In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 12 ( 2021-12-9), p. e0258681-
Abstract:
Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in digital images to identify and classify areas based on the socioeconomic index, in order to assist public health programs in the control of diseases linked to the Aedes aegypti mosquito. This study covers four regions of Campinas, São Paulo, characterized by different socioeconomic contexts. With mosaics of images obtained by a 12.1 MP Canon PowerShot S100 (5.2 mm focal length) carried by unmanned aerial vehicles, we developed deep learning algorithms in the scope of computer vision for the detection of water tanks and swimming pools. An object detection model, which was initially created for areas of Belo Horizonte, Minas Gerais, was enhanced using the transfer learning technique, and allowed us to detect objects in Campinas with fewer samples and more efficiency. With the detection of objects in digital images, the proportions of objects per square kilometer for each region studied were estimated by adopting a Chi-square distribution model. Thus, we found that regions with low socioeconomic status had more exposed water tanks, while regions with high socioeconomic levels had more exposed pools. Using deep learning approaches, we created a useful tool for Ae . aegypti control programs to utilize and direct disease prevention efforts. Therefore, we concluded that it is possible to detect objects directly related to the socioeconomic level of a given region from digital images, which encourages the practicality of this approach for studies aimed towards public health.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0258681
DOI:
10.1371/journal.pone.0258681.g001
DOI:
10.1371/journal.pone.0258681.g002
DOI:
10.1371/journal.pone.0258681.g003
DOI:
10.1371/journal.pone.0258681.g004
DOI:
10.1371/journal.pone.0258681.g005
DOI:
10.1371/journal.pone.0258681.g006
DOI:
10.1371/journal.pone.0258681.g007
DOI:
10.1371/journal.pone.0258681.g008
DOI:
10.1371/journal.pone.0258681.g009
DOI:
10.1371/journal.pone.0258681.t001
DOI:
10.1371/journal.pone.0258681.t002
DOI:
10.1371/journal.pone.0258681.t003
DOI:
10.1371/journal.pone.0258681.t004
DOI:
10.1371/journal.pone.0258681.s001
DOI:
10.1371/journal.pone.0258681.s002
DOI:
10.1371/journal.pone.0258681.s003
DOI:
10.1371/journal.pone.0258681.r001
DOI:
10.1371/journal.pone.0258681.r002
DOI:
10.1371/journal.pone.0258681.r003
DOI:
10.1371/journal.pone.0258681.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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