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Cited by 84Malaria Research and ControlMosquito-borne diseases and controlDengue and Mosquito Control Research

The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

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Stanton, Michelle C., Kalonde, Patrick, Zembere, Kennedy, Hoek Spaans, Remy, Jones, Christopher M.·2021·Malaria Journal
DOI 10.1186/s12936-021-03759-2PMC PMC8165685

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Abstract

Background: Spatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors’ experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this approach. Methods: Drone mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval presence. Results: Imagery covering an area of 8.9 km 2 across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy = 98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval presence. Conclusions: This study demonstrates the potential for drone-acquired imagery to support mosquito larval habitat identification in rural, malaria-endemic areas, although technical challenges were identified which may hinder the scale up of this approach. Potential solutions have however been identified, including strengthening linkages with the flourishing drone industry in countries such as Malawi. Further consultations are therefore needed between experts in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited in malaria control.

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  • LSM is known to be efficient where habitats are findable, few and fixed.

    “LSM is known to be efficient where habitats are findable, few and fixed (Djamouko‐Djonkam et al., 2019; Stanton et al., 2021).”

    From Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology‐Based Model (2023) · cites this paper as Stanton et al., 2021

    The Background states that WHO recommends LSM in rural areas where larval sources are ‘few, fixed and findable,’ alongside other conditions. It does not establish that LSM is known to be efficient whenever those three habitat criteria are met; that wording is stronger and omits the other conditions the paper names. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
Stanton, M. C., Kalonde, P., Zembere, K., Hoek Spaans, R., & Jones, C. M.
Affiliations
  • Liverpool School of Tropical Medicine (GB)
  • Malawi-Liverpool-Wellcome Trust Clinical Research Programme (MW)
Corresponding author
Stanton, Michelle C.
ORCID
Venue
Malaria Journal
Publisher
Springer Science and Business Media LLC
Published
2021-05-31
Volume
20
Issue
1
Pages
244-244
ISSN
1475-2875 (ISSN-L)
DOI
10.1186/s12936-021-03759-2
PMC
PMC8165685
OpenAlex
W3164982201
Cited by
84
References
2
Citations by year
2026: 21 · 2025: 10 · 2024: 17 · 2023: 21 · 2022: 12 · 2021: 3
FWCI
11.48
Type
journal article
Language
en
Open access
Licence
Locations
  • Malaria Journal (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Medical Research Council (MR/M014975/1)
  • Wellcome Trust (215184/A/19/Z)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Malaria Research and Control
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mosquito-borne diseases and control
  • Social Sciences › Sociology and Political Science › Dengue and Mosquito Control Research
Keywords
unmanned aerial vehicles, mosquito larval habitats, larval source management, malaria control, drone mapping, aquatic plants, water bodies, rural malaria, near-infrared imagery, vector control, Malawi
MeSH terms
Aircraft, Animals, Communicable Disease Control, Larva, Malaria, Mosquito Control, Culicidae, Ecosystem

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