https://doi.org/10.4081/gh.2020.810
Choice of unmanned aerial vehicles for identification of mosquito breeding sites
Submitted: 27 August 2019
Accepted: 12 April 2020
Published: 17 June 2020
Accepted: 12 April 2020
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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The disordered urban growth that may favour the emergence of the Aedes aegypti mosquito in cities is a problem of increasing magnitude in middle- and high-income countries in the tropical part of the world. Currently, the World Health Organization (WHO) considers the control and elimination of Ae. aegypti a world-wide high priority as it is the main vector of many rapidly spreading viral diseases, dengue in particular. A major difficulty in controlling the proliferation of this vector is associated with identification of the breeding sites. The use of Unmanned Aerial Vehicles (UAVs) can be an efficient alternative to manual search because of high mobility and the ability to overcome physical obstacles, particularly in urban areas where it can offer close-up images of potential breeding sites that are difficult to reach. The objective of this study was to find a way to select the most suitable UAV for the identification of Ae. aegypti habitats by providing images of potential mosquito breeding sites. This can be accomplished by a Multiple-Criteria Decision Method (MCDM) based on an Analytical Hierarchy Process (AHP) for the evaluation of weights of the criteria used for characterizing UAVs. The alternatives were analyzed and ranked using the Fuzzy Set Theory (FST) merged with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The methodology is explained and discussed with respect to identification and selection of the most appropriate UAV for aerial mapping of Aedes breeding sites.
Bhatt, S., Gething, P. W., Brady, O. J., Messina, J. P., Farlow, A. W., Moyes, C. L., ... & Myers, M. F. The global distribution and burden of dengue. Nature. 2013, 496(7446), 504. https://doi.org/10.1038/nature12060
DOI: https://doi.org/10.1038/nature12060
Liu-Helmersson, J., Rocklöv, J., Sewe, M., & Brännström, Å.. Climate change may enable Aedes aegypti infestation in major European cities by 2100. Environmental research. 2019. 172, 693-699. https://doi.org/10.1016/j.envres.2019.02.026
DOI: https://doi.org/10.1016/j.envres.2019.02.026
World Health Organization (WHO). WHAT is dengue and how is it treated?. http://www.who.int/features/qa/54/en/. Accessed May 2018.
Ferreira, A. C., Chiaravalloti, F., & Mondini, A.. Dengue em Araraquara, SP: epidemiologia, clima e infestação por Aedes aegypti. Rev. Saúde Pública. 2018, 52. https://doi.org/10.11606/S1518-8787.2018052000414
DOI: https://doi.org/10.11606/S1518-8787.2018052000414
Espinosa, M. O., Polop, F., Rotela, C. H., Abril, M., & Scavuzzo, C. M. (2016). Spatial pattern evolution of Aedes aegypti breeding sites in an Argentinean city without a dengue vector control programme. Geospatial health. DOI: https://doi.org/10.4081/gh.2016.471
DOI: https://doi.org/10.4081/gh.2016.471
Khormi, H. M., & Kumar, L. (2012). Assessing the risk for dengue fever based on socioeconomic and environmental variables in a geographical information system environment. Geospatial health, 6(2), 171-176. DOI: https://doi.org/10.4081/gh.2012.135
DOI: https://doi.org/10.4081/gh.2012.135
Brown, J. E., Evans, B. R., Zheng, W., Obas, V., Barreraâ€Martinez, L., Egizi, A., ... & Powell, J. R.. Human impacts have shaped historical and recent evolution in Aedes aegypti, the dengue and yellow fever mosquito. Evolution. 2014. 68(2), 514-525. https://doi.org/10.1111/evo.12281
DOI: https://doi.org/10.1111/evo.12281
Gloriaâ€Soria, A., Ayala, D., Bheecarry, A., Calderonâ€Arguedas, O., Chadee, D. D., Chiappero, M., ... & Kamgang, B.. Global genetic diversity of Aedes aegypti. Molecular ecology. 2016, 25(21), 5377-5395. DOI: https://doi.org/10.1111/mec.13866
Khormi, H. M., & Kumar, L.. Climate change and the potential global distribution of Aedes aegypti: spatial modelling using geographical information system and CLIMEX. Geospatial health. 2014, 405-415. https://doi.org/10.1111/mec.13866
DOI: https://doi.org/10.1111/mec.13866
Donalisio, M. R., Freitas, A. R. R., & Zuben, A. P. B. V.. Arboviruses emerging in Brazil: challenges for clinic and implications for public health. Revista de Saúde Pública. 2017, 51, 30. http://dx.doi.org/10.1590/s1518-8787.2017051006889
DOI: https://doi.org/10.1590/s1518-8787.2017051006889
Sarfraz, M. S., Tripathi, N. K., Faruque, F. S., Bajwa, U. I., Kitamoto, A., & Souris, M. (2014). Mapping urban and peri-urban breeding habitats of Aedes mosquitoes using a fuzzy analytical hierarchical process based on climatic and physical parameters. Geospatial health, S685-S697. https://doi.org/10.4081/gh.2014.297
DOI: https://doi.org/10.4081/gh.2014.297
Liu, B., Gao, X., Ma, J., Jiao, Z., Xiao, J., Hayat, M. A., & Wang, H.. Modeling the present and future distribution of arbovirus vectors Aedes aegypti and Aedes albopictus under climate change scenarios in Mainland China. Science of The Total Environment. 2019, 664, 203-214. https://doi.org/10.1016/j.scitotenv.2019.01.301
DOI: https://doi.org/10.1016/j.scitotenv.2019.01.301
Madariaga, M., Ticona, E., & Resurrecion, C.. Chikungunya: bending over the Americas and the rest of the world. Brazilian Journal of Infectious Diseases. 2016 20(1), 91-98. http://dx.doi.org/10.1016/j.bjid.2015.10.004
DOI: https://doi.org/10.1016/j.bjid.2015.10.004
Carvalho, S., Magalhães, M.D.A.F.M., & Medronho, R.D.A. Análise da distribuição espacial de casos da dengue no municÃpio do Rio de Janeiro, 2011 e 2012. Rev. Saúde Pública. 2017, 51. https://doi.org/10.11606/s1518-8787.2017051006239
DOI: https://doi.org/10.11606/s1518-8787.2017051006239
Maitra, A., Cunha-Machado, A. S., de Souza Leandro, A., da Costa, F. M., & Scarpassa, V. M.. Exploring deeper genetic structures: Aedes aegypti in Brazil. Acta Tropica. 2019, 195, 68-77. https://doi.org/10.1016/j.actatropica.2019.04.027
DOI: https://doi.org/10.1016/j.actatropica.2019.04.027
Machault, V., Vignolles, C., Borchi, F., Vounatsou, P., Pages, F., Briolant, S., ... & Rogier, C.. The use of remotely sensed environmental data in the study of malaria. Geospatial Health. 2011, 151-168. https://doi.org/10.4081/gh.2011.167
DOI: https://doi.org/10.4081/gh.2011.167
Hay, S. I., George, D. B., Moyes, C. L., & Brownstein, J. S.. Big data opportunities for global infectious disease surveillance. PLoS Medicine. 2013, 10(4), e1001413. https://doi.org/10.1371/journal.pmed.1001413
DOI: https://doi.org/10.1371/journal.pmed.1001413
Turner, D., Lucieer, A., & de Jong, S.. Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sensing. 2015, 7(2), 1736-1757. https://doi.org/10.3390/rs70201736
DOI: https://doi.org/10.3390/rs70201736
Amarasinghe, A., Suduwella, C., Niroshan, L., Elvitigala, C., De Zoysa, K., & Keppetiyagama, C.. Suppressing dengue via a drone system. In 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer). 2017, (pp. 1-7). IEEE.
DOI: https://doi.org/10.1109/ICTER.2017.8257797
Amenyo, J. T., Phelps, D., Oladipo, O., Sewovoe-Ekuoe, F., Jadoonanan, S., Jadoonanan, S., ... & Hossain, A.. MedizDroids Project: Ultra-low cost, low-altitude, affordable and sustainable UAV multicopter drones for mosquito vector control in malaria disease management. In IEEE Global Humanitarian Technology Conference (GHTC 2014). 2014, (pp. 590-596). IEEE. https://doi.org/10.1109/GHTC.2014.6970343
DOI: https://doi.org/10.1109/GHTC.2014.6970343
International Atomic Energy Agency (IAEA). IAEA Conducts Successful Test of Unmanned Aerial Vehicles in Fight Against Disease-Transmitting Mosquitos. https://www.iaea.org/newscenter/pressreleases/iaea-conducts-successful-test-of-Unmanned Aerial Vehicles-in-fight-against-disease-transmitting-mosquitos. Accessed May 2018.
Saaty, T. L. The Analytic Network Process. 1 ed. McGraw Hill: New York, 1980.
DOI: https://doi.org/10.1007/0-387-33987-6_1
Zadeh, L.A. , (1965). Fuzzy sets. Information and control. Inf. Control, 8 (3) (1965), pp. 338-353
DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
Sousa, C. A. D., Duarte, P. S., & Pereira, J. C. R.. Lógica fuzzy e regressão logÃstica na decisão para prática de cintilografia das paratiróides. Revista de Saúde Pública. 2006, 40, 898-906.
Chaves, L. E., Nascimento, L. F. C., & Rizol, P. M. S. R.. Modelo fuzzy para estimar o número de internações por asma e pneumonia sob os efeitos da poluição do ar. Revista de Saúde Pública. 2017, 51, 1-8. https://doi.org/10.1590/S1518-8787.2017051006501 1
DOI: https://doi.org/10.1590/s1518-8787.2017051006501
Chen, C. T.. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets And Systems. 2000, 114(1), 1-9. https://doi.org/10.1016/S0165-0114(97)00377-1
DOI: https://doi.org/10.1016/S0165-0114(97)00377-1
Wolters, W. T. M., & Mareschal, B.. Novel types of sensitivity analysis for additive MCDM methods. European Journal of Operational Research. 1955, 81(2), 281-290. https://doi.org/10.1016/0377-2217(93)E0343-V
DOI: https://doi.org/10.1016/0377-2217(93)E0343-V
IBGE, 2010. IBGE – Instituto Brasileiro de Geografia EstatÃstica (2010). Panorama. https://cidades.ibge.gov.br/brasil/pr/maringa/panorama. Accessed March 2019.
Secretary of Health-Paraná, 2019. Boletim da dengue (2019). http://www.dengue.pr.gov.br/modules/conteudo/conteudo.php?conteudo=28. Accessed Mach 2019.
SACDENGUE – Sistema de Alerta Climático de Dengue. Climatic risk to development of breeding ground by Meteorological Stations (2019). http://www.terra.ufpr.br/portal/laboclima/sacdengue/. Accessed Mach 2019.
Supporting Agencies
Coordenação de Aperfeiçoamento de Pessoal de NÃvel Superior - Brasil (CAPES).How to Cite
Aragão, F. V., Cavicchioli Zola, F., Nogueira Marinho, L. H., de Genaro Chiroli, D. M., Braghini Junior, A., & Colmenero, J. C. (2020). Choice of unmanned aerial vehicles for identification of mosquito breeding sites. Geospatial Health, 15(1). https://doi.org/10.4081/gh.2020.810
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