Work

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

Year: 2026

Type: article

Abstract: 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. However, evidence on the effectiveness of spatially-targeted interventions is mixed, depending on both the setting and intervention. In addition, the potential role of community health programs in implementing spatial targeting is underexplored, with models focusing on coarser spatial scales than those needed to guide programmatic activities. 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 spatial structure, to explore spatially-targeted community health interventions. The model was fit to field-based case notifications and malaria prevalence data and then used to simulate three interventions: indoor residual spraying (IRS), supplemental coverage of long-lasting insecticide-treated nets (LLIN), and active case detection (ACD). We compared five scenarios, one with no spatial targeting and others targeting communities (i) nearest to clinics, (ii) furthest from clinics, (iii) with highest incidence, and (iv) 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 non-targeted intervention. No combination of interventions was able to eliminate malaria in the district, although detecting symptomatic cases through ACD could avert 100% of severe malaria cases at high detection rates. 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 transport networks to reduce malaria burdens using fewer resources than conventional district-wide interventions. [A French language abstract is available as Text A in S1 Appendix].

Source: PLoS Computational Biology

DOI: 10.1371/journal.pcbi.1014857