利用空间结构在社区尺度设计空间靶向疟疾干预
Leveraging spatial structure to design spatially-targeted malaria interventions at the community-scale
研究构建马达加斯加东南部某区的双年龄组空间显式疟疾模型，拟合实地病例报告与患病率数据，模拟IRS、LLIN补充覆盖和ACD三种干预在五种空间靶向情景下的效果。非靶向干预总体最有效但资源效率最低，基于空间中心性的靶向策略覆盖人口更多、运输资源更少。任何干预组合均未能消除该区疟疾，但高检测率下ACD可避免100%的重症疟疾病例。
来源：PLOS Computational Biology · journals.plos.org
by Michelle V. Evans, Benjamin Roche, Vincent Herbreteau, Christophe Révillion, Thibault Catry, Matthew H. Bonds, Karen E. Finnegan, Ezra Mitsinjoniala, Felana A. Ihantamalala, Mauricianot Randriamihaja, Oméga Raobela, Andres Garchitorena
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].