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

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.

Best Practices and Lessons Learned through Reveal Implementations

Design with the user. Reveal is a context-appropriate solution, developed alongside country-level implementation programs, that ensures all user groups are included in the incremental and iterative development of the platform. 

Be collaborative. Reveal is supported by a consortium of organizations, which allows for input from experts within a diverse range of disciplines and industries. 

Design for scale. Reveal has been implemented in Asia and several countries across Africa, all with varying geographies and contexts. This allows for extensive learning opportunities that factor back into the design and development of Reveal.

Build for sustainability. Reveal is open-source, and is implemented with the goal of building local capacity. This ensures buy-in to the platform and implementation process, while facilitating a transition to local ownership.

Test appropriately. Reveal is tested in the field, at the expected volume. This is a necessity prior to implementation, given that it is implemented in areas that do not always have reliable power and network.

Support the user. Ensure users engage with the tool regularly, such that it becomes an intimate part of the intervention workflow. Cover technical issues that users may encounter, as well as strategies to manage them. Furthermore, ensure support networks are feedback-driven. As issues are reported, solutions, fixes, and non-fixes must be communicated. This keeps users engaged and builds trust that the process leads to improvements. 

Demonstrate value. Implementers become advocates of a tool as soon as value is demonstrated, which often happens through pilots supported by external partners. True ownership is when, driven by a recognized value, key personnel understand and expect implementation challenges, and are willing and capacitated to manage them.