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

@article{evans2026leveraging,
  title = {{Leveraging spatial structure to design spatially-targeted malaria interventions at the community-scale}},
  author = {Michelle V Evans and Benjamin Roche and Vincent Herbreteau and Christophe Révillion and Thibault Catry and Matthew H Bonds and Karen E Finnegan and Ezra Mitsinjoniala and Felana A Ihantamalala and Mauricianot Randriamihaja and Oméga Raobela and Andres Garchitorena},
  journal = {PLOS Computational Biology},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pcbi.1014857},
  url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014857},
  note = {CC BY},
}

TY  - JOUR
TI  - Leveraging spatial structure to design spatially-targeted malaria interventions at the community-scale
AU  - Michelle V Evans
AU  - Benjamin Roche
AU  - Vincent Herbreteau
AU  - Christophe Révillion
AU  - Thibault Catry
AU  - Matthew H Bonds
AU  - Karen E Finnegan
AU  - Ezra Mitsinjoniala
AU  - Felana A Ihantamalala
AU  - Mauricianot Randriamihaja
AU  - Oméga Raobela
AU  - Andres Garchitorena
PY  - 2026
DA  - 2026/10/15
JO  - PLOS Computational Biology
DO  - 10.1371/journal.pcbi.1014857
UR  - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014857
AB  - 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]. Author summary: As progress against malaria slows, spatially-targeted interventions are one tool to control malaria in resource-limited contexts. Fine-scale targeting requires acting at the community scale, relying on community health workers to help implement programs. Using a mathematical model based on field-derived environmental, prevalence, and transport network data, we modeled malaria at the community-scale for a health district in Madagascar. We then used this model to compare different scenarios of spatially-targeted interventions using supplemental mosquito net distributions (LLIN), indoor residual spraying (IRS), and active case detection (ACD). IRS and ACD averted the most simple and severe malaria cases, respectively. Non-targeted interventions were the most effective, but required the most resources to implement; targeting based on the underlying transport and population structure averted a similar number of cases while using 2/3 of the resources. This work can help inform community malaria programs that wish to implement spatially-targeted interventions and is supported by a web application to explore different scenarios for community health programs in the district.
SN  - 1553-7358
SN  - 1553-734X
ER  -