Suppressing dengue via a drone system

Amarasinghe, Akarshani; Suduwella, Chathura; Niroshan, Lasith; Elvitigala, Charith; Zoysa, Kasun De; Keppetiyagama, Chamath

doi:10.1109/icter.2017.8257797

“…This is commonly approached as detecting a predefined set of object classes often associated with mosquito breeding grounds in urban areas, such as tires, pools, and watertanks [46,84]. Still, it may also be framed as a general water retention detection based on the physical behavior of water in both natural and artificial environments [85].…”

Section: Opportunities For Improvementsmentioning

confidence: 99%

Automatic mapping of high-risk urban areas for Aedes aegypti infestation based on building facade image analysis

Laranjeira,

Pereira,

Oliveira

et al. 2024

PLoS Negl Trop Dis

0

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Background Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet. Methodology Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings’ conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas. Principal findings PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases.

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“…Recent studies have used UAVs to detect water bodies for larval source management and analysis of the best routes for teams to access them for malaria control in Zanzibar [27], Côte d'Ivoire [20], Malawi [34], Peru [39] and for detection of oviposition sites of arbovirus vectors in Mexico [38], Peru [40], Sri Lanka [41], and Australia [35]. Additionally, UAVs have been used in Borneo to investigate the movements of humans and primates to elucidate the transmission mechanisms of Plasmodium knowlesi malaria [19], as well as to map snail habitats to investigate the risk of schistosomiasis [42,43].…”

Section: Uav Use In Vector Habitat Surveillancementioning

confidence: 99%

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

Mechan

¹

,

Bartonicek

²

,

Malone

³

et al. 2023

Malar J

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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. Graphical Abstract

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“…This is commonly approached as detecting a predefined set of object classes often associated with mosquito breeding grounds in urban areas, such as tires, pools, and watertanks [46,86]. Still, it may also be framed as a general water retention detection based on the physical behavior of water in both natural and artificial environments [87].…”

Section: Opportunities For Improvementsmentioning

confidence: 99%

Automatic mapping of high-risk urban areas forAedes aegyptiinfestation based on building facade image analysis

Laranjeira,

Pereira,

Vitor Ferreira de Oliveira

et al. 2023

Preprint

0

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BackgroundDengue, Zika, and chikungunya, whose viruses are transmitted mainly byAedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet.MethodologyOur study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings’ conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas.Principal findingsPCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimizeAe. aegyptisurveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases.Author SummaryThe strategies to controlAe. aegyptirequire intensive work and considerable financial resources, are time-consuming, and are commonly affected by operational problems requiring urgent improvement. The PCI is a good tool for identifying higher-risk areas; however, its measure requires a high amount of human and material resources, and the aforementioned issues remain. In this paper, we propose a novel approach capable of predicting the PCI of buildings based on street-level images. This first work combines deep learning-based methods with street-level data to predict facade conditions.Considering the good results obtained with PCINet and the good correlations of facade conditions with PCI components, we could use this methodology to classify building conditions without visiting them physically. With this, we intend to overcome the high cost of identifying high-risk areas. Although we have a long road ahead, our results show that PCINet could help to optimizeAe. aegyptiand arbovirus surveillance and control, reducing the number of in-person visits necessary to identify buildings or areas at risk.

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Suppressing dengue via a drone system

Cited by 15 publications

References 12 publications

Automatic mapping of high-risk urban areas for Aedes aegypti infestation based on building facade image analysis

Automatic mapping of high-risk urban areas for Aedes aegypti infestation based on building facade image analysis

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

Automatic mapping of high-risk urban areas forAedes aegyptiinfestation based on building facade image analysis

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