Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Rogelio Danis-Lozano
 -- 1728 2022-12-22 21:11:33 |
2 layout
Camila Xu
-2 word(s) 1726 2022-12-23 05:12:47 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Muñiz-Sánchez, V.;  Valdez-Delgado, K.M.;  Hernandez-Lopez, F.J.;  Moo-Llanes, D.A.;  González-Farías, G.;  Danis-Lozano, R. Unmanned Aerial Vehicles for Mosquito-Borne Viral Diseases. Encyclopedia. Available online: https://encyclopedia.pub/entry/39119 (accessed on 04 May 2024).
Muñiz-Sánchez V,  Valdez-Delgado KM,  Hernandez-Lopez FJ,  Moo-Llanes DA,  González-Farías G,  Danis-Lozano R. Unmanned Aerial Vehicles for Mosquito-Borne Viral Diseases. Encyclopedia. Available at: https://encyclopedia.pub/entry/39119. Accessed May 04, 2024.
Muñiz-Sánchez, Víctor, Kenia Mayela Valdez-Delgado, Francisco J. Hernandez-Lopez, David A. Moo-Llanes, Graciela González-Farías, Rogelio Danis-Lozano. "Unmanned Aerial Vehicles for Mosquito-Borne Viral Diseases" Encyclopedia, https://encyclopedia.pub/entry/39119 (accessed May 04, 2024).
Muñiz-Sánchez, V.,  Valdez-Delgado, K.M.,  Hernandez-Lopez, F.J.,  Moo-Llanes, D.A.,  González-Farías, G., & Danis-Lozano, R. (2022, December 22). Unmanned Aerial Vehicles for Mosquito-Borne Viral Diseases. In Encyclopedia. https://encyclopedia.pub/entry/39119
Muñiz-Sánchez, Víctor, et al. "Unmanned Aerial Vehicles for Mosquito-Borne Viral Diseases." Encyclopedia. Web. 22 December, 2022.
Unmanned Aerial Vehicles for Mosquito-Borne Viral Diseases
Edit
This entry is adapted from the peer-reviewed paper 10.3390/machines10121161

Unmanned aerial vehicle (UAV) technology has developed in recent years; its applications avoid many of the limitations associated with satellite data, such as long repetition times, cloud contamination, low spatial resolution, and lack of homogeneity in camera angle or shooting time.

Aedes aegypti UAV risk model FAMD PLS

1. Introduction

Unmanned aerial vehicle (UAV) technology has developed in recent years; its applications avoid many of the limitations associated with satellite data, such as long repetition times, cloud contamination, low spatial resolution, and lack of homogeneity in camera angle or shooting time [1]. It also offers the possibility of collecting detailed spatial information in real time at a relatively low cost [2][3]. They carry out highly detailed imaging work with essential high spatial resolution, spectral, and temporal characteristics [4][5][6][7]. Likewise, unmanned aerial vehicles (UAVs) are developing technology for remote-sensing applications based on passive sensors, such as RGB, multispectral, hyperspectral, thermal cameras, and active sensors, such as LiDAR or RADAR [8][9].
The UAVs can provide precise spatial and temporal data to understand the links between disease transmission and environmental factors [2][3][10]. Moreover, it is a flexible and low-cost solution for mapping mosquito breeding sites and the operational dissemination of strategies in mosquito-elimination campaigns. It can be used in communication materials to demonstrate the specific risk conditions for a given area [5][11]. Through aerial surveillance, possible mosquito breeding sites could be identified in domestic areas inaccessible to traditional ground surveillance, which allows them to be proposed as a useful and complementary tool in surveillance programs and mosquito control [12]. Likewise, it can help operators of vector-control actions to optimize treatments by accurately identifying and mapping the coverage of breeding sites by using standard and multispectral images [13][14]. Orthomosaics provide vital information for other public health planning activities [11].
The physical landscape characteristics influence the distribution of adult female mosquitoes [15] as interrelated socioeconomic, environmental, and behavioral factors can explain the presence and abundance of Aedes aegypti [16]. Anthropogenic environmental changes can create an overabundance of resources responsible for sustaining vector mosquito species’ invasion, spread, and colonization of urban areas [17]. Vegetation cover is an important environmental factor that allows optimal conditions for mosquito life-cycle development [18][19]. The organization of the backyard, knowledge about vector biology, and cleaning containers are identified as the main topics for future prevention strategies for the transmission of dengue in the local and national context [20].
New technologies for mosquito surveillance are growing; drones and specialized cartography, machine learning, deep learning, big data, and citizen science can be added as tools for better understanding the mosquito population dynamics and determining the areas at risk to better conditions for spreading mosquito populations [21]. Predictive models can be used for decision making in preventing various arboviruses transmitted by Ae. aegypti in endemic urban and semiurban areas. Building a model that determines the power and association of different variables is a necessary challenge because the Ae. aegypti mosquito exists in complex environments with complex dynamics, where numerous factors intervene in different dimensions, and each one can have a different role of lesser or greater importance. However, it does not stop influencing the response of the vector to the differences in the landscape.
In this research, by UAV cartography, field surveillance, and algorithm development, researchers design a risk index to determine which houses have the environmental, demographic, and socioeconomic characteristics to be classified as “aedic” houses for immature and adult Ae. aegypti mosquitoes in Tapachula, Chiapas, a city on the Mexican southern border. This proposed methodology can adapt the model to the different regions of our country, and this flexibility is allowed mainly by the availability of real-time information provided by UAV, one of its most important contributions.
Nonetheless, for the best performance of the UAV, it is necessary to consider the meteorological conditions and know the flight area [22][23] and the terrain’s characteristics, such as tall trees and telecommunication antennae, to avoid electromagnetic interference [22]. As a user of UAVs, the national regulations must be observed [24]. Moreover, it should be considered that one of the factors for UAV flight coverage depends on the battery time [3][23]. Indeed, before the flights, the field teams should visit and talk with the health authorities, principal neighborhood actors, and community leaders, who are necessary for developing these new tools [25]. The costs of this technology must be taken into account and considered as open-source software for affordable technology. A multidisciplinary team is also required to address the entire strategy.

2. Unmanned Aerial Vehicles for Mosquito-Borne Viral Diseases

Studies on UAV applications in health are scarce. They addressed simulations on using drones for out-of-hospital care in cardiac arrest emergencies, identification of people after accidents, transport of blood samples, and improvement of surgical procedures in war zones [26]. UAVs can transport medical supplies, specimens, biological materials, and vaccines to hard-to-reach areas at a reduced travel time. Moreover, the research on the use of drones in health is almost limited to simulation scenarios [26][27].
In mosquito-surveillance and control programs, UAVs have been designed to control mosquitos by spraying larvicides, dropping larvicide tablets, spreading larvicide granules, the application of adulticides in ultra-low volume, sampling in bodies of water, and surveillance of adult mosquitoes [28][29]. Likewise, drones can help operators of vector-control actions to optimize treatments by accurately identifying and mapping the coverage of breeding sites by using standard and multispectral images [14] and as tools for the release of sterile mosquitoes [30][31].
Indeed, three years ago, different studies were conducted to evaluate the use of UAVs to identify the larval breeding sites of different mosquito species. In Peru, multispectral mapping was used with random forest analysis to identify Anopheles darling larval breeding sites [32]. In Brazil, it was used to detect Ae. aegypti breeding sites [33]. In Mexico, the first study evaluating UAVs was carried out to identify Ae. aegypti breeding sites [12].
In studies of spatiotemporal distribution models of Ae. aegypti vectors, the limited availability of high-resolution data for physical variables stands out as part of the inconsistencies in the number and influencing factors [34]. Few studies comprehensively highlight the interaction between environmental, socioeconomic, meteorological, and topographical systems [34]. Maximum entropy species-distribution models have been used, with human population density, distance to vegetation, water channels [35], population density, and poverty [36] being the main predictor variables of the vector suitability of a given area.
There are few works reported in the literature about the use of machine learning (ML) and nonparametric techniques for mosquito surveillance. In [37], authors used three ML models (nonlinear discriminant analysis, random forest, and generalized linear models) to analyze the environmental suitability of three indigenous mosquito species in the Netherlands: Culiseta annulata, Anopheles claviger, and Ochlerotatus punctor, where the response variable is mosquito abundance sampled at 778 locations by using CO –baited mosquito magnet traps. As covariates, they used environmental variables obtained from satellite images and meteorological data. The best results were achieved with random forests, and they showed the most discriminative variables for each species. In [38], the authors used classification methods based on –nearest neighbor, support vector machines, neural networks, and random forest to predict mosquito abundance in 90 sampling sites from Charlotte, NC, USA. The data consisted of mosquito samples collected with gravid traps and socioeconomic and landscape factors. The dependent variable to predict mosquito abundance was binarized as low (0) and high (1) according to the median value, and the independent variables or predictors were divided into seven socioeconomic variables and seven landscape variables obtained from remote-sensing images. Two versions of the input variables were used, one dichotomized into binary values according to the median of each variable, and one with the raw continuous values scaled. The best performance was achieved with the –nearest neighbor algorithm in both cases; however, there are no further details about the distance metric used in the binary input variables case, because, except for random forest, all classification methods use Euclidean distance as the default option, even when a nonlinear kernel or activation function is used; i.e., they are originally formulated for continuous input variables. In [39], 1066 females adult Ae. aegypti were collected by using battery-powered Prokopack aspirators from 128 indoor and outdoor houses in northeastern Thailand to map and predict the female adult Ae. aegypti abundance. They used five popular supervised learning models (logistic regression, support vector machine, –nearest neighbor, artificial neural networks, and random forest) based on socioeconomic, climate change, dengue knowledge, attitude, and practices (KAP), and/or landscape factors to predict the abundance considering only two classes (high and low). In this research, the random forest method had the highest prediction model accuracy, and the research demonstrated that climate change, KAP, and landscape factors were more important than socioeconomic factors in explaining mosquito abundance. Similar to [38], it is not clear how the mixed input data (categorical and continuous) are managed, because the classification models they used (except random forest) cannot naturally handle mixed-type data. This may be the reason why random forest showed the best results.
Those approaches are interesting in terms of prediction, and the use of ML models provides flexibility because most of them are nonparametric and do not assume a priori any probability distribution of data. However, for researchers it is very important to gain some insight into the local characteristics of the area of study, which makes it more prone to mosquito-borne diseases. A study of the importance of the variables can be done based on the parameters of the models [37][39], when it is possible, correlation analysis, or by a specific hypothesis tested on the models [38], based, for example, on manually adding or excluding individual variables or sets of variables [39]. All those approaches must be carried out carefully when researchers' variables are mixed-type data, particularly for ML models based on Euclidean distance between observations. For this reason, in the approach researchers pay special attention to obtaining an adequate distance metric for mixed-type data, which allows to obtain an index representing the local characteristics of houses that are related in some way to dengue disease.
Despite the scientific evidence available, the objectives of each previous study—to analyze the available entomological, epidemiological, and environmental data in defined study areas and to consider different types of variables—have been limited in terms of technology, resolution, and frequency of images available. Having high-resolution, real-time, and more accessible images taken by UAVs dictates the need to evaluate their use with predictive power within the prevention and control of vector-borne diseases. Prior knowledge is a good foundation, but current technology allows to analyze the dynamics of different changes at different spatial scales, such as overpopulation, urbanization, and climate change.

References

  1. Nex, F.; Remondino, F. UAV for 3D mapping applications: A review. Appl. Geomat. 2014, 6, 1–15.
  2. Eskandari, R.; Mahdianpari, M.; Mohammadimanesh, F.; Salehi, B.; Brisco, B.; Homayouni, S. Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models. Remote Sens. 2020, 12, 3511.
  3. Rojas Viloria, D.; Solano Charris, E.L.; Muñoz Villamizar, A.; Montoya Torres, J.R. Unmanned aerial vehicles/drones in vehicle routing problems: A literature review. Int. Trans. Oper. Res. 2021, 28, 1626–1657.
  4. Bendig, J.; Bolten, A.; Bareth, G. Introducing a Low-Cost Mini-Uav for-and Multispectral-Imaging. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, 39B1, 345–349.
  5. Landau, K.; Van Leeuwen, W. Fine Scale Spatial Urban Land Cover Factors Associated with Adult Mosquito Abundance and Risk in Tucson, Arizona. J. Vector Ecol. J. Soc. Vector Ecol. 2012, 37, 407–418.
  6. Ivosevic, B.; Han, Y.G.; Kwon, O. Calculating coniferous tree coverage using unmanned aerial vehicle photogrammetry. J. Ecol. Environ. 2017, 41, 1–8.
  7. Robb, C.; Hardy, A.; Doonan, J.H.; Brook, J. Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture. Front. Plant Sci. 2020, 11, 591886.
  8. González-Jorge, H.; Martínez-Sánchez, J.; Bueno, M.; Arias, P. Unmanned Aerial Systems for Civil Applications: A Review. Drones 2017, 1, 2.
  9. Hassanalian, M.; Abdelkefi, A. Classifications, applications, and design challenges of drones: A review. Prog. Aerosp. Sci. 2017, 91, 99–131.
  10. Fornace, K.M.; Drakeley, C.J.; William, T.; Espino, F.; Cox, J. Mapping infectious disease landscapes: Unmanned aerial vehicles and epidemiology. Trends Parasitol. 2014, 30, 514–519.
  11. Hardy, A.; Makame, M.; Cross, D.; Majambere, S.; Msellem, M. Using low-cost drones to map malaria vector habitats. Parasites Vectors 2017, 10, 1.
  12. Valdez-Delgado, K.M.; Moo-Llanes, D.A.; Danis-Lozano, R.; Cisneros-Vázquez, L.A.; Flores-Suarez, A.E.; Ponce-García, G.; Medina-De la Garza, C.E.; Díaz-González, E.E.; Fernández-Salas, I. Field effectiveness of drones to identify potential Aedes aegypti breeding sites in household environments from Tapachula, a dengue-endemic city in southern Mexico. Insects 2021, 12, 663.
  13. Haas-Stapleton, E.; Barretto, M.; Castillo, E.; Clausnitzer, R.; Ferdan, R. Assessing Mosquito Breeding Sites and Abundance Using An Unmanned Aircraft. J. Am. Mosq. Control. Assoc. 2019, 35, 228–232.
  14. Johnson, B.J.; Manby, R.; Devine, G.J. Performance of aerial Bacillus thuringiensis var. israelensis applications in mixed saltmarsh-mangrove systems and use of affordable unmanned aerial systems to identify problematic levels of canopy cover. bioRxiv 2020.
  15. Lorenz, C.; Castro, M.C.; Trindade, P.M.P.; Nogueira, M.L.; de Oliveira Lage, M.; Quintanilha, J.A.; Parra, M.C.; Dibo, M.R.; Fávaro, E.A.; Guirado, M.M.; et al. Predicting Aedes aegypti infestation using landscape and thermal features. Sci. Rep. 2020, 10, 21688.
  16. Markwardt, R.; Sorosjinda-Nunthawarasilp, P. Innovations in the Entomological Surveillance of Vector-Borne Diseases; Cambridge Scholars Publisher: Cambridge, UK, 2021.
  17. Wilke, A.B.B.; Vasquez, C.; Carvajal, A.; Moreno, M.; Fuller, D.O.; Cardenas, G.; Petrie, W.D.; Beier, J.C. Urbanization favors the proliferation of Aedes aegypti and Culex quinquefasciatus in urban areas of Miami-Dade County, Florida. Sci. Rep. 2021, 11, 22989.
  18. de Jesús Crespo, R.; Rogers, R.E. Habitat Segregation Patterns of Container Breeding Mosquitos: The Role of Urban Heat Islands, Vegetation Cover, and Income Disparity in Cemeteries of New Orleans. Int. J. Environ. Res. Public Health 2022, 19, 245.
  19. Tsuda, Y.; Suwonkerd, W.; Chawprom, S.; Prajakwong, S.; Takagi, M. Different spatial distribution of Aedes aegypti and Aedes albopictus along an urban-rural gradient and the relating environmental factors examined in three villages in northern Thailand. J. Am. Mosq. Control. Assoc. 2006, 22, 222–228.
  20. Vásquez-Trujillo, A.; Cardona, D.; Segura Cardona, A.; Camara, D.; Alves-Honório, N.; Parra-Henao, G. House-Level Risk Factors for Aedes aegypti Infestation in the Urban Center of Castilla la Nueva, Meta State, Colombia. J. Trop. Med. 2021, 2021, 12.
  21. Liu, M.; Wang, X.; Zhou, A.; Fu, X.; Ma, Y.; Piao, C. UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective. Sensors 2020, 20, 2238.
  22. DJI. Matrice 600®. Available online: https://www.dji.com/mx/downloads/products/matrice600 (accessed on 19 October 2019).
  23. Thibbotuwawa, A.; Bocewicz, G.; Nielsen, P.; Banaszak, Z. Unmanned aerial vehicle routing problems: A literature review. Appl. Sci. 2020, 10, 4504.
  24. Diario Oficial de la Federación. NORMA Oficial Mexicana NOM-032-SSA2-2014, Para la Vigilancia Epidemiológica, Promoción, Prevención y Control de las Enfermedades Transmitidas por Vectores. 2015. Available online: https://www.dof.gob.mx/nota_detalle.php?codigo=5389045&fecha=16/04/2015 (accessed on 18 April 2019).
  25. Hardy, A.; Proctor, M.; MacCallum, C.; Shawe, J.; Abdalla, S.; Ali, R.; Abdalla, S.; Oakes, G.; Rosu, L.; Worrall, E. Conditional trust: Community perceptions of drone use in malaria control in Zanzibar. Technol. Soc. 2022, 68, 101895.
  26. Carrillo-Larco, R.; Moscoso-Porras, M.; Taype-Rondan, A.; Ruiz-Alejos, A.; Bernabe-Ortiz, A. The use of unmanned aerial vehicles for health purposes: A systematic review of experimental studies. Glob. Health Epidemiol. Genom. 2018, 3, e13.
  27. Laksham, K.B. Unmanned aerial vehicle (drones) in public health: A SWOT analysis. J. Fam. Med. Prim. Care 2019, 8, 342–346.
  28. Williams, G.M.; Wang, Y.; Suman, D.S.; Unlu, I.; Gaugler, R. The development of autonomous unmanned aircraft systems for mosquito control. PLoS ONE 2020, 15, e0235548.
  29. Faraji, A.; Haas-Stapleton, E.; Sorensen, B.; Scholl, M.; Goodman, G.; Buettner, J.; Schon, S.; Lefkow, N.; Lewis, C.; Fritz, B.; et al. Toys or Tools? Utilization of Unmanned Aerial Systems in Mosquito and Vector Control Programs. J. Econ. Entomol. 2021, 114, 1896–1909.
  30. Bouyer, J.; Culbert, N.J.; Dicko, A.H.; Pacheco, M.G.; Virginio, J.; Pedrosa, M.C.; Garziera, L.; Pinto, A.T.M.; Klaptocz, A.; Germann, J.; et al. Field performance of sterile male mosquitoes released from an uncrewed aerial vehicle. Sci. Robot. 2020, 5, eaba6251.
  31. Marina, C.; Liedo, P.; Bond, G.; Osorio, A.; Valle Mora, J.; Angulo, R.; Gomez-Simuta, Y.; Fernandez 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. Insects 2022, 13, 347.
  32. Carrasco-Escobar, G.; Manrique, E.; Ruiz-Cabrejos, J.; Saavedra, M.; Alava, F.; Bickersmith, S.; Prussing, C.; Vinetz, J.M.; Conn, J.E.; Moreno, M.; et al. High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery. PLoS Neglected Trop. Dis. 2019, 13, 1–24.
  33. Aragão, F.V.; Cavicchioli Zola, F.; Nogueira Marinho, L.H.; de Genaro Chiroli, D.M.; Braghini Junior, A.; Colmenero, J.C. Choice of unmanned aerial vehicles for identification of mosquito breeding sites. Geospatial Health 2020, 15, 92–100.
  34. Sallam, M.F.; Fizer, C.; Pilant, A.N.; Whung, P.Y. Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping. Int. J. Environ. Res. Public Health 2017, 14, 1230.
  35. Estallo, E.; Sangermano, F.; Grech, M.; Ludueña-Almeida, F.; Frías-Cespedes, M.; Ainete, M.; Almirón, W.; Livdahl, T. Modelling the distribution of the vector Aedes aegypti in a central Argentine city: Modelling Aedes aegypti distribution. Med. Vet. Entomol. 2018, 32, 451–461.
  36. Obenauer, J.; Joyner, T.A.; Harris, J.B. The importance of human population characteristics in modeling Aedes aegypti distributions and assessing risk of mosquito-borne infectious diseases. Trop. Med. Health 2017, 45, 1–9.
  37. Cianci, D.; Hartemink, N.; Ibáñez-Justicia, A. Modelling the potential spatial distribution of mosquito species using three different techniques. Int. J. Health Geogr. 2015, 14, 10.
  38. Chen, S.; Whiteman, A.; Li, A.; Rapp, T.; Delmelle, E.; Chen, G.; Brown, C.L.; Robinson, P.; Coffman, M.J.; Janies, D.; et al. An operational machine learning approach to predict mosquito abundance based on socioeconomic and landscape patterns. Landsc. Ecol. 2019, 34, 1295–1311.
  39. Rahman, M.S.; Pientong, C.; Zafar, S.; Ekalaksananan, T.; Paul, R.E.; Haque, U.; Rocklöv, J.; Overgaard, H.J. Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach. One Health 2021, 13, 100358.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Computer Science, Interdisciplinary Applications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Víctor Muñiz-Sánchez
,
Kenia Mayela Valdez-Delgado
,
Francisco J. Hernandez-Lopez
,
David A. Moo-Llanes
,
Graciela González-Farías
,
Rogelio Danis-Lozano
View Times: 238
Revisions: 2 times (View History)
Update Date: 26 Dec 2022
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback