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

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.

Improved Use of Drone Imagery for Malaria Vector Control through Technology-Assisted Digitizing (TAD)

“…In the case of malaria, important mosquito breeding sites can often be smaller than 1 m and therefore generating maps with high precision is crucial, this representing a significant drawback of most satellite Earth Observation based solutions [7]. Perhaps more significantly, drones can be flown under the clouds, overcoming a major limitation of optical satellite imaging solutions, especially in tropical and temperate locations where cloud cover is persistent [7][8][9][10]. Additionally, due to the relatively low-cost and ease-of-use of many drone systems, there is greater potential for them to be owned and operated by disease control managers (e.g., National Malaria Control programs) rather than relying on external organizations, often from the Global North.…”

“…Using drones, imagery can be acquired rapidly over relatively large areas, e.g., ~400-600 ha (4-6 km 2 ) in a single day (although this figure varies considerably according to flying height, camera type, drone-use restrictions, drone type). This imagery can be used to generate maps of potential mosquito habitats [7][8][9]11,12], that can subsequently be used by ground-based teams to direct their LSM programme. This approach can revolutionize a key component of LSM where maps are produced conventionally through ground-based efforts that require a large number of fieldworkers, with coverage often being limited by accessibility issues due to private land/property or challenging terrain (e.g., dense thicket or forest, extensive swamp or flooded areas such as rice paddies).…”

“…Specifically, in terms of mapping potential malarial mosquito larval habitats, previous studies have focussed on the use of supervised image classification approaches [9,11], which offers a number of benefits. Following the manual delineation of training data, a trained classifier can be used to rapidly identify features over the entire image, representing efficiency gains over a manual digitizing approach where a human operator is expected to manually delineate every feature of interest (i.e., a potential breeding site) in the image.…”

“…For this to work reliably, the originally trained classifier must be representative of any new site: for tropical landscapes this is a challenge as they are often heterogenous in nature with dynamic hydrological conditions, i.e., the same site will have very different land cover characteristics between the wet and dry seasons. In Malawi, Stanton et al (2021) assessed the quality of a Random Forest supervised classification to create maps of potential malarial mosquito larval habitats. Although the overall results demonstrated good agreement with test samples (mean overall accuracy 91%), when the trained classifier was applied outside the initial calibration area, the accuracy noticeably reduced (mean overall accuracy 76%).…”

Conditional trust: Community perceptions of drone use in malaria control in Zanzibar

“…Drone technology has the potential to provide benefits for a wide range of sectors and applications. The advantages that drones can offer to the public health sector have been acknowledged and their integration into practical public health interventions and operations are beginning to be realised, notably in the way medical supplies can be delivered and how disease risk can be mapped and controlled [ [1] , [2] , [3] , [4] , [5] , [6] , [7] ]. In malaria control, there is growing evidence that drones can be used for mapping mosquito vector habitats [ [1] , [2] , [3] , [4] , 7 ] potentially providing a step-change in the way that vector control interventions are delivered.…”