PMID- 32575963
OWN - NLM
STAT- MEDLINE
DCOM- 20210215
LR  - 20210215
IS  - 1970-7096 (Electronic)
IS  - 1827-1987 (Linking)
VI  - 15
IP  - 1
DP  - 2020 Jun 17
TI  - Choice of unmanned aerial vehicles for identification of mosquito breeding sites.
LID - 10.4081/gh.2020.810 [doi]
AB  - 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.
FAU - Aragao, Franciely Velozo
AU  - Aragao FV
AD  - Department of Industrial Engineering, Federal University of Technology (UTFPR), 
      Parana. fran-aragao@hotmail.com.
FAU - Cavicchioli Zola, Fernanda
AU  - Cavicchioli Zola F
AD  - Department of Industrial Engineering, Federal University of Technology (UTFPR), 
      Parana. fran-aragao@hotmail.com.
FAU - Nogueira Marinho, Luis Henrique
AU  - Nogueira Marinho LH
AD  - Department of Exact Sciences, Universidade Estadual de Londrina (UEL). 
      fran-aragao@hotmail.com.
FAU - De Genaro Chiroli, Daiane Maria
AU  - De Genaro Chiroli DM
AD  - Department of Industrial Engineering, Federal University of Technology (UTFPR), 
      Parana. fran-aragao@hotmail.com.
FAU - Braghini Junior, Aldo
AU  - Braghini Junior A
AD  - Department of Industrial Engineering, Federal University of Technology (UTFPR), 
      Parana. fran-aragao@hotmail.com.
FAU - Colmenero, Joao Carlos
AU  - Colmenero JC
AD  - Department of Industrial Engineering, Federal University of Technology (UTFPR), 
      Parana. fran-aragao@hotmail.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20200617
PL  - Italy
TA  - Geospat Health
JT  - Geospatial health
JID - 101302943
SB  - IM
MH  - Aedes/growth & development
MH  - Animals
MH  - Breeding
MH  - Cities
MH  - Dengue/transmission
MH  - Ecosystem
MH  - *Environmental Monitoring/methods
MH  - *Mosquito Control/methods
MH  - *Mosquito Vectors/growth & development
EDAT- 2020/06/25 06:00
MHDA- 2021/02/16 06:00
CRDT- 2020/06/25 06:00
PHST- 2019/08/27 00:00 [received]
PHST- 2020/04/12 00:00 [accepted]
PHST- 2020/06/25 06:00 [entrez]
PHST- 2020/06/25 06:00 [pubmed]
PHST- 2021/02/16 06:00 [medline]
AID - 10.4081/gh.2020.810 [doi]
PST - epublish
SO  - Geospat Health. 2020 Jun 17;15(1). doi: 10.4081/gh.2020.810.