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