperatures, including the lifespan of the mosquito, how frequently it bites, and the length of time it will take the parasite to develop inside the mosquito.

Pascual and her colleagues used data from the early years—1970 to 1985—to help calibrate their model. During that time, the warming had already begun, Pascual says, but the number of malaria cases hadn't yet started to rise. To address the effect of rising temperatures, they turned to the second half of the data, collected from 1986 to 2003.

First, they asked their model to predict the number of malaria cases the tea plantation could expect if no warming had occurred. Then, they added in rising temperatures. As expected, the number of malaria cases increased. In fact, the seasonal peaks were about eight times larger than what the researchers observed in the no-warming scenario. The trends were similar for both an altitude of 1,780 meters and 1,880 meters.

Next, Pascual and her colleagues compared the model's predictions with the actual data. The model predicted fewer cases of malaria than had actually been recorded, but that's not surprising, Pascual says. "We know that there are other factors that are also contributing to more malaria in the region," she says. "The frequency of drug resistance in the parasite is increasing. There is more movement of humans in the region." Land use change also plays a role.

Pascual acknowledges that the model doesn't take every variable into account. "Regardless of how careful one is, models will always be a fairly simple representation of reality," she says. However, the researchers ran the model in a number of different ways to ensure that small changes wouldn't affect the overall trend. They changed the size of the population from 50,000 to 25,000 and 75,000. They also looked at several different ways of representing disease immunity in the human population. None of these changes had a significant impact on the model's predictions.

"I'm certain there has been a significant effect of warming," Pascual says. She points out, however, that it will be difficult to tease apart what proportion of the increase in malaria was due to warming and what proportion was due to other factors.

Pascual says the relationship between warming and malaria she and her colleagues observed in Kenya will likely hold true for other highland regions. The researchers plan to apply their model to address malaria increases in the highlands of Ethiopia. But she points out that their predictions don't necessarily mean that malaria will become a problem everywhere. "This kind of gloom scenario is not reasonable," she says. "But I also think it's not reasonable to say, 'climate change will not matter.'"

Scientist Profile

Investigator
University of Michigan
Computational Biology, Epidemiology

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