Evidence brief: Field evaluation of drone and AI assisted larval source management in Ghana

Abstract

Malaria remains a major public health burden in sub-Saharan Africa. In Ghana, in particular, larval source management (LSM) is increasingly recognized as a complementary vector control strategy. This study evaluates a field-adapted LSM approach that integrates drone-based mapping and artificial intelligence (AI)–driven site prioritization to enhance operational efficiency and reduce resource use.

The intervention replaces conventional manual scouting with aerial mapping conducted one day prior to larvicide application. An AI model analyzes geospatial and morphological features of water bodies to identify high-risk larval habitats. Site coordinates are transmitted to field teams via mobile devices for targeted treatment. A comparative field trial was conducted in eight administrative sub-districts within Ghana’s Eastern Region. Four sub-districts implemented the drone- and AI-assisted approach, while four served as controls using standard LSM procedures. A mixed-methods evaluation was employed, incorporating quantitative metrics and qualitative field insights.

Drone-assisted mapping led to more than a threefold increase in the number of identified breeding sites. AI-based targeting reduced larvicide consumption by over 60%. The combined technologies lowered worker requirements by approximately 50%. Despite these reductions, malaria case trends in the intervention sub-districts remained comparable to those in the control sub-districts. The study’s limitations include its restriction to the dry season and below-average rainfall, which may have influenced mosquito abundance and transmission.

Drone- and AI-assisted LSM demonstrated substantial resource savings without compromising vector control outcomes. Further longitudinal evaluation across transmission seasons is warranted to assess sustained effectiveness and inform national policy.

Introduction

Global efforts to control malaria have long relied on integrated vector management strategies, including long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), and prompt treatment of clinical cases [1–3]. Yet, malaria remains a significant global health threat. In 2023, an estimated 263 million cases and 597000 deaths were reported across 85 countries, with sub-Saharan Africa accounting for approximately 94% of all cases and 95% of deaths [4–6]. The burden disproportionately affects vulnerable populations, particularly children under five and pregnant women, while socioeconomic and geographic inequities further exacerbate barriers to access. Children from the poorest households in sub-Saharan Africa are five times more likely to be infected than those from wealthier households [5].

Despite intensified global action, recent trends show stagnation in malaria control, threatening the achievement of Sustainable Development Goal 3 targets. In response, the WHO’s Global Technical Strategy for Malaria 2016–2030 aims to reduce incidence and mortality by 90% by 2030 through three pillars: scaling up responses, increasing investment, and promoting innovation. One such innovation is larval source management (LSM), which targets mosquito breeding sites through larviciding, environmental manipulation, and habitat modification [7,8]. LSM also includes biological control agents such as larvivorous fish. Gambusia affinis has long been recognized as an efficient predator of mosquito larvae in permanent aquatic habitats, and in Ghana, studies have examined the prey selectivity and captive-breeding feasibility of the native lampeye killifish (Aplocheilichthys spilauchen) for potential use in mosquito biocontrol [9,10]. However, in large-scale LSM targeting densely populated areas, many breeding sites are temporary pools where fish cannot survive, making wide-scale deployment of fish neither practical nor sustainable.