Dataset Information

High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery.

ABSTRACT: Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aerial vehicles (drones) for identifying Nyssorhynchus darlingi (formerly Anopheles darlingi) breeding sites with high-resolution imagery (~0.02m/pixel) and their multispectral profile in Amazonian Peru. Our results show that high-resolution multispectral imagery can discriminate a profile of water bodies where Ny. darlingi is most likely to breed (overall accuracy 86.73%- 96.98%) with a moderate differentiation of spectral bands. This work provides proof-of-concept of the use of high-resolution images to detect malaria vector breeding sites in Amazonian Peru and such innovative methodology could be crucial for LSM malaria integrated interventions.

SUBMITTER: Carrasco-Escobar G

PROVIDER: S-EPMC6353212 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery.

Carrasco-Escobar Gabriel G Manrique Edgar E Ruiz-Cabrejos Jorge J Saavedra Marlon M Alava Freddy F Bickersmith Sara S Prussing Catharine C Vinetz Joseph M JM Conn Jan E JE Moreno Marta M Gamboa Dionicia D

PLoS neglected tropical diseases 20190117 1

Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aeria ...[more]

PMID: 30653491

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Project description:In Amazonian Peru, the primary malaria vector, Nyssorhynchus darlingi (formerly Anopheles darlingi), is difficult to target using standard vector control methods because it mainly feeds and rests outdoors. Larval source management could be a useful supplementary intervention, but to determine its feasibility, more detailed studies on the larval ecology of Ny. darlingi are essential. We conducted a multi-level study of the larval ecology of Anophelinae mosquitoes in the peri-Iquitos region of Amazonian Peru, examining the environmental characteristics of the larval habitats of four species, comparing the larval microbiota among species and habitats, and placing Ny. darlingi larval habitats in the context of spatial heterogeneity in human malaria transmission. We collected Ny. darlingi, Nyssorhynchus rangeli (formerly Anopheles rangeli), Nyssorhynchus triannulatus s.l. (formerly Anopheles triannulatus s.l.), and Nyssorhynchus sp. nr. konderi (formerly Anopheles sp. nr. konderi) from natural and artificial water bodies throughout the rainy and dry seasons. We found that, consistent with previous studies in this region and in Brazil, the presence of Ny. darlingi was significantly associated with water bodies in landscapes with more recent deforestation and lower light intensity. Nyssorhynchus darlingi presence was also significantly associated with a lower vegetation index, other Anophelinae species, and emergent vegetation. Though they were collected in the same water bodies, the microbial communities of Ny. darlingi larvae were distinct from those of Ny. rangeli and Ny. triannulatus s.l., providing evidence either for a species-specific larval microbiome or for segregation of these species in distinct microhabitats within each water body. We demonstrated that houses with more reported malaria cases were located closer to Ny. darlingi larval habitats; thus, targeted control of these sites could help ameliorate malaria risk. The co-occurrence of Ny. darlingi larvae in water bodies with other putative malaria vectors increases the potential impact of larval source management in this region.

Project description:BACKGROUND:Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae. METHODS:Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC). RESULTS:All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The predictability of habitat types varied. Drains, foot-prints, puddles and swamp habitat types were most predictable. CONCLUSIONS:Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

Dataset Information

High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery.

Publications

High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery.

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