Leveraging spatial structure to design spatially-targeted malaria interventions at the community-scale

Progress in malaria control has stagnated since the early 21st century in many countries, requiring new approaches such as the use of spatially-targeted interventions. Evidence on the effectiveness of spatially-targeted interventions is mixed. Their success can be dependent on whether the setting is endemic, the metrics used to target the intervention, and the spatial resolution and scale of deployment. We developed a two-age-class, spatially-explicit model of malaria at the community-scale for a district in southeastern Madagascar, accounting for environmental heterogeneity and human mobility. The model was fit to field-based case notifications and malaria prevalence data and then used to simulate three interventions: indoor residual spraying (IRS), long-lasting insecticide-treated nets (LLIN), and active case detection (ACD). We compared five spatial targeting scenarios for each simulated intervention: (i) equally distributed, (ii) targeting communities nearest or (iii) furthest from clinics, (iv) targeting communities with highest incidence, and (v) targeting communities that are spatially central. The non-targeted intervention was generally the most effective, but the least resource efficient. The second most effective intervention was based on spatial centrality, which reached a larger population while using fewer transportation resources than the equally distributed. No combination of interventions was able to eliminate malaria in the district, although a "perfect" ACD intervention could avert 100% of severe malaria cases. These results highlight the potential for targeted malaria interventions, especially in low-income settings, that take into account spatial structure in the human population and mobility to reduce malaria burden using fewer resources than conventional district-wide interventions.