Field Effectiveness of Drones to Identify Potential Aedes aegypti Breeding Sites in Household Environments from Tapachula, a Dengue-Endemic City in Southern Mexico

Barriers and opportunities for community engagement in UAV-based dengue management in rural Malaysia

Dengue fever remains a significant public health issue in Malaysia, particularly in rural areas where unique challenges such as dispersed populations, limited infrastructure, and distinct socio-cultural dynamics complicate vector control efforts. Drone technology has emerged as an innovative tool for dengue management, offering capabilities such as aerial surveillance and targeted interventions. However, its adoption in rural communities is hindered by barriers related to community engagement and acceptance. This study aims to evaluate the barriers and opportunities for community engagement in drone-based dengue management within rural Malaysian settings. A cross-sectional study was conducted across six states representing rural Malaysia: Kelantan, Terengganu, Pahang, Johor, Kedah, and Perlis. A total of 190 participants were recruited using a stratified purposive sampling method. Data were collected via structured questionnaires assessing sociodemographic characteristics, perceptions of drone technology, and willingness to engage in dengue prevention activities, such as downloading a dengue-related application or participating in mosquito control training programs. Descriptive statistics and multinomial logistic regression were used to analyze factors influencing community engagement. Participants were predominantly female (67.4%) and of Malay ethnicity (>90%). Younger participants (<40 years) showed significantly lower willingness to participate in training programs ("Maybe" vs. "No": OR = 0.255, 95% CI: 0.067-0.968, p = 0.045), while age was not a significant predictor for app adoption. Negative perceptions of drone use and sociodemographic factors, such as housing type and residency duration, did not significantly influence willingness to engage. Despite these findings, qualitative responses highlighted concerns related to privacy, trust, and technological accessibility in rural areas. Drone-based dengue management in rural Malaysia faces challenges in community engagement, particularly among younger demographics. Tailored strategies, such as gamified training programs and targeted awareness campaigns, are necessary to address barriers and foster acceptance. These findings provide critical insights for developing inclusive and effective public health interventions leveraging drone technology in resource-limited rural settings.

Comparison of unmanned aerial vehicle imaging to ground truth walkthroughs for identifying and classifying trash sites serving as potential Aedes aegypti breeding grounds

BACKGROUND

Trash piles and abandoned tires that are exposed to the elements collect water and create productive breeding grounds for Aedes aegypti mosquitoes, the primary vector for multiple arboviruses. Unmanned aerial vehicle (UAV) imaging provides a novel approach to efficiently and accurately mapping trash, which could facilitate improved prediction of Ae. aegypti habitat and consequent arbovirus transmission. This study evaluates the efficacy of trash identification by UAV imaging analysis compared with the standard practice of walking through a community to count and classify trash piles.

METHODS

We conducted UAV flights and four types of walkthrough trash surveys in the city of Kisumu and town of Ukunda in western and coastal Kenya, respectively. Trash was classified on the basis of a scheme previously developed to identify high and low risk Aedes aegypti breeding sites. We then compared trash detection between the UAV images and walkthrough surveys.

RESULTS

Across all walkthrough methods, UAV image analysis captured 1.8-fold to 4.4-fold more trash than the walkthrough method alone. Ground truth validation of UAV-identified trash showed that 94% of the labeled trash sites were correctly identified with regards to both location and trash classification. In addition, 98% of the visible trash mimics documented during walkthroughs were correctly avoided during UAV image analysis. We identified advantages and limitations to using UAV imaging to identify trash piles. While UAV imaging did miss trash underneath vegetation or buildings and did not show the exact composition of trash piles, this method was efficient, enabled detailed quantitative trash data, and granted access to areas that were not easily accessible by walking.

CONCLUSIONS

UAVs provide a promising method of trash mapping and classification, which can improve research evaluating trash as a risk factor for infectious diseases or aiming to decrease community trash exposure.

Deep learning in disease vector image identification

Vector-borne diseases (VBDs) represent a critical global public health concern, with approximately 80% of the world's population at risk of one or more VBD. Manual disease vector identification is time-consuming and expert-dependent, hindering disease control efforts. Deep learning (DL), widely used in image, text, and audio tasks, offers automation potential for disease vector identification. This paper explores the substantial potential of combining DL with disease vector identification. Our aim is to comprehensively summarize the current status of DL in disease vector identification, covering data collection, data preprocessing, model construction, evaluation methods, and applications in identification spanning from species classification to object detection and breeding site identification. We also discuss the challenges and possible prospects for DL in disease vector identification for further research. © 2024 Society of Chemical Industry.

Innovative technologies to address neglected tropical diseases in African settings with persistent sociopolitical instability

The health, economic, and social burden of neglected tropical diseases (NTDs) in Africa remains substantial, with elimination efforts hindered by persistent sociopolitical instability, including ongoing conflicts among political and ethnic groups that lead to internal displacement and migration. Here, we explore how innovative technologies can support Africa in addressing NTDs amidst such instability, through analysis of WHO and UNHCR data and a systematic literature review. Countries in Africa facing sociopolitical instability also bear a high burden of NTDs, with the continent ranking second globally in NTD burden (33%, 578 million people) and first in internal displacement (50%, 31.6 million people) in 2023. Studies have investigated technologies for their potential in NTD prevention, surveillance, diagnosis, treatment and management. Integrating the evidence, we discuss nine promising technologies-artificial intelligence, drones, mobile clinics, nanotechnology, telemedicine, augmented reality, advanced point-of-care diagnostics, mobile health Apps, and wearable sensors-that could enhance Africa's response to NTDs in the face of persistent sociopolitical instability. As stability returns, these technologies will evolve to support more comprehensive and sustainable health development. The global health community should facilitate deployment of health technologies to those in greatest need to help achieve the NTD 2030 Roadmap and other global health targets.

Diflubenzuron larvicide auto-dissemination: A modeling study

Proposing substitutes for Pyriproxyfen (PPF) in the auto-dissemination strategy is essential to ensure the continuity of the strategy in the field, especially in the case of the emergence of populations resistant to this larvicide. One possible substitute among the compounds already in use in Brazil is the larvicide Diflubenzuron (DFB). The equation that defines the proportion of oviposition sites (habitats) contaminated by the auto-dissemination strategy was modified to account for the number of visits required to reach the necessary concentration of DFB for contamination, considering scenarios with varying numbers of oviposition sites and mosquito densities. The dissemination was evaluated in oviposition sites of 2 L, 1.5 L, 1 L, 0.5 L, 0.2 L, and 0.1 L. The minimum concentration of active ingredient (a.i) of DFB required for a commercial product to contaminate at least 50% of oviposition sites was also investigated, along with the impact of other vector control methods, such as the removal/destruction of oviposition sites and the use of insecticides to kill adult 'females, on the auto-dissemination approach. The use of pure DFB compounds enabled contamination efficiency of more than 50% in oviposition sites with a volume of less than 2 L in scenarios with fewer oviposition sites. On the other hand, with the use of the commonly used concentration of the product, similar efficacy was only achieved in oviposition sites of 0.1 L and 0.2 L in medium and high infestation scenarios. Strategies that reduce the number of available oviposition sites work synergistically with the auto-dissemination strategy, making it possible to use less concentrated products and contaminated sites of larger volume. The strategy proved to be resilient in situations of insecticide application according to the concentration of DFB used, abundance of females, and low number of oviposition sites. Increasing the number of dissemination traps on the field also contributes to better results, especially for oviposition sites of 0.5 L and 1 L. The results of the model obtained under the stipulated conditions provide further support for the potential use of DFB as a substitute for PPF in the auto-dissemination strategy.

Dengue is a product of the environment: an approach to the impacts of the environment on the Aedes aegypti mosquito and disease cases

Dengue is an arbovirus infection whose etiologic agent is transmitted by the Aedes aegypti mosquito. Since the early 1980s, when the circulation of the dengue virus (DENV) was confirmed in Brazil, the disease has become a growing multifactorial public health problem. This article presented the main factors that have contributed to the frequent dengue epidemics in recent years, such as the behavior of the vector, climate change, and social, political, and economic aspects. The intersection between these different factors in the dynamics of the disease is highlighted, including the increase in the mosquito population due to higher temperatures and rainy periods, as well as the influence of socioeconomic conditions on the incidence of dengue. Some mosquito control strategies are also addressed, including the use of innovative technologies such as drones and the Wolbachia bacterium, as well as the hope represented by the dengue vaccine. Nevertheless, the need for integrated and effective public policies to reduce social inequalities and the impacts of climate change on the spread of dengue is emphasized.

Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus

Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts in vector surveillance and control. In multiple mosquito control methods, the identification and elimination of potential breeding sites (PBS) for Aedes are recognized as effective methods for population control. Previous studies utilizing unmanned aerial vehicles (UAVs) and deep learning to identify PBS have primarily focused on large, regularly-shaped containers. However, there has been a small amount of empirical research into their practical application in the field. We have thus constructed a PBS dataset specifically tailored for Ae. albopictus, including items such as buckets, bowls, bins, aquatic plants, jars, lids, pots, boxes, and sinks that were common in the Yangtze River Basin in China. Then, a YOLO v7 model for identifying these PBS was developed. Finally, we recognized and labeled the area with the highest PBS density, as well as the subarea with the most urgent need for source reduction in the empirical region, by calculating the kernel density value. Based on the above research, we proposed a UAV-AI-based methodological framework to locate the spatial distribution of PBS, and conducted empirical research on Jinhulu New Village, a typical model community. The results revealed that the YOLO v7 model achieved an excellent result on the F1 score and mAP(both above 0.99), with 97% of PBS correctly located. The predicted distribution of different PBS categories in each subarea was completely consistent with true distribution; the five houses with the most PBS were correctly located. The results of the kernel density map indicate the subarea 4 with the highest density of PBS, where PBS needs to be removed or destroyed with immediate effect. These results demonstrate the reliability of the prediction results and the feasibility of the UAV-AI-based methodological framework. It can minimize repetitive labor, enhance efficiency, and provide guidance for the removal and destruction of PBS. The research can shed light on the investigation of mosquito PBS investigation both methodologically and practically.

Development of a trash classification system to map potential Aedes aegypti breeding grounds using unmanned aerial vehicle imaging

Aedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions.

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments

BACKGROUND

In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies.

RESULTS

We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance.

CONCLUSIONS

Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Introduction of Aedes aegypti mosquitoes carrying wAlbB Wolbachia sharply decreases dengue incidence in disease hotspots

Partial replacement of resident Aedes aegypti mosquitoes with introduced mosquitoes carrying certain strains of inherited Wolbachia symbionts can result in transmission blocking of dengue and other viruses of public health importance. Wolbachia strain wAlbB is an effective transmission blocker and stable at high temperatures, making it particularly suitable for hot tropical climates. Following trial field releases in Malaysia, releases using wAlbB Ae. aegypti have become operationalized by the Malaysian health authorities. We report here on an average reduction in dengue fever of 62.4% (confidence intervals 50-71%) in 20 releases sites when compared to 76 control sites in high-rise residential areas. Importantly the level of dengue reduction increased with Wolbachia frequency, with 75.8% reduction (61-87%) estimated at 100% Wolbachia frequency. These findings indicate large impacts of wAlbB Wolbachia invasions on dengue fever incidence in an operational setting, with incidence expected to further decrease as wider areas are invaded.

Challenge and opportunity for vector control strategies on key mosquito-borne diseases during the COVID-19 pandemic

Mosquito-borne diseases are major global health problems that threaten nearly half of the world's population. Conflicting resources and infrastructure required by the coronavirus disease 2019 (COVID-19) global pandemic have resulted in the vector control process being more demanding than ever. Although novel vector control paradigms may have been more applicable and efficacious in these challenging settings, there were virtually no reports of novel strategies being developed or implemented during COVID-19 pandemic. Evidence shows that the COVID-19 pandemic has dramatically impacted the implementation of conventional mosquito vector measures. Varying degrees of disruptions in malaria control and insecticide-treated nets (ITNs) and indoor residual spray (IRS) distributions worldwide from 2020 to 2021 were reported. Control measures such as mosquito net distribution and community education were significantly reduced in sub-Saharan countries. The COVID-19 pandemic has provided an opportunity for innovative vector control technologies currently being developed. Releasing sterile or lethal gene-carrying male mosquitoes and novel biopesticides may have advantages that are not matched by traditional vector measures in the current context. Here, we review the effects of COVID-19 pandemic on current vector control measures from 2020 to 2021 and discuss the future direction of vector control, taking into account probable evolving conditions of the COVID-19 pandemic.

Unmanned aerial vehicles for surveillance and control of vectors of malaria and other vector-borne diseases

The use of Unmanned Aerial Vehicles (UAVs) has expanded rapidly in ecological conservation and agriculture, with a growing literature describing their potential applications in global health efforts including vector control. Vector-borne diseases carry severe public health and economic impacts to over half of the global population yet conventional approaches to the surveillance and treatment of vector habitats is typically laborious and slow. The high mobility of UAVs allows them to reach remote areas that might otherwise be inaccessible to ground-based teams. Given the rapidly expanding examples of these tools in vector control programmes, there is a need to establish the current knowledge base of applications for UAVs in this context and assess the strengths and challenges compared to conventional methodologies. This review aims to summarize the currently available knowledge on the capabilities of UAVs in both malaria control and in vector control more broadly in cases where the technology could be readily adapted to malaria vectors. This review will cover the current use of UAVs in vector habitat surveillance and deployment of control payloads, in comparison with their existing conventional approaches. Finally, this review will highlight the logistical and regulatory challenges in scaling up the use of UAVs in malaria control programmes and highlight potential future developments.

Osorio A, Valle J, Angulo-Kladt R, Gómez-Simuta Y, Fernández-Salas I, Dor A, Williams T. Comparison of Ground Release and Drone-Mediated Aerial Release of Aedes aegypti Sterile Males in Southern Mexico: Efficacy and Challenges

Sterile males of Aedes aegypti were released once a week for 8 weeks to evaluate the dispersal efficiency of ground and aerial drone release methods in a rural village of 26 Ha in southern Mexico. Indoor and outdoor BG-Sentinel traps were placed in 13−16 houses distributed throughout the village. The BG traps were activated 48 h after the release of the sterile males and functioned for a 24 h period following each release. Over the 8-week period of simultaneous ground and aerial releases, an average of 85,117 ± 6457 sterile males/week were released at ground level and 86,724 ± 6474 sterile males/week were released using an aerial drone. The ground release method resulted in higher numbers of captured males (mean = 5.1 ± 1.4, range 1.1−15.7 sterile males/trap) compared with the aerial release method (mean = 2.6 ± 0.8, range 0.5−7.3 sterile males/trap) (p < 0.05). Similarly, the prevalence of traps that captured at least one sterile male was significantly higher for ground release compared to the aerial release method (p < 0.01). The lower numbers of sterile males captured in the aerial release method could be due to mortality or physical injury caused by the chilling process for immobilization, or the compaction of these insects during transport and release. However, aerial releases by a two-person team distributed insects over the entire village in just 20 min, compared to ~90 min of work for a five-person team during the ground release method. Ground release also resulted in higher aggregations of males and some villagers reported feeling discomfort from the presence of large numbers of mosquitoes in and around their houses. We conclude that modifications to the handling and transport of sterile males and the design of containers used to store males are required to avoid injury and to improve the efficiency of aerial releases for area-wide SIT-based population suppression programs targeted at mosquito vectors of human disease.