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

Abstract

O_LISpatial and 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 removing habitat and/or reduce the likelihood of larvae developing into adults, 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, we use our experiences of drone-led larval habitat identification in Malawi to assess the accuracy and practicalities of this approach. C_LIO_LIDrone imagery and larval surveys were conducted in Kasungu district, Malawi between 2018-2020. Water bodies and aquatic vegetation were identified in the imagery using both manual methods and geographical object-based image analysis (GeoOBIA) and the performance of the classifications were compared. Larval sampling sites were characterised by biotic factors visible in drone imagery (e.g. vegetation coverage, type), and generalised linear mixed models were used to determine their association with larval presence. C_LIO_LIImagery covering an area of 8.9km2 across eight sites was captured. Characteristics associated with rural larval habitat were successfully identified using GeoOBIA (e.g. median accuracy = 0.98, median kappa = 0.96 using a standard RGB camera), with a median of 18.3% being classed as surface water, compared to 20.1% using manual identification. The GeoOBIA approach, however, required greater processing time and technical skills. Larval samples were captured from 326 sites, and a relationship was identified between larval presence and vegetation (log-OR=1.44, p=0.01). Vegetation type was also a significant factor when considering late stage anopheline larvae only. C_LIO_LIOur study demonstrates the potential for drone-acquired imagery as a tool to support the identification of mosquito larval habitat in rural areas where malaria is endemic. There are, however, technical challenges to overcome before it can be smoothly integrated into malaria control activities. Further consultations between experts and stakeholders 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. C_LI