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Stanton, Michelle 
ORCID: https://orcid.org/0000-0002-1754-4894, Kalonde, Patrick, Zembere, Kennedy, HoekSpaans, Remy and Jones, Chris ORCID: https://orcid.org/0000-0002-6504-6224
(2021)
'The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?'. Malaria Journal, Vol 20, Issue 244.
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s12936-021-03759-2.pdf - Published Version Available under License Creative Commons Attribution. Download (7MB) | Preview |
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 km2 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.
| Item Type: | Article |
|---|---|
| Subjects: | QX Parasitology > QX 4 General works WA Public Health > Health Problems of Special Population Groups > WA 395 Health in developing countries WA Public Health > Sanitation. Environmental Control > General Sanitation and Environmental Control > WA 670 General works WC Communicable Diseases > Tropical and Parasitic Diseases > WC 750 Malaria WC Communicable Diseases > Tropical and Parasitic Diseases > WC 765 Prevention and control |
| Faculty: Department: | Biological Sciences > Vector Biology Department |
| Digital Object Identifer (DOI): | https://doi.org/10.1186/s12936-021-03759-2 |
| Depositing User: | Samantha Sheldrake |
| Date Deposited: | 03 Jun 2021 10:13 |
| Last Modified: | 03 Jun 2021 10:13 |
| URI: | https://archive.lstmed.ac.uk/id/eprint/18012 |
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