Reveal and GRID3 Bring Geospatial Data to the Ground for Malaria Prevention

By Anabelle Nuelle on July 26, 2020 in GIS, Malaria, News

Zambia, along with a handful of countries within Southern and Eastern Africa, is on track to reduce malaria cases 40% by 2020. Relative to 2015, the country’s progress so far translates to as many as 700,000 cases prevented annually. Such tremendous strides speak to the success of preventative interventions, such as improved access to indoor residual spraying (IRS) and insecticide treated nets (ITNs), but the work is far from over.

The citizens and communities across Zambia rely on annual district planning to determine where, how much of, and what interventions are needed in a particular population. In answering these questions, district teams begin their microplanning processes, determining on a local level the nuances of the year’s malaria interventions. These teams must deeply understand the communities they serve. Who is at greatest risk of infection? Where do they live? What settlements ought to be targeted? And what resources are needed to bring a community out of harm’s way? Lacking this information, district teams cannot fully grasp the extent of preventable malaria cases and consequently limit their capacity to act.

Traditionally, teams of local community health workers aggregate this information on foot and process it in hard copy. This costly and time consuming work flow jeopardizes the data’s accuracy, totality, and speedy delivery to key decision makers. System bottlenecks, limited resources, or a lack of confidence in the data can then undermine the quantitative foundation of an intervention. Weary decision makers might turn instead to outdated data and an imprecise understanding of the population they aim to serve. What ought to be a concrete step in the year’s plan bends to inefficacious circumstances.

Reveal—spatial intelligence for planning, targeting, and real-time management of interventions in low-resource settings – A Case Study

By Kyle Hutchinson on June 28, 2020 in Capacity Building, GIS, Malaria, News

The Denominator Challenge

Accurate quantification of a population, and the ability to locate this population with precision, are fundamental requirements for reporting the true coverage and effectiveness of public health interventions—such as childhood immunizations, indoor residual spraying (IRS) for malaria, or mass drug administration (MDA) for neglected tropical diseases.

Public health interventions, however, often rely on field teams to locate rural villages or even homes on the ground. In areas where there are no street address systems, or where homes are not mapped, manual searches often result in groups of households being missed; thus preventing the delivery of services to those in need. When service coverage is subsequently reported as a function of the population found, the impact and effectiveness of an intervention may be overstated.

Spatial Intelligence and the Reveal Solution

Reveal features a field-verified denominator, data collection points, and maps.

The transformative field of spatial intelligence is revolutionizing digital health and public health more broadly. Artificial intelligence (AI), digital maps, and spatial modeling are powerful, burgeoning toolsets; but until more recently, they have not benefited field workers and large-scale, labor-intensive campaigns. Now, the power of these digital tools is being accessed by field workers in rural, underserved communities. 

Reveal, an open-source platform and global good, uses spatial intelligence to help field workers effectively navigate and deliver life-saving interventions to people who previously would have been missed, increasing the true coverage of interventions and improving health outcomes for vulnerable populations.

Supporting an IRS Campaign in Zambia

Satellite imagery was enumerated to establish a baseline understanding of structure count and spatial distribution in several districts. These were layered with risk maps to target high-risk regions, which enabled users to identify eligible households and assign teams to priority areas.

Using Reveal’s mobile and map-based interface, field workers were able to navigate through communities, identify targeted households, and collect intervention data against eligible households in a coordinated manner within and across teams. The near real-time feedback of data, as a result of the mobile application’s offline and peer-to-peer (P2P) syncing functionality, inspired increased teamwork and cohesion as the campaign progressed and teams worked toward a common goal.

Reveal uses electronic data collection forms that are smart and easy-to-use to ensure data quality, collect GPS data, and provide real time data feedback for decision making.

Through dashboards, map-based visualizations, and built-in feedback loops, intervention managers were able to actively monitor campaign progress toward targets, in a given spray area and as a whole, thus facilitating data-driven course correction to optimize performance and maximize impact.

With the support of Reveal, Siavonga District increased its absolute coverage of IRS from 51.5% to 75.5%, while Sinazongwe increased from 31.5% to 61.9%. These changes in coverage were possible due to a better understanding of resource needs. In other words, the use of Reveal allowed districts to better understand the size and distribution of the target population, thus impacting planning and implementation.