By Andy Evangelista
Malaria is one of the deadliest diseases on Earth. But remarkable progress has been made over the past 50 years, and now several countries are on the verge of eliminating this mosquito born disease.
DiSARM (Disease Surveillance and Risk Monitoring) – developed and refined continuously by UCSF researchers – could be the technological closer to make eliminating countries finally malaria-free.
In malaria-eliminating countries, health workers in remote villages typically go house to house to track every case of malaria and collect a GPS point with the precise infection locations. DiSARM combines that data with satellite imagery – collected and sorted using Google Earth Engine – of conditions such as rainfall, temperature, vegetation, water proximity and elevation, all of which affect mosquito breeding and parasite growth.
“It produces ‘risk maps’ for health programs which must decide how best to use scarce resources immediately and in the future,” explained Hugh Sturrock, PhD, assistant professor of epidemiology and biostatistics, who leads the DiSARM project for the Malaria Elimination Initiative, a program of the Global Health Group in UCSF’s Institute for Global Health Sciences (IGHS).
DiSARM provides malaria programs a near real-time view of the disease and where – even in the tiniest geographic pockets -- it could strike. Using machine learning, DiSARM can also predict which buildings are residential, and require visiting, and which are commercial or other non-residential. Combined with risk maps, programs can then plan where to deliver interventions such as insecticide spraying, mosquito nets or antimalarial treatments down to specific individual households.
These highly precise risk maps are critical in eliminating countries, as they get close to zero malaria cases. “Often, countries make the mistake of cutting their efforts too soon,” he said. “Malaria can rebound quickly. But with these maps, health workers will be alerted and know when and where to target their resources until the disease is totally eliminated.”
If a malaria case is pinpointed in a low and wet area, for example, the map can steer malaria workers to other areas with similar geographic and environmental conditions.
DiSARM is also accessible on mobile devices so that field teams can see locations of buildings they need to target using offline maps. They also can use the app to collect data even when they don’t have connectivity in remote areas, said Sturrock.
DiSARM is being implemented in Namibia, Botswana, Zimbabwe and Swaziland with support from the Clinton Health Initiative, who help to provide constant feedback about the crucial data they collect in villages, schools, hospitals and other locations that are rigorously monitored.
For example, in Zimbabwe, which has achieved a sharp decline in malaria – a 74 percent drop in cases from 2005 to 2015 – DiSARM supports indoor residual spraying efforts in two provinces – Matabeleland South and North – that are on the cusp of malaria elimination.
But DiSARM is not only a valuable closer. Malaria afflicts 212 million people worldwide and kills some 429,000, mostly children, and the tool has great potential in the global fight against the disease. “Even in higher transmission settings, there is a need to ensure places are sprayed and to monitor progress, which DiSARM is designed to do,” said Sturrock.
And while the technology was developed to target malaria, DiSARM is flexible and can be adapted to predict other infectious and mosquito-borne diseases such as Zika, Dengue and Chikugunya.
In addition to risk mapping, it has potential for treatment and disease control, even in urban areas of high-risk countries, said Sturrock. In Swaziland, Zimbabwe and Botswana, millions of buildings are being mapped. Again, by combining surveillance and satellite data, health workers, for example, can identify and target building-specific residents for vaccination or prevention programs.
The beauty of DiSARM is that it gives the power of prediction to intervention teams on the ground through a single platform that analyzes the many factors that contribute to a disease.