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Scientists Use Satellites, Population Data to Build Malaria Early Warning System

A Johns Hopkins University scientist is part of a team working on a method to predict malaria outbreaks months in advance, potentially giving public health officials a chance to protect people from a disease that poses a risk to nearly half the world’s population and kills hundreds of thousands a year.

Benjamin Zaitchik, associate professor in the Johns Hopkins Department of Earth & Planetary Sciences is working on a formula that takes into account the movements of people and changing environmental conditions to forecast where and when the mosquito-borne disease will emerge.   With funding from NASA, the scientists are working in collaboration with the governments of Peru and Ecuador and using NASA satellite information on ground conditions.

As co-investigator, Zaitchik, who specializes in climate and hydrology, said the Johns Hopkins part of the project “is to provide the Earth observation backbone for the early warning system. We work on understanding, monitoring and predicting variability in rainfall, soil moisture, evaporation, anything else in the environment that might influence where mosquitos that transmit malaria might end up.”

William Pan, assistant professor of global environmental health at Duke University, is the principal investigator. His role is focused on disease transmission and the public health response to outbreaks.

Together Zaitchik and Pan are seeking a way to precisely predict where malaria outbreaks will occur 12 weeks in advance. That would give public health officials time to take steps to prevent the disease, including administering medications, spraying insecticides and providing bed nets.

“The problem is that you don’t know where the outbreak will happen and you don’t necessarily have the resources to be everywhere at all times,” Zaitchik said. “An early warning system allows the government to move in in advance of the outbreak to try to deploy some of these known preventative measures before malaria takes over a region.”

As it is, the government of Peru – where malaria commonly appears in the eastern portion of the country near the Amazon River – often distributes such resources widely in hopes of covering anyone who might be affected. That can waste materials that could be used more efficiently in a targeted effort.

Dr. Peter Agre, Bloomberg Distinguished Professor and Director of the Johns Hopkins Malaria Research Institute – who is not involved in this project — said the warning system project is promising.

“Dr. Zaitchik and his collaborators have developed important new technology that should allow identification of potential malaria outbreaks by satellite imaging of ground conditions,” Agre said. “By predicting mosquito breeding, public health officials may have essential advance notice to protect the populace,” he said, adding that the system could potentially help avert outbreaks of other mosquito-borne diseases such as yellow fever, dengue, and Zika.

Malaria causes a range of symptoms, from fever and fatigue in mild cases to seizures, coma and death. According to the World Health Organization, in 2015 there were about 212 million malaria cases and 429,000 malaria deaths. The WHO reported that since 2010, prevention and control efforts have contributed to a nearly 30 percent decline in malaria deaths.

The disease is most commonly spread by the female Anopheles mosquito, which lays its eggs in still water in warm weather. To chart conditions in which mosquitos thrive and make outbreaks more likely, Pan and Zaitchik are relying on the Land Data Assimilation System (LDAS). The land-surface tracking project is a collaboration of universities and federal agencies relying in part on information compiled by NASA satellites.

At the moment, the information on ground conditions is more complete than the information on people, including migration associated with seasonal labor. That information is key, as the prediction model will have to account for where people are getting infected and where they’re traveling.

As the project stands, Zaitchik said they have a “descriptive, explanatory model” taking account of all these elements. The next step is to turn this into “an actionable, predictive model” that would be specific enough to provide a guide for public health response, and to expand the forecast range from two to three months.

To provide the longer warning time, Zaitchik said, “either we get better at knowing how malaria epidemics develop, and that would be on the malaria modeling side of the project. Or, we get better at predicting the environmental conditions we already know of indicative of risk.”

Source: Johns Hopkins University

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