An overview of technologies available to monitor behaviours of mosquitoes.

ABSTRACT

Mosquito-borne diseases such as malaria, dengue, Zika, and chikungunya cause significant morbidity and mortality globally, resulting in over 600,000 deaths from malaria and around 36,000 deaths from dengue each year, with millions of people infected annually, leading to substantial economic losses. The existing mosquito control measures, such as long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS), helped to reduce the infections. However, mosquito-borne diseases are still among the deadliest diseases, forcing us to improve the existing control methods and look for alternative methods simultaneously. Advanced monitoring techniques, including remote sensing, and geographic information systems (GIS) have significantly enhanced the efficiency and effectiveness of mosquito control measures. Mosquitoes' behavioural traits, such as locomotion, blood-feeding, and fertility are the key determinants of disease transmission and epidemiology. Technological advancements, such as high-resolution cameras, infrared imaging, and artificial intelligence (AI) driven object detection models, including groundbreaking convolutional neural networks, have provided efficient and precise options to monitor various mosquito behaviours, including locomotion, oviposition, fertility, and host-seeking. However, they are not commonly employed in mosquito-based research. This review highlights the novel and significant advancements in behaviour-monitoring tools, mostly from the last decade, due to cutting-edge video monitoring technology and artificial intelligence. These advancements can offer enhanced accuracy, efficiency, and the ability to quickly process large volumes of data, enabling detailed behavioural analysis over extended periods and large sample sizes, unlike traditional manual methods prone to human error and labour-intensive. The use of behaviour-assaying techniques can support or replace existing monitoring techniques and directly contribute to improving control measures by providing more accurate and real-time data on mosquito activity patterns and responses to interventions. This enhanced understanding can help establish the role of behavioural changes in improving epidemiological models, making them more precise and dynamic. As a result, mosquito management strategies can become more adaptive and responsive, leading to more effective and targeted interventions. Ultimately, this will reduce disease transmission and significantly improve public health outcomes.